data democratization case study

Data democratization, much like the term digital transformation five years ago, has become a popular buzzword throughout organizations, from IT departments to the C-suite. It’s often described as a way to simply increase data access, but the transition is about far more than that. When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard.

Beyond the technical aspects, the goals are far loftier. When done well, data democratization empowers employees with tools that let everyone work with data, not just the data scientists. It can spark employees’ curiosity and spur innovation. When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” through a truly data literate organization.

In this article, we’ll explore the benefits of data democratization and how companies can overcome the challenges of transitioning to this new approach to data.

What is data democratization?

Data democratization helps companies make data-driven decisions by creating systems and adopting tools that allow anyone in the organization, regardless of their technical background, to access, use and talk about the data they need with ease. Instead of seeing data given with consent as the output of workers clients and prospects, it’s now the company’s gateway to strategic decision-making.

For true data democratization, both employees and consumers need to have data in an easy-to-use format to maximize its value. It also requires data literacy throughout the organization. Employees and leaders need to trust the data is accurate, know how to access it, as well as how it could be applied to business problems. In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used.

Data democratization is often conflated with data transparency, which refers to processes that help ensure data accuracy and easy access to data regardless of its location or the application that created it. Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Architecture for data democratization

Data democratization requires a move away from traditional “data at rest” architecture, which is meant for storing static data. Traditionally, data was seen as information to be put on reserve, only called upon during customer interactions or executing a program. Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations.

Data democratization uses a fit-for-purpose data architecture that is designed for the way today’s businesses operate, in real-time. It’s distributed both in the cloud and on-premises, allowing extensive use and movement across clouds, apps and networks, as well as stores of data at rest. An architecture designed for data democratization aims to be flexible, integrated, agile and secure to enable the use of data and artificial intelligence (AI) at scale. Here are some examples of the types of architectures well suited for data democratization.

Data fabric

Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance.

Data within a data fabric is defined using metadata and may be stored in a data lake, a low-cost storage environment that houses large stores of structured, semi-structured and unstructured data for business analytics, machine learning and other broad applications.

Another approach to data democratization uses a data mesh , a decentralized architecture that organizes data by a specific business domain. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata. Then, it applies these insights to automate and orchestrate the data lifecycle. Instead of handling extract, transform and load (ETL) operations within a data lake, a data mesh defines the data as a product in multiple repositories, each given its own domain for managing its data pipeline.

Like microservices architecture where lightweight services are coupled together, a data mesh uses functional domains to set parameters around the data. This lets users across the organization treat the data like a product with widespread access. For example, marketing, sales and customer service teams would have their own domains, providing more ownership to the producers of a given dataset, while still allowing for sharing across different teams.

Data fabric and data mesh architectures are not mutually exclusive; they can even be used to complement each other. For example, a data fabric can make the data mesh stronger because it can automate key processes, such as creating data products faster, enforcing global governance, and making it easier to orchestrate the combination of multiple data products.

Read more: Data fabric versus data mesh: Which is right for you?

Key considerations for data democratization

As more organizations seek to evolve toward a culture of data democratization and build the architecture to support a data literate culture, they’ll realize several benefits—and encounter a few challenges along the way. Here are some advantages—and potential risk—to consider during this organizational change:

Productivity

Many companies look to data democratization to eliminate silos and get more out of their data across departments. The necessary data integration it requires reduces data bottlenecks, enabling business users to make faster business decisions and freeing up technical users to prioritize tasks that better utilize their skillsets. The result is greater efficiency and productivity.

Data security is a high priority. Data democratization inherently helps companies improve data security processes by requiring deliberate and constant attention to data governance and data integrity. There is a thoughtful focus on oversight and getting the right data in the hands of the right people resulting in a more comprehensive data security strategy.

Risk of data swamps

A data swamp is the result of a poorly managed data lake that lacks appropriate data quality and data governance practices to provide insightful learnings, rendering the data useless. Too many businesses struggle with poor data quality; data democratization aims to tackle this problem with comprehensive oversight and data governance. By recognizing data as a product, it creates greater incentive to properly manage data.

Agile data use

Data democratization counteracts the problem of data gravity, or the idea that data becomes more difficult to move as it grows in size. Things like massive stores of customer data are approached more strategically, allowing companies to maintain access as the company scales.

User-friendly tools

Data democratization seeks to make data more accessible to non-technical users, in part, by making the tools that access the data easier to use. This includes tools that do not require advanced technical skill or deep understanding of data analytics to use.

How to get started with data democratization

As with any major change in business operations, companies should develop a comprehensive data strategy to reach their data democratization goals. Key steps include:

• Define business and data objectives –What are your company’s goals? What are your data and AI objectives? The alignment of data and business goals is essential for data democratization. By tapping the expertise of stakeholders, you can ensure your objectives are inclusive and realistic.
• Perform a data audit –How is data managed today? Examine what’s working, what is not and identify bottlenecks and areas where better tools and increased access are needed. Understanding the current status of your data management helps you understand what changes the organization needs to make.
• Map a data framework –When you achieve full data democratization, what will that look like? Design a path toward that goal, defining where application modernization, data analysis, automation and AI can help get you there.
• Establish controls –This is where you lean on data allies to help with compliance across the organization. How will data standards and process be communicated and enforced? Use this step to create and implement data governance policies.
• Integrate your data –It’s common for organizations to suffer from a lack of visibility between departments. Implementing data democratization means breaking down these siloes and designing a way to effectively integrate processes in a way that encourages adoption.
• Train and empower employees –Successful implementation of data democratization requires employees to have the right level of data literacy to access and use the data effectively. Look to data leaders to drive adoption and make data literacy part of the new hiring process. Train employees on how data democratization can improve their work outcomes and improve customer experience.

Use data democratization to scale AI

Once your data democratization journey has begun, teams can begin to look at what this new data paradigm can bring, including advancing new tools like AI and machine learning. Here are some ways companies can use data democratization to enable wider AI implementation:

Define AI use cases

Discuss business analytics and automation priorities and decide where to implement AI first. For example, you may want to invest in analytics tools to develop internal business intelligence reports, real-time customer service chatbots and self-service analytics for different business teams. It’s likely you can’t manage implementing these AI tools all at once, so define the best areas to use AI first.

Identify data sets

Not all data within your company is right for AI, or use cases for that matter. Examine your data sets and determine which ones are right for further research to see if they will help you tackle relevant use cases. With data democratization in place, your company should have greater insights into the quality and availability of data to drive this process, and the ROI for each use case.

Use MLOps for scalability

The development of machine learning (ML) models is notoriously error-prone and time-consuming. MLOps creates a process where it’s easier to cull insights from business data. It also optimizes the process with machine learning operations (MLOps) which uses prebuilt ML models designed to automate the ML model-building process.

Make AI transparent

Data democratization ensures data collection, model building, deploying, managing and monitoring are visible. This results in more marketable AI-driven products and greater accountability.

IBM and data democratization

There are two key elements for data democratization: it starts with the right data architecture, but is amplified by the right automation and AI solutions. IBM offers a modern approach to designing and implementing a data fabric architecture that helps organizations experience the benefits of data fabric in a unified platform that makes all data—spanning hybrid and multicloud environments—available for AI and data analytics.

Watsonx is a next generation data and AI platform built to help organizations multiply the power of AI for business. The platform comprises three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.

Together, watsonx offers organizations the ability to:

• Train, tune and deploy AI across your business with watsonx.ai
• Scale AI workloads, for all your data, anywhere with watsonx.data
• Enable responsible, transparent and explainable data and AI workflows with watsonx.governance

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• Data Democratization: What It Means and Why It Matters

February 21, 2024

In today’s fast-paced digital landscape, the ability to access, analyze, and act upon data has become a cornerstone of organizational success. Data democratization is the process through which data is made accessible to all users, not just those who have experience with data analytics or writing code. This ensures that individuals across all levels of an organization can engage with data directly, fostering a culture of informed decision-making and data-driven innovation.

The significance of data democratization in the current data landscape cannot be overstated, as it fundamentally shifts how decisions are made, moving from a model of centralized data control to a more inclusive, collaborative approach. The ability to access data freely empowers teams to derive meaningful insights, enhancing decision-making processes and giving businesses a competitive edge.

Understanding Data Democratization

Core concepts.

• Accessibility: The bedrock of data democratization is unrestricted access to data for all stakeholders. This means breaking down barriers that limit data availability, ensuring that everyone, from the CEO to the frontline employee, can access the information they need when they need it.
• Empowerment: Empowerment goes hand in hand with accessibility. Providing individuals with the tools and knowledge to analyze data and derive meaningful insights is crucial. This involves not just access to data but also to analytical tools and training.
• Collaboration: A culture where data is viewed as a shared resource fosters collaboration across departments. Data democratization encourages a multidisciplinary approach to problem-solving, leveraging diverse perspectives for richer insights.

Historical Context

The shift from centralized to decentralized data management marks a significant evolution in the field. Technological advances, particularly in cloud computing and self-service BI tools, have been pivotal in driving the democratization of data, enabling more agile and adaptable data access frameworks.

Importance of Data Democratization

Breaking down silos.

Data democratization facilitates cross-functional collaboration by providing a common ground for data access. This cross-pollination of ideas and information breaks down silos, enhancing organizational agility.

By making data more accessible, organizations see a notable improvement in communication and decision-making processes. When everyone has access to the same data, there’s a shared understanding that can lead to faster, more cohesive action.

Empowering Non-technical Users

The empowerment of non-technical users to independently derive insights through self-service analytics platforms is a cornerstone of data democratization. Tools like Power BI and Tableau have made it possible for anyone to analyze data without specialized training.

In addition, data democratization enhances data literacy, or the ability for business users to understand the organization’s data and work with it effectively. Educational programs that focus on understanding and interpreting data can empower all employees, not just data specialists, to make informed decisions.

Enhancing Innovation and Creativity

Democratizing data access encourages innovative thinking by enabling diverse teams to contribute insights. This diversity in perspective can lead to breakthrough ideas and solutions.

Furthermore, a culture where data is readily accessible fosters an environment of experimentation. Teams are more inclined to test new ideas when they can easily access and analyze the data they need to validate their hypotheses.

Challenges and Considerations

Data security and privacy concerns.

As data becomes more democratized, ensuring the security and privacy of this data is paramount. The balance between accessibility and security is delicate; organizations must navigate this landscape carefully to avoid data breaches and ensure compliance with global data protection regulations, such as GDPR in Europe and CCPA in California .

Employing strategies such as encryption, anonymization, and pseudonymization can protect sensitive information while still making it available for analysis. Furthermore, implementing a comprehensive access control system ensures that employees can only access data relevant to their roles, minimizing the risk of internal data leaks.

Maintaining Data Quality and Consistency

A strong data governance framework is necessary to maintain the quality and consistency of data across the organization. This includes defining clear data standards, metadata management practices, and data stewardship roles to ensure that data across systems is accurate, consistent, and accessible.

In addition, regular audits, data cleaning processes, and validation checks are crucial for maintaining data integrity. Investing in data quality management tools and technologies can automate many of these tasks, ensuring that data democratization efforts are built on a foundation of reliable information.

Overcoming Resistance to Change

Adopting data democratization requires a significant cultural shift within organizations. Moving from a model of restricted access to one of openness can meet resistance from those accustomed to traditional hierarchies of data control. Overcoming resistance often involves extensive training and education initiatives. These programs are designed to demonstrate the value of data democratization and equip employees with the skills they need to participate effectively.

Technological Enablers of Data Democratization

Self-service bi tools.

Tools like Power BI , Tableau , Looker , and Qlik have revolutionized the way organizations access data. These platforms offer user-friendly interfaces that make data analysis accessible to all, regardless of technical expertise.

Cloud Computing

The scalability and accessibility benefits of cloud computing have been instrumental in advancing data democratization. Cloud solutions like Snowflake , Databricks , and Google BigQuery provide a flexible, cost-effective way for organizations to manage and access data.

Data Catalogs

Data catalogs like Alation are becoming increasingly popular at organizations that need to provide access to large volumes of data. These tools can make it easy for anyone at the business to explore available datasets and see statistics like the most popular datasets, the upstream and downstream consumers of datasets, whether datasets have been validated by data quality tools, and more.

AI and Machine Learning Integration

The integration of AI and machine learning technologies automates the process of generating insights from data, making it easier for non-experts to interpret complex datasets. With new generative AI tools, non-technical users can even formulate complex data analysis questions in natural language, and those queries can be generated into SQL to retrieve the appropriate data. This automation is a key factor in enabling broader access to data analysis.

Best Practices for Implementing Data Democratization

Establishing a data culture.

Data democratization is a cultural shift just as much as it is a technological change. The support of leadership is crucial in fostering a data-centric culture. Leaders must champion the cause of data democratization, demonstrating its value and encouraging its adoption across the organization.

In addition, grassroots efforts play a significant role in promoting a culture of data democratization. Encouraging advocacy among employees can help build momentum and ensure that teams understand the value of data-driven insights.

Providing Adequate Training and Support

For data democratization to succeed, initiatives aimed at enhancing the data literacy of employees are essential. These programs should focus on building the skills necessary for effective data analysis and decision-making.

Keep in mind that the landscape of data and analytics is constantly evolving. Organizations must commit to ongoing training and support to keep pace with new technologies and methodologies.

Implementing Robust Data Governance

Implementing role-based access controls is a key aspect of data governance. These controls ensure that individuals have access to the data they need, while protecting sensitive information. In addition, continuous monitoring and auditing processes are essential to maintaining the integrity of data and ensuring compliance with data protection regulations.

Real-world Examples and Case Studies

Success stories.

Many organizations have successfully embraced data democratization, leading to measurable improvements in operations and decision-making. These case studies highlight the tangible benefits of making data more accessible across all levels of an organization.

Airbnb is often cited for its innovative use of data democratization to empower employees across the company. They developed an internal data portal named Dataportal, which serves as a one-stop-shop for employees to access and analyze data. This tool not only provides access to data but also includes metadata, context, and tools for data visualization. By enabling all employees to make data-informed decisions, Airbnb has fostered a culture of curiosity and experimentation, leading to continuous improvement in their offerings and customer experiences.

Spotify employs a comprehensive approach to data democratization, focusing on creating a seamless and collaborative data environment. They have developed several internal analytics tools, such as Lexikon, a self-service portal where employees can find and understand data sets, and Backstage , an open platform for building web-based tools, including those for data visualization and analysis. These initiatives help Spotify maintain its competitive edge in the music streaming industry by enabling quick, data-driven decisions.

Netflix’s success is deeply rooted in its data-driven culture, where data democratization plays a key role. The company uses a combination of open-source tools and custom-built solutions to ensure that data is accessible and actionable for all employees. Netflix’s data platform allows teams to analyze viewer preferences, optimize streaming quality, and make informed content acquisition and production decisions. This approach has enabled Netflix to tailor its offerings to user preferences, significantly enhancing viewer satisfaction and retention.

Google’s approach to data democratization extends both internally and externally. Internally, Google encourages a culture of data-driven decision-making, supported by tools like BigQuery and Data Studio, which allow employees to analyze data and share insights easily. Externally, Google provides public access to vast datasets through Google Cloud Platform , enabling businesses, researchers, and developers to leverage Google’s infrastructure for their data analysis needs. This strategy not only enhances Google’s products and services but also contributes to the broader field of data science and analytics.

Lessons Learned from Failures

Data democratization initiatives, while transformative, are not without their challenges. Common pitfalls often arise from both technical and cultural aspects of an organization’s approach to democratization. Recognizing and addressing these pitfalls is crucial for the success and sustainability of democratization efforts.

One significant pitfall is neglecting data governance and quality control in the rush to make data widely accessible. This oversight can lead to inconsistencies, inaccuracies, and ultimately, mistrust in the data. Organizations might find themselves grappling with data swamps rather than data lakes, where valuable insights are obscured by irrelevant or erroneous information. To recover from this setback, companies need to implement robust data governance frameworks that define clear policies for data quality, privacy, and security. They should also consider leveraging tools such as automated data quality monitoring .

A second hurdle is compliance and privacy risk. In the enthusiasm to democratize data, organizations may inadvertently expose sensitive information. This pitfall underscores the importance of incorporating security and privacy considerations into the democratization strategy from the outset. Recovery entails conducting thorough audits of data access controls and ensuring that all data sharing complies with regulatory requirements. Implementing a comprehensive data classification system and adopting a principle of least privilege for data access can mitigate these risks.

Another common challenge is the underestimation of the cultural shift required for effective data democratization. Organizations might face resistance from employees who are either skeptical of the new approach or lack the skills to engage with data effectively. This situation calls for a concerted effort in education and training to build data literacy across the organization. Creating a culture that values data-driven decision-making involves not just providing the tools for data access but also fostering an environment where questions and experimentation are encouraged. Encouraging leadership to model data-driven behaviors can also help in overcoming resistance and embedding data democratization into the organizational culture.

Finally, the technical implementation of data democratization tools can sometimes be overly complex, discouraging non-technical users from engaging with the data. If users find it difficult to access or interpret the data, they’re less likely to use it for decision-making. To address this issue, organizations should prioritize user experience in the design of data tools and platforms, ensuring they are intuitive and accessible to all employees, regardless of their technical expertise. Gathering user feedback and conducting usability testing can guide iterative improvements, making data tools more user-friendly and effective.

Future Trends in Data Democratization

As we look toward the future of data democratization, several trends are poised to further transform how data is accessed, analyzed, and utilized across industries.

First, the future of data accessibility is closely tied to advancements in user interface (UI) design and interactivity. As data democratization seeks to make data accessible to users regardless of their technical expertise, we anticipate a shift towards more intuitive, conversational, and AI-driven interfaces. Generative AI will play a pivotal role, enabling users to query and interact with data through natural language.

Second, the integration of data democratization with emerging technologies such as blockchain and the Internet of Things (IoT) promises to redefine the landscapes of security, transparency, and real-time data analysis. Blockchain technology, with its inherent security and decentralization features, offers a compelling solution for managing access and ensuring the integrity of shared data. This could revolutionize areas such as supply chain management, financial services, and identity verification, providing a secure and transparent framework for data sharing. Meanwhile, IoT’s proliferation will generate vast amounts of data from connected devices, offering unprecedented insights into consumer behavior, operational efficiency, and environmental monitoring.

Finally, on a global scale, data democratization holds the potential to level the playing field for businesses of all sizes and across regions. Small to medium-sized enterprises (SMEs) will gain access to insights and analytical capabilities once reserved for larger corporations, fostering innovation and competition. For societies, the democratization of data can lead to more informed citizenry and enhanced public services, as government data becomes more accessible and actionable. Furthermore, this shift promises to accelerate global collaboration on pressing issues such as climate change, public health, and economic development, by making relevant data more widely available and actionable.

The journey towards data democratization is both challenging and rewarding. By embracing this approach, organizations can unlock the full potential of their data, fostering a culture of innovation and informed decision-making. The key to sustained success lies in commitment to accessibility, empowerment, and collaboration.

As discussed in this article, data quality is essential for data democratization to succeed. Business users need to feel confident trusting the data, or they will be unlikely to use it, and will fall back to relying on gut instinct rather than making data-driven decisions. In addition, if there are quality problems, democratization will expose them to more people at the organization, increasing their negative impact and generating a flood of requests for help from data analyst teams. For this reason, having a robust data quality monitoring solution in place paves the way for data democratization efforts.

To learn more about how Anomalo’s data quality monitoring platform can help, request a demo .

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General IS Topics

Data democratization: toward a deeper understanding

Presenter Information

Hippolyte Lefebvre , University of Lausanne Follow Christine Legner , University of Lausanne Follow Martin Fadler , University of Lausanne Follow

Paper Number

Description.

Owing to a lack of access and skill, most of the data that companies are creating today is unused, even though it is widely viewed as a strategic asset. To overcome this obstacle, enterprises are establishing data democratization initiatives that can empower employees to use data and extract additional business value from them. However, IS research on data democratization has been scarce and has yet to explain how companies build their data democratization capability. Leveraging a multiple case study involving eight companies, we identify five enablers of data democratization: (1) Broader data access, (2) Self-service analytics tools, (3) Development of data and analytics skills, (4) Collaboration and knowledge sharing, and (5) Promotion of data value. As academic contribution, our findings clarify the concept of data democratization and shed light on the differences between traditional and born-digital companies. For practitioners, our study delivers actionable insights to tailor their data democratization initiatives.

Recommended Citation

Lefebvre, Hippolyte; Legner, Christine; and Fadler, Martin, "Data democratization: toward a deeper understanding" (2021). ICIS 2021 Proceedings . 7. https://aisel.aisnet.org/icis2021/gen_topics/gen_topics/7

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Demystifying Data Democratization: Unleash the Power of Big Data for Everyone

Table of Contents

However, between 60% and 73% of it goes unused for enterprise analytics.

That’s a lot of untapped value.

Although there’s plenty of technology to help leverage big data and costly data centers effectively, many organizations still consider data analytics a responsibility of IT.

But to stay competitive, that mindset must change. Agile, data-driven enterprises have already embraced data democratization: the process that shifts responsibility for data analytics from IT to all users across the organization, regardless of their technical expertise.

So if you haven’t democratized your enterprise data management strategy, now’s the time — because two-thirds (or more!) of your data shouldn’t be going to waste!

Once you get a grasp on data democratization, the solutions it poses to business and data management challenges are apparent. Yet it’s no walk in the park — you need to brace for potential barriers to adoption and keep up with the latest data-centric trends and tech.

Along the way, it’s crucial to understand the importance of data governance and why you must have a data governance framework in place before democratizing your data.

All of this below — plus a six-step roadmap to unlock the potential of (all) your enterprise data through data democratization.

What is Data Democratization?

Here’s, probably, your current situation:

Traditional data management and data analytics operations require users to submit a request to IT for the information they need beyond regularly distributed reports.

So, if a sales manager wanted to get a mid-month status report on sales revenue, they would have to wait for IT to produce and deliver the information.

As a result, their ability to make data-driven decisions or changes in sales strategy would be at the mercy of IT’s workload.

Data democratization solves this bottleneck:

The introduction of data democratization eliminates the siloed, outdated process you’re used to. Instead, it enables everyone (creators, analysts, agencies, et al) to access and make sense of the enterprise’s data regardless of their technical abilities or job function.

In this format, data ownership and responsibility is shared throughout the organization. Everyone can contribute and access with roughly the same scope (IT may still have in place safeguards, of course).

But this shift to self-service data and analytics doesn’t happen overnight.

Is Your Enterprise Ready for Data Democratization?

Like any process, data democratization must be continually monitored and promoted to drive maximum value for the organization. For established businesses, the democratization of data facilitates organizational agility and the ability to make data-driven decisions.

Once data democratization processes are in place, valuable IT professionals can focus on more critical business, technology, infrastructure, and strategy concerns.

However, taking the steps to harness the power of enterprise data may require a cultural shift ; one that can only happen when the following are true of your employees:

Comfortable – Employees feel comfortable asking questions and working with data

Armed – Employees are armed with the right tools and trained accordingly

Empowered – Employees are empowered to make decisions based on self-service analytics

What Business Challenges Does Data Democratization Address?

Organizations that democratize their data can address business challenges associated with:

Information access – Data democratization unlocks access to information previously confined in a data warehouse or data lake and accessible only by IT professionals.

Resource limitations – Data democratization alleviates pressure on valuable IT resources and frees them from having to fulfill data and reporting requests.

Data enablement – Data democratization allows organizations to get value from their vast data stores by empowering users to serve as their own data analysts or data scientists.

(Data) Governance Before Data Democratization

Of course, providing unfettered access to data before you achieve data governance maturity could have a devastating impact on a business. Without a robust data governance framework, sound decision-making and regulatory compliance are virtually impossible.

But embracing and benefitting from data democratization involves walking a fine line. Organizations must ensure their efforts to expand information access don’t result in poor business decisions or sow more consumer distrust .

That’s why key elements of your data governance strategy must be rock solid before implementing data democratization processes:

Data security – Policies that safeguard data from being compromised by internal and external threats

Data privacy – Policies that control how enterprise data is collected, shared, and used

Data quality –  Policies that establish standards and ensure data sets are accurate, consistent, complete, and timely Once the transition to broader access to data is complete, IT professionals can focus on continued data standards , data governance, and compliance with regulatory and consumer privacy laws .

Barriers to Adoption and Success

There are pros and cons associated with taking on any new initiative. And although there are far more reasons organizations should democratize their data, it’s essential to acknowledge the potential downside of expanding access to information.

Poor decision making – Employees don’t suddenly achieve data literacy or become data experts just because they have access to data democratization tools. Concerns that less savvy employees might misinterpret data and make bad business decisions are a legitimate concern.

Data misuse – Expanding access to information increases the potential of valuable or sensitive data being misused. Data security is always a top priority. Whether misuse is well-intentioned or nefarious, the result is the same — exposure to data breaches, theft, regulatory non-compliance, and possible fines or reputational damage.

Duplicate data – Giving more people self-service access to the same data can result in duplicative efforts across teams and business units. This can be a costly waste of time and resources when compared to entrusting IT with data analytics and distribution.

Upfront Cost – Data democratization requires investment in technology and training. For organizations that haven’t already modernized their data warehousing and data management methods, it is even more costly. Data democratization is a strategic investment, but an investment nonetheless.

Each of these risks is mitigated if you already have effective data governance policies in place. In addition, understanding the benefits of democratizing data can also help put concerns to rest.

Core Benefits of Data Democratization

There are many reasons organizations should consider making enterprise data readily available to their users. Here are five key benefits of democratizing data.

Support a modern workforce

With just 1 in 5 back to full in-person work since the start of the pandemic, many organizations find ways to ensure their employees can be effective while working remotely. Data democratization enables users to access the information they need easily and instantly so decisions made outside the office are equally trustworthy and sound.

Empower employees

Empowered employees are confident and committed to meaningful business goals and take the initiative to achieve them. Simply giving more people broader access to information is empowering and provides an opportunity for every employee to influence and drive business growth. Democratizing data can also promote a collaborative culture that fosters innovation.

Expedite decision-making

Being able to make decisions on a dime, supported by quality data, is a hallmark of agile, data-driven organizations. By embracing data democratization and promoting data literacy, a regular user in marketing operations can function as a data analyst while evaluating the outcome of a marketing campaign. Equipping data users with autonomous decision-making ability can earn a competitive advantage over businesses stuck in the past.

Improve operations

Expanding access to enterprise data streamlines operations among functional areas of the business. Removing barriers to information means less time wasted accessing individual data silos or attending project status meetings. Within a data democracy, for example, sales benefits from marketing data governance and the ability to access marketing data to monitor the leads generated by a specific campaign. And marketers can access sales data to see the effectiveness of a new marketing channel.

Enhance customer experiences

Today’s consumers expect excellent customer service — actually, they expect excellence throughout their entire customer experience or the sum of all their interactions with your brand. Businesses that give every employee involved in the customer journey access to key information are better positioned to meet customers’ expectations and changing needs.

Your 6-Step Data Democratization Strategy

Democratizing big data at scale requires a coordinated, well-executed strategy. However, assuming you’ve established essential data governance practices, you have a solid foundation for success.

Here’s a suggested six-step roadmap to follow.

1. Secure leadership committment

Data democratization supports organizations that aim to be more data-driven. This is a strategic shift requiring a significant investment in self-service analytics tools and training. So it is critical to first secure leadership support. You’ll need to demonstrate that your approach aligns with the needs and priorities of individual business units to build a true data democracy.

2. Take stock of your data ecosystem

As an organization grows, it becomes more challenging to manage the increasing volume of incoming data. And if that data remains siloed and inaccessible to most users, its potential value can never be realized.

Ensuring your processes and data infrastructure scale with the increased demand for data starts with taking stock of your data ecosystem and identifying, then addressing problematic or fragmented systems.

3. Unlock your legacy data

When it comes to legacy systems, remember: ‘There’s gold in them thar hills’ . Of course, effective data democratization and data-driven decision-making don’t just involve data from the present-day forward. It’s also important to unlock the data stuck within legacy data silos and systems.

Accomplishing that is easier said than done, given the inflexibility of legacy platforms. So, it’s essential to budget for data integration tools and architecture design. Together, they’ll ensure interoperability between legacy databases, data management platforms, and cloud-based systems.

4. Make data accessible to all

Data-driven organizations don’t limit data analytics and data access to their IT departments. Instead, they empower users with (the right) access to make decisions and complete their job responsibilities more quickly and effectively.

Ensuring everyone has access to relevant data is an essential principle of data democratization. Providing that access requires an investment in user-friendly technology that’s suitable for technical and non-technical users alike. Data analysis dashboards or data visualization tools provide an easy way to view and decipher trends, outliers, and patterns in enterprise data.

5. Promote self-service

Enabling greater access to data is just one aspect of data democratization. But to harness its real power, users must incorporate data analytics and reporting into their daily work routine.

To promote self-service, of course, you’ll need the right data analysis dashboard. To compel employees to use it, you’ll need to instill trust in the enterprise data. Data management platforms, data integration solutions, and data quality software will ensure quality data and lead to better adoption.

6. Provide continued education

Non-technical users typically outnumber savvy users within an organization by a lot. That’s why effective onboarding and continuous training must be a strategic priority.

Together they’ll determine the success of your data democratization efforts. All data users must understand and feel comfortable with visualizations, dashboards, and analytics in order to leverage enterprise data as a competitive advantage.

The Future of Data Democratization

As more enterprises establish data-centric strategies and democratize their data, we can expect new trends to emerge. Here’s a sampling:

Technology development by non-IT professionals

Countless organizations have already discovered that arming all users with access to data and self-serve analytics fuels better business decisions and fosters innovation. As more organizations create data democracies, Gartner projects that by 2024, 80% of tech products and services will be built by non-IT professionals.

Innovation in patient care

Although the vast majority of medical data still resides on disparate systems, the technology for managing and leveraging patient data is widely available and constantly improving. We can expect further democratization of data to fuel new applications that leverage a central repository of healthcare data.

Data analytics tools that incorporate predictive analytics and machine learning will improve patient care, uncovering recommendations based on a 360-degree view of the patient.

Click the case study to learn how Claravine helped a leading healthcare company “collectively optimize the customer experience rather than have siloed brand activities.

Artificial intelligence’s (AI) emerging role

As more organizations democratize their data, simply providing employees with data access and self-service analytics tools won’t be enough to gain a competitive advantage. Data must also be contextualized.

More organizations will incorporate AI capabilities to provide richer analytics and predictive capabilities to help users understand why that data matters and what should be done with it.

Rolling Out a Data Democracy

Democratizing your data isn’t as simple as flipping a switch, but it is easier to achieve when you have key processes and infrastructure already in place.

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Why Data Democratization Matters Today

In this age of data dominance, data democratization becomes a lifeline for any organization trying to harness the most out of its information-based assets. Data democratization ensures access to data for all employees across varying organizational departments without technological barriers, which enables data-based business decisions to be made. Empowering the team members with the approach will open doors for improved collaboration and innovation.

Data democratization is one of the most important aspects in the modern corporate world for a company's success. As they face constant pressure for innovative and efficient business operations, there is a need for a robust and data-driven culture. Organizations must break down silos that confine data within certain IT departments so that information can flow and insights can be accessed from every corner of the organization. This holistic view of data utilization is very important for any organization that wishes to succeed in the ever-changing marketplace of today.

Here are the 5 Key Takeaways from this Why Data Democratization Matters Today article: 

Data democratization enhances business innovation and efficiency

Accessible data empowers informed decision-making across all levels

Technical, cultural, and security challenges hinder implementation

Strong data governance and suitable tools are crucial for success

Data democratization boosts competitiveness and agility in businesses

In this article, we explore the transformative impact of data democratization on today's businesses, emphasizing its essential role in fostering innovation, streamlining operations, and enabling a data-driven decision-making culture.

Table of Contents

The importance of data democratization, challenges in implementing data democratization, practical tips for data democratization, case studies.

Data democratization transforms companies' operations by significantly enhancing business innovation, boosting operational efficiency, and encouraging informed decision-making across all levels.

Enhancing Business Innovation

Open data access is a powerful catalyst for innovation across departments. By democratizing data, organizations enable employees from diverse functions—be it marketing, finance, or operations—to tap into relevant data sets without the bottleneck of gatekeepers. It is in that accessibility that creative sparks fly, as different perspectives and skills among employees can be brought together on data insights, solving complex problems and innovating solutions.

A good case in point may be a marketing team running analyses on customer behavior data to create personalized campaigns while product teams use the same data for enhancing or developing new product offerings.

Boosting Operational Efficiency

Accessible data also plays an essential role in increasing operational efficiency. With organizational team members across various departments having real-time access to data, they can identify inefficiencies, streamline processes, and reduce any waste.

For example, a company specializing in logistics would be able to democratize data for field operators so that they have supply chain data in real-time to allow better on-the-spot decisions that enhance delivery speeds while cutting downtime. Equipped with these performance analytics, sales teams can adjust strategies on the fly, resulting in better resource allocation and increasing productivity in sales.

Encouraging Informed Decision Making

Data democratization has a huge impact on strategic decision-making. When data is accessible, it provides the leaders and employees at all levels of an organization with the data that will aid them in making well-informed decisions. This broad access to data means decision-making will be based on deep insights into real-time data rather than hunches or limited information.

For instance, access to global market trends and analytics on consumer behavior would allow executives to decide on potential markets for expansion or change marketing strategies due to changing consumer preferences.

While the benefits of data democratization are clear, the hurdles organizations need to face to implement such an approach are immense. These come from a wide range of technical hurdles to cultural barriers and security concerns, which can potentially hamper the successful development of an adopted data-driven culture.

Technical Hurdles

One of the major technical challenges is integrating disparate data sources and platforms. For many organizations, this would be an assortment of systems and software, with information residing in data silos scattered across departments.

Achieving a unified system that facilitates easy access to data requires substantial investment in integration technologies and expertise. This must be done so that data from various sources can be aggregated, standardized, and readily available in a user-friendly format, which is often more straightforward in theory than practice since compatibility issues and the complexity of data structures come into play.

Cultural Barriers

Changing organizational mentality toward the accessibility of open data represents a formidable cultural barrier. Historically, data has been something that belongs to IT departments or perhaps certain data departments, and that has been fundamental to the gatekeeping culture surrounding data.

Shifting these mindsets to foster a culture of data sharing and working together at any organizational level involves a cultural change. The shift involves training and changing the underlying attitude and values toward the ownership and transparency of data, which can be difficult in established and rigidly structured companies.

Security Concerns

Balancing accessibility with data protection is another critical challenge in data democracy.  The more data that becomes accessible, the more under the threat of security breaches or misuse. Organizations should, therefore, develop and enforce robust data governance and effective security protocols to ensure that organizations can access data for making decisions while protected from any internal or external threat. This would include technological measures such as advanced encryption and access control, as well as periodic audits , compliance checks, and workforce training on best practices for securing data.

Successfully implementing data democratization requires a strategic approach that addresses technical, cultural, and security challenges. Here are practical tips on how organizations can facilitate data accessibility while maintaining integrity and security.

Establishing a Data Governance Framework

A robust data governance framework is foundational to data democratization. The framework should detail policies defining who accesses what kind of data under what conditions. Roles and responsibilities should be clearly outlined and set for different access levels to prevent access from unauthorized users and promote transparency. For example, all employees can view broad performance metrics, while only certain roles gain access to sensitive data.

Such policies can be implemented only through an interdepartmental approach so that they reflect the datasets' requirements and associated risks. The framework should specify the procedures of regular audits and compliance checks to assess staff's adherence to the internal policies and those from other regulatory bodies.

Investing in the Right Tools

Another very important step to achieving data democratization is investing in the right tools and technologies. Tools that easily facilitate access to data, visualization, and data analysis empower every employee to leverage the information regardless of their technical expertise. Some solutions include self-service BI (Business Intelligence) tools, data visualization software, and integrated data platforms that allow insights into data without the need for complex programming.

The chosen tools should be user-friendly, and the team must be able to access data in real-time to make timely decisions. Investments in integration platforms should consolidate the right data sources into a single coherent and manageable system, thereby enhancing overall access and usefulness.

Training and Empowering Non-Technical Users

Truly democratizing data will only be achieved once organizations focus on enhancing employee data literacy. Training programs must introduce data interpretation and elaborate on making decisions based on data, from basic knowledge of data analytics to security practices and the use of specified tools.

Moreover, organizations should offer constant support and learning so that all team members are informed of new tools, technologies, and best practices in handling data. Empowering people with information and skills drives a culture of data innovation and engagement at all levels of an organization.

Related Reading: Data Analytics vs Business Intelligence

Netflix exemplifies data democratization by granting broad access to data across its organization, empowering employees at all levels. This approach has allowed content creators, marketers, and analysts to make informed decisions, significantly contributing to the personalized user experiences and highly tailored content recommendations that have become synonymous with the brand. This strategic use of data has been instrumental in developing successful original programming and enhancing viewer satisfaction.

Walmart has implemented a cloud analytics hub known as "Data Café," which democratizes data access across various departments. This initiative has improved inventory forecasting, reduced stockouts, and optimized product placement in stores. By making data accessible and actionable, Walmart has achieved cost savings, enhanced operational efficiency, and improved customer experiences.

Airbnb has embraced data democratization to enhance both host and guest experiences. Hosts receive detailed data about their property's performance, enabling them to optimize pricing and availability, while guests benefit from robust filters to find accommodations that best meet their needs. This democratization supports Airbnb's commitment to providing exceptional, personalized service and maintaining competitive advantage.

These use cases illustrate how data democratization offers a digital transformation for business operations, driving innovation and enhancing customer engagement across diverse industries.

Data democratization has proven to be a game-changer for businesses, facilitating enhanced innovation, improved operational efficiency, and more informed decision-making across all levels of an organization. Enabling access to data without technical barriers empowers employees to contribute to their company's success and adapt quickly to market changes and consumer demands.

Looking ahead, the future of data democratization in businesses seems poised for further growth. As companies continue to recognize the value of a data-driven culture, the emphasis will likely shift towards refining data governance frameworks, investing in user-friendly tools, and enhancing employee data literacy. This evolution will ensure that data democratization keeps pace with technological advancements and remains integral to organizational strategy and success.

Integrate.io’s low-code data integration platform was built for data democratization. Line of Business teams and non-technical users build and manage their data pipelines seamlessly with Integrate.io ensuring that they meet their deadlines and core IT can focus on building core product. To learn more about how Integrate.io can help with data democratization, schedule a time to speak with one of our team today.  

What is data democratization in simple terms?

Data democratization is the practice of making data accessible to as many people within an organization as possible without requiring specialized skills to understand it. This concept aims to empower all employees to use data in their decision-making processes, breaking down traditional barriers where only certain departments or individuals had access to valuable business information.

Why is data democratization crucial for business growth?

Data democratization is crucial for business growth because it allows for more informed decision-making across all levels of an organization. When everyone has access to data, it leads to increased innovation, quicker responses to market changes, and more proactive data management. This broad access helps companies become more agile, make better strategic decisions, and identify opportunities or issues faster, thus driving growth and competitive advantage.

What are the main obstacles to data democratization?

The main obstacles to data democratization include technical challenges related to integrating disparate data systems, cultural resistance within organizations used to hierarchical data controls, and security concerns about protecting sensitive information while it is made accessible to a broader audience. Overcoming these challenges requires strategic planning, investment in technology, and a change in company culture to embrace open data access.

How does data democratization impact data security?

Data democratization can pose challenges to data security by increasing the number of access points to sensitive information. However, it also offers an opportunity to strengthen security measures. Effective data democratization requires robust data governance and advanced security protocols to ensure that data is not only accessible but also protected from unauthorized access and breaches. This involves implementing role-based access controls, regular audits, and comprehensive data protection strategies.

Can data democratization help in reducing business costs?

Yes, data democratization can help in reducing business costs by improving operational efficiencies and enabling better decision-making. With broader access to data, organizations can identify and eliminate inefficiencies, optimize processes, and reduce wastage. Additionally, democratized data can enhance decision accuracy, reducing costly errors and enabling quicker adjustments to business strategies and operations.

What tools are recommended for data democratization?

Recommended tools for data democratization include Business Intelligence (BI) platforms, data integration tools, and data visualization software. BI platforms like Tableau, Microsoft Power BI, and Looker help users analyze and report on data. Data integration tools such as Talend and Informatica simplify the merging of data from various sources, and visualization tools like Qlik and Grafana help in presenting data in an understandable format to non-technical users.

How can small businesses implement data democratization?

Small businesses can implement data democratization by starting with a clear data governance plan and choosing the right tools that fit their budget and technical capacity. They should focus on training employees to improve data literacy across the organization. Utilizing cloud-based BI and analytics tools can be cost-effective and scalable, allowing small businesses to benefit from data insights without the need for heavy upfront investment in IT infrastructure.

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How to Democratize Data across Organizations: Use Cases

Enterprise AI, like any other technology product, must provide value. And not just across one department or one project but the entire organization. Democratization of data and AI is the ultimate goal enabling practitioners across functions to innovate and maximize the value of the organization’s advanced analytics capabilities. 

According to Gartner, the goal behind data democratization is to “allow non-specialists to be able to gather and analyze data without requiring outside help”. Organizations must equip all employees and stakeholders with easy access to data and no code AI products without extensive or expensive training to achieve this goal. That is why data democratization is often referred to as citizen access.

Many findings prove that organizations that apply this approach make better strategic decisions, have higher efficiency, improved customer satisfaction, and generate more profits. 

Some sources define data democratization as a trend that is part of a larger trend of technological democratization. This means that the benefits of the technology and the technological advances can be accessed by non-technical or non-traditional users, too.

Why should organizations know about the importance of the decentralized value creation of AI? And what should they do if they decide to apply a similar approach? This article will point out several steps organizations should be aware of from the data management perspective. And we will use several use cases by Telia, EQT, Pandora and other companies to emphasize those steps.

Limitations and Solutions around Data Democratization

The key factors that can foster an organization’s data democratization are machine learning, data access, deployment, culture, technology and governance. But, like any other, the process of data democratization has challenges, linked to silos and bottlenecks, complex infrastructure, data access and quality issues, and lack of collaboration and resources. Let’s explore the different use cases from the organizations mentioned above to see what they have done to overcome these challenges. Few of these use cases address the importance of decentralizing data ownership, data teams and infrastructure towards the organization’s data democratization.

Overcoming the silos and bottlenecks

The global jewellery company Pandora has around 27,000 employees, with 6,800 sales points in nearly 100 countries, and 670+ million visits to offline and online stores. The company wanted to use all data from these sources to run the company better, and make strategic and tactical decisions. To overcome any silos and bottlenecks with the centralized structure , the company decided to decentralize the team and to originate them around the tools. The company is using three different processing tools (in-memory storage and compute, data warehouse and data science workspace) to address requirements that are coming from the three main personas that generate data in the company: the enterprise analyst, the front-end engineer, and the data scientist. Other personas participating in the process of generating data include data engineers, analysts, and ML engineers.

“We started with demands of data, and then we started centralizing the data teams. At one point, it became a bottleneck, and now we are embarking on a journey to see if decentralizing the data team and if we do it properly will work for us.”, explains Febiyan Rachman, Senior Data Engineer at Pandora Jewelry.

Promote collaboration

In Pandora’s case, the company decided to partly decentralize the architecture so that the data domain teams could coordinate and collaborate better. Febiyan Rachman explains that every individual domain team will have its automated workspace, but the data product will be put in a common space accessible by the shared workspace where it is expected that collaboration will happen. With this plan, the company also envisions ensuring that the architecture is repeatable.

Overcoming complex infrastructure, data access and data quality issues

Telia is a Swedish multinational telecommunications company and mobile network operator that serves over 25 million customers in 6 different markets. On an average day the company runs over 3 million queries. According to the presentation by Karolina Perzon , Head of Governance & Strategy at this edition of the Data 2030 Summit, Telia has around 350 analytical experts, over 5,000 BI users and works with large volumes of data. Besides a massive data legacy, the company faced a complex IT source structure impacting the overall data quality. 

“Within the analytics team we have been working for a long time to mitigate these issues. We have created workarounds to improve the data quality but that also became very complex and time consuming and required a lot of resources.”, adds Karolina Perzon, Head of Governance & Strategy at Telia.

The solution for overcoming the problem was an updated data strategy and a pragmatic breakdown of activities to quickly prove value of the strategy.

Reasonable usage of tools and resources

Telia also felt that it had an endless loop of proof of concepts (POCs), evaluations, and talks about what is the perfect tool and technology to move forward with data democratization. At this time, the company had implemented a catalog but the capability itself was sparsely utilized.

“It was not because of the catalogue itself, the tool or the technology, but we didn’t have a process that describes how it should be used and implemented. We also didn’t have processes for other issues, such as how to drive development, issue resolution etc.,” says Karolina Perzon. She adds that to overcome the issue, there was a request for an update of the data strategy. 

Juts on the above mentioned use cases, some common proposed steps to overcome any limitations with data democratization include: 

• Inventory of data and know where it is stored, which tools and technologies are used to capture, store, and analyze data, do you have resources to provide analysis.
• Identification how much data-literate the employees are, and for this there are different methods, some simple and some more complex.
• Investment in proper training and ongoing education based on the findings and the needs the organization has. Ongoing training and check-ins are vital for the data democratization journey if the organization wants to see a strong return on investment and benefit from it.
• Assessing potential data solutions. Consider budget, customer service reputation, scalability, and market prominence of each potential tool. 
• Shift from centralized to decentralized data ownership/structure/teams/analytics as part of the data democratization journey.

Decentralization is Crucial for Data Democratization – Use Cases

In the following paragraphs, we’ll focus on the last step mentioned in the section above: data decentralization. Since, according to many, it is seen as an essential step towards data democratization, we’ll share a few approaches to help organizations shift from centralized to decentralized data, ownership, team and analytics. We’ll use the same companies as a positive example with their initiatives and strategies. 

Setting goal and ambition

Establishing a goal, ambition and approach that is adopted to a specific organization, and that will bring value to the organization the fastest is the first step towards data decentralization.

“Taking too much of the best practice approach when it comes to establishing your data strategy, can easily become overwhelming. It requires a lot of resources, effort, and costs, and most likely, you won’t necessarily see long lasting results. I’ve seen this a number of times before. You set up a big project, you bring resources, you point to data owners, you document the processes, you might even procure tools but at the end as the project dissolves, you will lose commitment and go back to the old ways. I won’t say you should not set up a project or bring resources, what I’m saying is, there is no best practice approach that will work for every company. We need to have a very pragmatic approach in terms of how we implement this.” Karolina Perzon, Head of Governance & Strategy, Telia.

When Telia set the ambition they wanted and expected: data self-service for the company’s technical users and later scaled to the business users; data as an asset which for the company was meaning prioritizing the most valuable assets; data driven business decisions based on high quality data; and data democratization by enabling a common language cross markets and departments which, again, means that any given user within Telia, independent of their technical know-how to be able to work with data comfortably. 

Optimizing for adoption of decentralization

Data projects may fail because their assessment takes much time so by the time organizations are done with the assessment, the relevance may pass. 

“To overcome this our strategy is to look at the broken processes and super frustrated users within the organization… So, we find them and tell them that there is technology available to help them out with automation of repetitive tasks. With other words we tell them to iterate and don’t be afraid of shortcuts. Like that you can fail fast and learn from the mistakes and find good solutions. The next level is – solve users’ problems. This leads to strong allies and endorsement in the data journey. Later on we can have access to this type of data which is important for the auditors.”, explains Pedram Birounvand, Head of Data Management at EQT Group.

Building data management strategy and structure 

When Telia started shaping their data strategy these are the five key areas that they started to look at: technical foundation as an enabler, processes and organization, common language as a factor to unlock greater power, communication and training, and privacy and security incorporated by design.

Data governance enablers 

Data governance helps to know what data organizations have, who is the consumer, and what control the organization has on different data points. 

“So, next time when a team decides to build something, go to a data catalog and see to reuse a metric or build a metric from scratch. While you do the documentation you start to find other types of synergies that you can start using like dynamic data masking (important when working with GDPR) and specifying object tagging (important when working with Personal Identifying Information – PII data and confidential and sensitive information).”, explains Pedram Birounvand, Head of Data Management at EQT Group.

Having technology and agile data infrastructure 

Being iterative as part of the organization strategy requires having good technology, think the data experts from EQT Group.

“Compared to the previous traditional data architecture with a centralized and normalized database, we think about different slices we want to do with the data and we are building data teams surrounding those different data products. Those are small teams of 3-5 people focused on certain data domains and they build their own. This has been proven to be agile and successful and they know their customers and understand their pains. But like every architecture model this one contains errors as well.”, explains Pedram Birounvand, Head of Data Management at EQT Group.

Organizing and structuring the data team 

As the data demand grows and there are more use cases executed, organizations may experience bottlenecks. To tackle the challenge, they can think about decentralizing the data teams. As in the Pandora case.  

“So, we have an ingestion team that takes care of ingesting data from hundreds of different sources, that puts the data into a data lake and needs to provide downstream teams with easy to organize raw data. We also have a data warehouse team that has the purpose of modeling the data and making it standardized and ready usable by the whole enterprise. We also have an in-memory analytics team that takes the data from those two platforms and puts it in the in-memory store for the enterprise analysts to use. These four teams are working closely together. Besides them we have a front-end engineering team and the enterprise dashboard portal team. The platform team works to support the initial four teams. It was not easy to come to this structure, it was not an easy journey to get there.”, elaborates Febiyan Rachman, Senior Data Engineer at Pandora Jewelry.

Data management culture and value creation across functions

Building data management culture requires organizations to work close to the business in regards to different types of objectives including: accountability, data quality and sense of urgency. And regarding the communication with the language of value and not technology, Jo Coutuer, Founder of Datamerit and former Chief Data Officer at BNP Paribas Fortis says:

“Anyone in the position of CDO or running data initiative should be the agent of value discovery and agree with the finance people about the financial mechanisms or with the peer about how you are going to recognize it, on which accounts it can be booked…speak with the language as the finance people, chief revenue office or anyone else responsible for the revenue recognition would speak.”

Key Takeaways

There is no silver bullet and best use case to follow when leading an organization towards data democratization. Define your own best practice. See what works best for your organization. There is no one product, one process, and one approach that will get you to what you want from data democratization. But a combination of everything may be the right fit for organizations. The real value is exchanging experiences on data management strategies, governance, teams and how to decentralize them, and how to decentralize the ownership of the data. For more best practices on this, we recommend exploring the agenda of the Data Innovation Summit for 2023. Also, explore Hyperight Premium content to hear first-hand stories from the best global leaders in data democratization.

Featured image: Tyler Franta on Unsplash

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How to overcome the challenges of data democratization

JUN. 19, 2024

Understand where data democratization challenges come from and follow our best practices to address them.

The world is awash in data. Global data volumes are increasing exponentially and are projected to reach nearly 200 zettabytes by 2025.

But despite this data deluge, most companies struggle to harness its full value. Information remains trapped in silos, unable to be leveraged effectively.

Allowing data to flow freely throughout an organization can go a long way toward realizing its full potential. This concept is known as data democratization — it’s rapidly gaining traction in a range of industries, and for good reason.

But the path towards achieving true data democratization is fraught with hurdles. Learn how to overcome these barriers by understanding where they come from and following our best practices for addressing them.

What is data democratization?

Data democratization is an organizational strategy in which even your least data-savvy people can incorporate regular data insight generation and usage into their roles.

Unfortunately, traditional corporate data infrastructures are anything but democratized. Data availability is restricted to a select few within an organization, usually IT staff and database administrators, and employees must submit requests to gain access to the data they need. 

What should be a simple task of pulling relevant information is instead a friction-laden, time-consuming ordeal. This can lead to a cascade of issues, including:

• Missed opportunities: Valuable insights that could help marketing teams prevent customer churn or capitalize on interest in a new product remain locked away in data silos.
• Slow decision-making: Employees must navigate a labyrinth of approvals to access the data they need.
• Inefficient resource allocation: Data and IT professionals spend too much time on routine tasks like generating reports and fulfilling data requests, instead of focusing on more strategic work.
• Limited collaboration: Data silos hinder collaboration between departments, preventing teams from sharing knowledge and insights effectively.
• Reduced employee engagement: When employees feel excluded from the data conversation, it can lead to disengagement and lower morale.

Data democratization is all about breaking down the barriers that prevent business users from accessing, understanding, and using data in their work. Doing so reduces their reliance on IT and empowers them to make more data-driven decisions.

It sounds great on paper, but making your data freely accessible is not without its challenges.

Challenges in pursuit of data democratization

The data literacy gap.

One of the biggest obstacles to data democratization is managing varying levels of data literacy across teams. Even if data is easily accessible, it won’t be of much use if users don’t know what to do with it.

Some employees lack the skills to interpret and analyze data effectively, while others may not have the confidence to use data analysis tools.

Here are some ways to level up your entire employee base:

Invest in comprehensive training

Implement data literacy programs that cater to different skill levels, from foundational courses for beginners to advanced analytics workshops for those with more experience.

Promote a culture of learning

Encourage continuous learning and development around data skills. Offer incentives for employees to improve their data literacy.

Provide user-friendly tools

Choose data analysis and visualization tools that are intuitive and easy to use, even for those without a technical background.

Data silos and infrastructure challenges

Before you can democratize your data, you should collect it in one place. It’s common for data to be scattered across disparate systems and departments, creating silos that hinder access and collaboration.

Outdated infrastructure and fragmented data sources force teams to waste valuable time manually collecting and consolidating information. Plus, without access to real-time metrics, decisions are based on stale information.

To lay the groundwork for data democratization, it’s crucial to address these infrastructure challenges head-on.

Build a data fabric architecture

Investing in modern data architectures, such as data fabrics or data meshes, can weave together disparate data sources, creating a unified and accessible view of your organization's information.

Upgrade legacy systems

Upgrading outdated technology is key to ensuring your data tools and processes work well together. This will make the move to a more open, accessible data environment a whole lot smoother.

Data quality and governance concerns

Data democratization hinges on the reliability and trustworthiness of the data itself. Inaccurate, inconsistent, or poorly secured data can easily lead decision-makers astray.

Consider the following strategies to fortify your datasets:

Drain your data swamp

Data quality issues can accumulate over time, resulting in a chaotic and disorganized “data swamp.” Address these issues by implementing data cleaning and quality control processes .

Establish data governance

Define roles, responsibilities, and processes for managing data throughout its lifecycle.

Prioritize data security and privacy

Implement stringent security measures to protect sensitive data and ensure compliance with relevant regulations (e.g., GDPR, CCPA).

Cost and resource constraints

Data democratization is a great long-term investment, but it typically comes with an upfront cost. Organizations must allocate resources for new technology, training, and potentially hiring additional data professionals.

Here are some ways to mitigate the financial burden:

Start small and scale

Begin with pilot projects focused on specific use cases or departments. As you tally up small wins, gradually expand the program.

Prioritize high-impact areas

Focus on areas where data democratization can deliver the most significant benefits.

Consider cloud-based solutions

Cloud-based data platforms can be more cost-effective than on-premises solutions, especially for smaller organizations.

Resistance to change and cultural barriers

Embracing data democratization can be a big change, and you may encounter pockets of resistance in your organization. This can come in many forms: Some teams may guard their data closely, while others might lack trust in an organization-wide data warehouse .

Resistance can be overcome with a few key steps:

Communicate the benefits

Before your data democratization effort begins in earnest, host sessions with employees that cover the advantages of data democratization for individuals, teams, and the organization as a whole. Be open to questions and concerns they might have.

Lead by example

Once the effort is underway, senior leadership should actively embrace data-driven decision-making and encourage others to do the same.

Create a safe space for experimentation

Finally, encourage employees to explore data without fear of repercussions. Mistakes should be expected in the first several months and should be seen as a teaching opportunity rather than a cause for blame.

Don’t let your data go to waste

Historically, a lack of data held organizations back. But these days, most have more data than they know what to do with. The challenge is allowing it to flow freely and enable useful insights.

The rewards of data democratization are immense. With all the data they need at their fingertips, employees can make more informed decisions and unveil cross-functional insights that were previously hidden in data silos.

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A case for Data Democratization

• Pranjal Awasthi , Jordana J. George
• Published in Americas Conference on… 2020
• Computer Science, Political Science

15 Citations

Exploring the critical success factors for data democratization.

• Highly Influenced
• 22 Excerpts

Towards Employee-Driven Idea Mining: Concept, Benefits, and Challenges

Introducing the enterprise data marketplace: a platform for democratizing company data, um caso de democratização de dados na indústria de óleo e gás, towards avoiding the data mess: industry insights from data mesh implementations, data mesh: motivational factors, challenges, and best practices, designing a feature selection method based on explainable artificial intelligence, datenbasiert energieverbrauch und raumklima öffentlicher gebäude verbessern – literaturstudie mit fokus auf den erfolg von ansätzen, towards a democratization of data in the context of industry 4.0, exploring tenets of data democratization, related papers.

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Data sharing: fix broken data access with synthetic data

Data sharing and data access challenges.

• Data access is increasingly limited within organizations. Data access privileges are getting hard to come by, and rightly so.  According to Gartner: 

"59% of privacy incidents originate with an organization's own employees. Worse still — 45% of employee-driven privacy failures come from intentional behavior (though it may not be malicious)."

• Limiting attack surfaces has become a high priority for companies that suffer major financial and reputational setbacks when data leaks happen. Protecting perimeters is no longer enough. Reducing the amount of unsafe data within the walls of organizations is more important than ever. 
• However, most traditional data governance strategies are not only unsafe, but seriously inefficient, with data scientists spending 80% of their time finding, cleaning, and organizing data.  
• In addition, more and more external stakeholders could benefit from easier access to data. Vendors and start-ups are asking for your data to work with. Research partners want access as well. Off-shore development teams rely heavily on data sharing for testing applications.
• With an increase in data privacy legislations and rulings, like Schrems II effectively prohibiting US-EU data sharing, such projects turn out to be impossible to pull off. An increasingly hostile cybersecurity environment further inhibits free data flows making organizations more reluctant than ever to share data .

The status quo in data sharing and data democratization

Everyone is talking about the importance of data-driven decisions, but in reality only a select few individuals in organizations actually have the data to make those decisions. Many times only privileged data scientists have full access to raw data. But it's not always easy for them either: often they need to request specific permissions to work with certain datasets.

Once data scientists or machine learning engineers venture into yet-undiscovered territories and ideas, they need to obtain new permissions. Sometimes that is even the case for performing new analyses on datasets they already worked with in the past! Depending on the organization these processes to gain permission can take weeks or more .

When it comes to external data sharing, organizations, especially those handling troves of sensitive data, like financial institutions, banks and insurance companies have two options: either to not share data externally at all, or to heavily rely on legacy data anonymization a pproaches. These approaches are known for their poor privacy protection and often poor data utility as well. Even worse, less mature organizations take unacceptable levels of risk by relying on simple forms of de-identification or sharing production data.

Better, faster and compliant ways of data access are already possible today with the right approach, yet most companies lack the awareness of: synthetic data.

The data democratization solution  

Data is increasingly treated as a product, even and especially within the walls of organizations. Data should be proactively served in a cross-departmental fashion, flowing freely between different lines of business and even subsidiaries located in different countries or continents.

The much-coveted concept of the data mesh remains hard to attain for highly regulated industries without the necessary privacy-enhancing technologies . And there is one privacy-enhancing technology, that stands out: synthetic data.  It is revolutionizing data anonymization and data-sharing processes and making true data democratization an everyday reality. 

In practical terms, the use of synthetic data significantly simplifies the implementation of data democratization within an organization, especially in sectors subject to stringent regulatory guidelines, such as healthcare, banking, and government .

While traditional data-sharing methods often require lengthy approval processes and complex legal frameworks to ensure privacy and compliance, synthetic data can bypass these hurdles. This is because synthetic data retains the useful characteristics of the original dataset for analysis, learning, or decision-making, but doesn't carry the personal or sensitive information that would trigger privacy concerns.

Therefore, synthetic data can be shared more freely across various departments, business units, or even between different companies in a conglomerate, without necessitating exhaustive privacy impact assessments or risking regulatory fines .

This not only speeds up decision-making but also fosters a more collaborative and innovative work environment. With synthetic data, the aspirational concept of a data mesh—a decentralized, domain-oriented ownership model for data architecture—becomes not just achievable but operationally efficient, even in the most regulated industries.

See a concrete example of how synthetic data can be shared within an Databricks environment in the following video.

Data democratization best practices

More and more companies pivot to a proactive data approach . These innovators create internal - or in some cases, external data exchange platforms - to facilitate innovation and data-forward thinking across the organization and beyond.

Synthetic data sandboxes are populated with curated and maintained synthetic versions of business-critical datasets. Access to synthetic data assets can be broadly and quickly provided. Citizen data scientists can freely use synthetic data sandboxes, accelerating innovation and compliance. This helps to unlock customer data for a wide variety of further use cases, such as: 

• advanced analytics, AI and machine learning model training, 
• test data for software development.

McKinsey estimates that privacy-safe data sharing could generate almost $3 trillion annual economic value . And synthetic data generators are the technology to make this a reality.

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Blog > Data Quality > Making the Business Case for Data Democratization

Making the Business Case for Data Democratization

The goal of data democratization is to enable free flow of information that powers business agility – anybody can use data at any time to make decisions without barriers to data access or understanding. When data is made widely available to people who are committed to a common purpose, amazing things can happen. Search and rescue teams have realized the benefits of sharing aerial images with the public  to assist in finding lost hikers. Groups like Safe Kids International are  crowdsourcing the process of locating missing children . The medical community has long understood the benefits of sharing data, and has accelerated those efforts  to help combat the COVID-19 pandemic .

Of course, not all information sharing is driven by a pre-determined purpose. Very often, individuals and organizations create value from data in entirely unexpected ways. Governments around the world share census data that can be used by business and non-profit organizations. The US National Climatic Data Center provides data that serves a broad community of climate scientists. Even private companies have established data sharing platforms with an eye toward providing benefits to the wider community.

Many enterprises recognize the benefits of making data more broadly available within their organizations. This process of data democratization means that people throughout the business can access a larger data pool and analytics toolset. They can ask questions and get meaningful data-driven answers. With data democratization, the availability of data and associated analysis tools extends far beyond the limited group of experts who have a data science background.

Why data democratization matters

First and foremost, data democratization is about empowering employees to access the data that informs better business decisions. When access to data is limited to a select few, it limits an organization’s ability to ask questions, elicit insights from the data, and apply those insights to the creation of business value.

In addition to driving good business decisions, data democratization also provides for a better customer experience. In this age of the omnichannel, consumers expect businesses to have a complete picture of their past purchases, touch points with company personnel, and even their demographics. If their experience with a company seems disjointed, many are prone to take their business elsewhere.

Read our eBook

Managing Risk & Compliance in the Age of Data Democratization

Organizations are increasingly concerned with how data gets used, which works against the idea of democratizing the data. Having a proper data governance process, particularly one that is flexible, is crucial to the success of any kind of data democratization process. This eBook describes a new approach to achieve the goal of making the data accessible within the organization while ensuring that proper governance is in place.

Data democracy: Why now?

Data democratization has become a hot topic lately; all the stars seem to be in alignment. With the advent of location-aware mobile devices, IoT sensors, digital marketing automation, and ever-increasing volumes of unstructured data, there is so much more information available to be analyzed. Stuart MacDonald, former CMO of Expedia and Freshbooks, puts it this way: “Either you’re analytical and data-driven, or you go by what you think works. People who go by gut are wrong.”

Combine that with advances in technology such as cloud storage and scalable server capacity, AI and machine learning, and improved integration. Then add self-service business intelligence tools that are accessible to virtually anyone. The net result has been a rapid advancement of analytical capabilities, capacity, and usability.

Limitations and concerns

There are some caveats around data democratization that business leaders need to understand. First, there is the question of security. Every organization has measures in place to prevent employees and outside parties from unauthorized access to information. When people start talking about making company data more widely available, it naturally raises questions about how much is too much, who should have access, and what the appropriate level of granularity is for any given dataset.

The same kinds of issues arise around compliance. Companies are already struggling to comply with GDPR, CCPA/CCRA, HIPAA, and a host of other data privacy and security regulations. When considering data democratization, business leaders need to clearly understand downstream compliance implications.

Concerns may also arise around duplication of effort and unintentional misuse of data. In other words, if every department is doing its own work around data analysis, some of that work may be redundant. In the wrong hands, data may be misinterpreted, which can lead to faulty conclusions and poor business decisions. These concerns can be addressed by building transparency, governance, and quality control into the data democratization model, allowing departments to explore new ways to extract value from data while limiting duplication of effort and reining in misuse.

The path forward to data democratization

Data democratization begins with a clear understanding of its potential value across the organization. That, in turn, calls for a holistic and comprehensive strategy that broadens enterprise data access, rather than a piecemeal project-based approach. When analytics initiatives are treated as one-off projects, integration and data governance often suffer as they are tailored to the needs of a specific project on a case-by-case basis. This leads to a “just get it done” mentality that serves the need at hand but fails to address the longer term requirements of a broader audience. A better approach is to build a data governance structure that will outlast the immediate needs driven by any specific project.

Data stewards must also balance security and compliance with the expansion of data availability throughout the organization. Ultimately, the goal is to do both, but clearly security and compliance are not negotiable. Once again, good data governance provides the framework in which organizations can achieve both objectives.

Data democratization is ultimately driven by a three-part equation comprised of simplicity, scalability, and attention to data quality. Simplicity means that the producers of data have clearly understood targets for the structure and quality of the data they create, manage, and provision, while consumers of that information know how they can access it and what they can or cannot expect to learn from it.

Scalability is achieved by limiting the proliferation of integration points, streamlining the flow of information for timely results, and deploying smart strategies such as data federation where applicable.

Finally, data stewards must address the problem of “garbage in, garbage out.” Data quality demands that information be consistent, complete, accurate, and timely. As the volume of information managed by today’s enterprises continues to grow, data quality demands even greater attention. For the end-user who may be unaware of anomalies in the data, there is a substantial risk that poor data quality will lead to inaccurate conclusions and poor business decisions.

Precisely is a global leader in data management, data quality, enrichment, and integration; we offer best-in-class tools and expertise to help companies achieve excellence in enterprise integration and data management.

To learn more about how your organization can get data democratization right in 2021, download our e-book Managing Risk & Compliance in the Age of Data Democratization .

Managing Risk & Compliance in the Age of Data Democratization

Learn how your organization can get data democratization right in 2021.

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5 Best Practices to Reap the Benefits of Data Democratization

In the digital age, the success, growth, and maximization of opportunities of an organization depend on insights gained from data. Analytics and business intelligence (ABI) tools enable organizations to drive greater meaning from their data to protect revenue streams, minimize risk, adapt to rapidly changing business environments, and better serve their customers.  In many cases, […]

In the digital age, the success, growth, and maximization of opportunities of an organization depend on insights gained from data. Analytics and business intelligence (ABI) tools enable organizations to drive greater meaning from their data to protect revenue streams, minimize risk, adapt to rapidly changing business environments, and better serve their customers. 

What Is Data Democratization?

Data democratization is the process by which data is made accessible to all employees in an organization, rather than only to a team of specially trained  data scientists . It provides everyone in the organization with the opportunity to “own” their role’s relevant data, analyzing trends and opportunities, enabling faster decision-making, and optimizing outcomes without waiting for IT teams with limited bandwidth to process queries and pull reports. In short, data democratization allows nontechnical users to avoid depending on other teams for data-driven insights. 

Data democratization can also significantly improve the customer experience – an essential component to the success of an organization. For example, marketing departments can analyze the success of past strategies to provide tailor-made offers and marketing campaigns to attract customers, improving sales and overall revenue. Product designers and customer support teams can correlate customer feedback data in order to improve their products, services, and interactions with customers.

Other benefits of data democratization include:

• Increased collaboration between teams
• A strong sense of ownership and empowerment for employees
• Greater productivity and more efficient workflows
• More accurate KPIs and goals, with more buy-in from stakeholders
• Increased trust and confidence in the data and the intelligence it fuels

Best Practices in a Data Democracy

Data democratization can be part of an organization’s larger digital modernization strategy, but it’s also a worthwhile goal for those just embarking on digital initiatives. Data democratization can lay the foundation for other, broader, loftier ambitions.

Taking data that was only in the hands of a few and giving it to the hands of many can seem overwhelming, but by taking strategic incremental steps and following a set of best practices, it’s completely attainable for organizations of any size. The following list can help ensure your organization (and employees) reap the many benefits of data democratization.

1. Start by establishing a culture of data literacy.   Data literacy  is the ability to gather, read, analyze, and extract insights from data. Fostering data literacy in an organization motivates and empowers all employees to incorporate data analytics into their day-to-day operations, using the extracted information to make decisions that foster the success of their initiatives. That empowerment is an essential ingredient in a data democracy. 

Further, Data literacy should be taught top-down, meaning leaders must talk to their teams about the value of their organization’s data – why it exists, where it comes from, and how it’s used.

2. Embrace self-service analytics tools.  Providing access to data is not enough for a nontechnical employee to develop game-changing insights. You must ensure the data is easy to find, retrieve, comprehend, and analyze.  Self-service ABI  software helps display data in readable, user-friendly formats, which helps the end user find patterns, trends, and outliers. 

Further, these applications often have robust data visualization tools and drag-and-drop modules that allow for nontechnical users to manipulate and “play” with data, making those patterns and trends evermore discernable (and visually pleasing to boot). Self-service ABI tools enable decision-makers, and those that support them, to devise market strategies that help organizations stay a step ahead of the competition.

3. Company-wide training and education.  Although ABI tools get more user-friendly with every release, very few employees can jump in and teach themselves how to work proficiently within them. Organizations must provide comprehensive training to their employees to guarantee that new users will be comfortable with using these tools. With encouragement and opportunities to practice, that comfort will turn into confidence, and you’ll soon have employees making empowered, informed, data-driven decisions.

It’s not just about the tools though. Training end users in the core concepts of Data Management will increase their efficiency, accuracy, and confidence. With data democratization, users have more freedom – but they also have more responsibility. Knowing how the data is entered, processed, managed, and stored helps to ensure strategic, thoughtful work as well as a reduction in mistakes.

Consider creating a user manual containing best practices, policies, and procedures for business intelligence tools to help your organization and employees understand the available resources, where to find them, and how to use them. Not only will a user manual guarantee that everyone is following the same protocols, it can also promote independent learning (another confidence builder).

4. Implement user permissions and create policies for data accessibility.  As we’ve established, a successful data democratization model means that data must be accessible to everyone in an organization. When widening access to other teams, however, you must create and deploy policies to ensure the continued credibility and quality of data. Policies such as the authentication, authorization, and documentation for modifying or erasing data will improve the quality and impact of the insights derived by nontechnical users.

Instituting different access levels of user permissions based on the needs, roles, and skill levels of individual users ensures that the right people access and comprehend the right data. It’s important to make sure that users access data that is relevant to their department and position. Not only does this promote credibility among end users, but it limits unwanted manipulation. 

5. Ensure high-quality data.  Maintaining data quality plays a vital role in a company’s growth. There’s an adage that business intelligence is only as good as the quality of data informing it. The same is true for a data democracy – organizations will see the biggest benefit when their employees are working with high-quality data. Insights extracted from flawed data will lead to flawed decisions. Data that is stale, inaccurate, or incomplete can have consequences that not only negatively affect business decisions, but also undermine the trustworthiness of the data analysis and the confidence of the nontechnical users in their own work. 

Create rigorous standards to evaluate data for the five main criteria of quality: completeness, accuracy, consistency, timeliness, and integrity. Assign tasks like data formatting, data cleaning, and processing unstructured data to non-technical employees. If you educate your nontechnical users about data quality management, they can act as a safety net for your existing quality procedures, spotting inconsistencies and errors and raising the alarm (or better yet, fixing it themselves). 

Data is everywhere and it impacts every aspect of business. It makes sense that people from roles that haven’t historically interacted with data would begin to do so. In a data democracy, anyone can (and should!) access, interpret, and act on the intelligence they glean from their role’s relevant data. When fueled by data, even little decisions and insights can impact your organization in big ways.

After all, according to  Bernard Marr , author of “Big Data in Practice,” “Data democratization is an evolution where each small win, when non-technical users gain insight because of accessing the data, adds up to ultimately prove the merits of data democratization.”       

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Winfried Ruigrok

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

This is the latest in an FT series of mini case studies on business dilemmas, for exploration in the classroom and beyond. Read the argument and then consider the questions raised in the box below

Across the western world, big pay rises for chief executives have triggered shareholder dissent.

In May, aerospace group Boeing’s outgoing chief executive David Calhoun was awarded a pay rise of 45 per cent to $32.8mn despite shareholder opposition, following a series of recent incidents and accidents .

In March, the board of pharma giant AstraZeneca proposed to pay chief executive Pascal Soriot £18.7mn. Two proxy advisers called the package “ excessive ”, but one major shareholder argued Soriot was “ massively underpaid ” and the package was approved. Also in March, a proposed increase to the fixed salary part of Banco Santander executive chair Ana Bótin’s package drew fire from adviser ISS.

These debates about executive pay, on both sides of the Atlantic, raise questions about the checks and balances on remuneration.

ISS research found that chief executive officers’ pay went up by 9 per cent in the US in the first part of 2024, even when company performance went down. And, in response to a widening pay gap between US CEOs and their European counterparts, many FTSE 100 companies have also proposed significant pay rises this year.

To retain senior executives, the chair of UK-based medical devices maker Smith & Nephew argued it was necessary to raise pay for US executives working at “Brilo” companies: “ British in listing only ”. The head of the London Stock Exchange Group even called on investors to support higher executive pay , to prevent UK-based companies that generate only a “ fraction of their revenue in the UK ” relocating to the US.

Research on the effects of CEO pay on performance is extensive but many questions remain. Some work suggests that long-term stock options most effectively align incentives between shareholders and executives, and that large differences between senior and junior employees may be associated with higher long-term profitability. Other studies warn that high pay and large differentials may undermine the extrinsic motivation of top executives and hurt employee morale.

Executive pay is subject to a company’s governance. In line with the OECD’s principles of corporate governance , the board of directors establishes a remuneration committee, which proposes the components and level of the CEO’s and executive team’s remuneration. Ultimately, shareholders vote on this proposal at the company’s annual general meeting.

Occasionally, a board of directors is criticised for not having done its work properly. In January, the Delaware Court of Chancery turned down a $55.8bn pay deal proposed by the Tesla board for Elon Musk. The judge said the board behaved “like supine servants of an overweening master” and the chair’s objectivity had been compromised by “ life-changing ” sums of money she received when selling Tesla shares worth $280mn in 2021 and 2022. Musk replied that Tesla should move its headquarters from Delaware to Texas.

In theory, when the board fails, shareholder democracy should kick in. But it is rare for an AGM to vote down a remuneration package. One exception was in May 2023, when Unilever shareholders rejected a base salary increase for Hein Schumacher, the incoming CEO.

Sometimes, a large minority will vote against a pay proposal, as happened with the €36.5mn package put forward for carmaker Stellantis’ CEO, Carlos Tavares , in April. However, while such signs of dissent may be embarrassing, they rarely change the outcome.

There are concerns, therefore, that shareholder democracy is not functioning properly.

One explanation for this is that an increasing percentage of shares is owned by passive investors such as BlackRock, Vanguard and State Street. They act on behalf of other financial actors, such as pension funds, but rarely voice opinions on CEO pay. In 2020, BlackRock, the world’s largest passive investor, announced that, by the year-end, “all active portfolios and advisory strategies will be fully ESG integrated” — raising hopes among activists that executive pay would be linked to environment, social and governance standards. But the recent anti-ESG backlash has left some boards uncertain if, and how, to link remuneration to sustainability goals .

A second explanation, as at AstraZeneca and Banco Santander, is that proxy advisers play a growing role. Many institutional investors delegate their voting rights to these specialists. The two largest of them — ISS and Glass Lewis — control most of the proxy advisory market and state opinions on a growing variety of issues . As a result, board members increasingly complain about the influence on pay that these advisers have.

To many critics, then, shareholder democracy is failing in arbitrating on fair executive pay.

Questions for discussion

In your view, has CEO pay become excessive?

Should European CEO pay follow the levels set by US companies?

How credible is the risk that European companies will move their head office to another state or country? How damaging would this be to the original state or country?

How do you evaluate the growing role of passive investors in a corporate governance context?

Have proxy advisers become too powerful?

Should executive pay be based more on ESG criteria?

In your opinion, is shareholder democracy failing us when it comes to executive pay? Why (not)? If so, what should be done to improve it?

Should executive pay be capped? What would be the benefits? What would be the cost?

Read more FT ‘instant caselets’ at ft.com/business-school

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Human Subjects Office

Medical terms in lay language.

Please use these descriptions in place of medical jargon in consent documents, recruitment materials and other study documents. Note: These terms are not the only acceptable plain language alternatives for these vocabulary words.

This glossary of terms is derived from a list copyrighted by the University of Kentucky, Office of Research Integrity (1990).

For clinical research-specific definitions, see also the Clinical Research Glossary developed by the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard  and the Clinical Data Interchange Standards Consortium (CDISC) .

Alternative Lay Language for Medical Terms for use in Informed Consent Documents

A   B   C   D   E   F   G   H   I  J  K   L   M   N   O   P   Q   R   S   T   U   V   W  X  Y  Z

ABDOMEN/ABDOMINAL body cavity below diaphragm that contains stomach, intestines, liver and other organs ABSORB take up fluids, take in ACIDOSIS condition when blood contains more acid than normal ACUITY clearness, keenness, esp. of vision and airways ACUTE new, recent, sudden, urgent ADENOPATHY swollen lymph nodes (glands) ADJUVANT helpful, assisting, aiding, supportive ADJUVANT TREATMENT added treatment (usually to a standard treatment) ANTIBIOTIC drug that kills bacteria and other germs ANTIMICROBIAL drug that kills bacteria and other germs ANTIRETROVIRAL drug that works against the growth of certain viruses ADVERSE EFFECT side effect, bad reaction, unwanted response ALLERGIC REACTION rash, hives, swelling, trouble breathing AMBULATE/AMBULATION/AMBULATORY walk, able to walk ANAPHYLAXIS serious, potentially life-threatening allergic reaction ANEMIA decreased red blood cells; low red cell blood count ANESTHETIC a drug or agent used to decrease the feeling of pain, or eliminate the feeling of pain by putting you to sleep ANGINA pain resulting from not enough blood flowing to the heart ANGINA PECTORIS pain resulting from not enough blood flowing to the heart ANOREXIA disorder in which person will not eat; lack of appetite ANTECUBITAL related to the inner side of the forearm ANTIBODY protein made in the body in response to foreign substance ANTICONVULSANT drug used to prevent seizures ANTILIPEMIC a drug that lowers fat levels in the blood ANTITUSSIVE a drug used to relieve coughing ARRHYTHMIA abnormal heartbeat; any change from the normal heartbeat ASPIRATION fluid entering the lungs, such as after vomiting ASSAY lab test ASSESS to learn about, measure, evaluate, look at ASTHMA lung disease associated with tightening of air passages, making breathing difficult ASYMPTOMATIC without symptoms AXILLA armpit

BENIGN not malignant, without serious consequences BID twice a day BINDING/BOUND carried by, to make stick together, transported BIOAVAILABILITY the extent to which a drug or other substance becomes available to the body BLOOD PROFILE series of blood tests BOLUS a large amount given all at once BONE MASS the amount of calcium and other minerals in a given amount of bone BRADYARRHYTHMIAS slow, irregular heartbeats BRADYCARDIA slow heartbeat BRONCHOSPASM breathing distress caused by narrowing of the airways

CARCINOGENIC cancer-causing CARCINOMA type of cancer CARDIAC related to the heart CARDIOVERSION return to normal heartbeat by electric shock CATHETER a tube for withdrawing or giving fluids CATHETER a tube placed near the spinal cord and used for anesthesia (indwelling epidural) during surgery CENTRAL NERVOUS SYSTEM (CNS) brain and spinal cord CEREBRAL TRAUMA damage to the brain CESSATION stopping CHD coronary heart disease CHEMOTHERAPY treatment of disease, usually cancer, by chemical agents CHRONIC continuing for a long time, ongoing CLINICAL pertaining to medical care CLINICAL TRIAL an experiment involving human subjects COMA unconscious state COMPLETE RESPONSE total disappearance of disease CONGENITAL present before birth CONJUNCTIVITIS redness and irritation of the thin membrane that covers the eye CONSOLIDATION PHASE treatment phase intended to make a remission permanent (follows induction phase) CONTROLLED TRIAL research study in which the experimental treatment or procedure is compared to a standard (control) treatment or procedure COOPERATIVE GROUP association of multiple institutions to perform clinical trials CORONARY related to the blood vessels that supply the heart, or to the heart itself CT SCAN (CAT) computerized series of x-rays (computerized tomography) CULTURE test for infection, or for organisms that could cause infection CUMULATIVE added together from the beginning CUTANEOUS relating to the skin CVA stroke (cerebrovascular accident)

DERMATOLOGIC pertaining to the skin DIASTOLIC lower number in a blood pressure reading DISTAL toward the end, away from the center of the body DIURETIC "water pill" or drug that causes increase in urination DOPPLER device using sound waves to diagnose or test DOUBLE BLIND study in which neither investigators nor subjects know what drug or treatment the subject is receiving DYSFUNCTION state of improper function DYSPLASIA abnormal cells

ECHOCARDIOGRAM sound wave test of the heart EDEMA excess fluid collecting in tissue EEG electric brain wave tracing (electroencephalogram) EFFICACY effectiveness ELECTROCARDIOGRAM electrical tracing of the heartbeat (ECG or EKG) ELECTROLYTE IMBALANCE an imbalance of minerals in the blood EMESIS vomiting EMPIRIC based on experience ENDOSCOPIC EXAMINATION viewing an  internal part of the body with a lighted tube  ENTERAL by way of the intestines EPIDURAL outside the spinal cord ERADICATE get rid of (such as disease) Page 2 of 7 EVALUATED, ASSESSED examined for a medical condition EXPEDITED REVIEW rapid review of a protocol by the IRB Chair without full committee approval, permitted with certain low-risk research studies EXTERNAL outside the body EXTRAVASATE to leak outside of a planned area, such as out of a blood vessel

FDA U.S. Food and Drug Administration, the branch of federal government that approves new drugs FIBROUS having many fibers, such as scar tissue FIBRILLATION irregular beat of the heart or other muscle

GENERAL ANESTHESIA pain prevention by giving drugs to cause loss of consciousness, as during surgery GESTATIONAL pertaining to pregnancy

HEMATOCRIT amount of red blood cells in the blood HEMATOMA a bruise, a black and blue mark HEMODYNAMIC MEASURING blood flow HEMOLYSIS breakdown in red blood cells HEPARIN LOCK needle placed in the arm with blood thinner to keep the blood from clotting HEPATOMA cancer or tumor of the liver HERITABLE DISEASE can be transmitted to one’s offspring, resulting in damage to future children HISTOPATHOLOGIC pertaining to the disease status of body tissues or cells HOLTER MONITOR a portable machine for recording heart beats HYPERCALCEMIA high blood calcium level HYPERKALEMIA high blood potassium level HYPERNATREMIA high blood sodium level HYPERTENSION high blood pressure HYPOCALCEMIA low blood calcium level HYPOKALEMIA low blood potassium level HYPONATREMIA low blood sodium level HYPOTENSION low blood pressure HYPOXEMIA a decrease of oxygen in the blood HYPOXIA a decrease of oxygen reaching body tissues HYSTERECTOMY surgical removal of the uterus, ovaries (female sex glands), or both uterus and ovaries

IATROGENIC caused by a physician or by treatment IDE investigational device exemption, the license to test an unapproved new medical device IDIOPATHIC of unknown cause IMMUNITY defense against, protection from IMMUNOGLOBIN a protein that makes antibodies IMMUNOSUPPRESSIVE drug which works against the body's immune (protective) response, often used in transplantation and diseases caused by immune system malfunction IMMUNOTHERAPY giving of drugs to help the body's immune (protective) system; usually used to destroy cancer cells IMPAIRED FUNCTION abnormal function IMPLANTED placed in the body IND investigational new drug, the license to test an unapproved new drug INDUCTION PHASE beginning phase or stage of a treatment INDURATION hardening INDWELLING remaining in a given location, such as a catheter INFARCT death of tissue due to lack of blood supply INFECTIOUS DISEASE transmitted from one person to the next INFLAMMATION swelling that is generally painful, red, and warm INFUSION slow injection of a substance into the body, usually into the blood by means of a catheter INGESTION eating; taking by mouth INTERFERON drug which acts against viruses; antiviral agent INTERMITTENT occurring (regularly or irregularly) between two time points; repeatedly stopping, then starting again INTERNAL within the body INTERIOR inside of the body INTRAMUSCULAR into the muscle; within the muscle INTRAPERITONEAL into the abdominal cavity INTRATHECAL into the spinal fluid INTRAVENOUS (IV) through the vein INTRAVESICAL in the bladder INTUBATE the placement of a tube into the airway INVASIVE PROCEDURE puncturing, opening, or cutting the skin INVESTIGATIONAL NEW DRUG (IND) a new drug that has not been approved by the FDA INVESTIGATIONAL METHOD a treatment method which has not been proven to be beneficial or has not been accepted as standard care ISCHEMIA decreased oxygen in a tissue (usually because of decreased blood flow)

LAPAROTOMY surgical procedure in which an incision is made in the abdominal wall to enable a doctor to look at the organs inside LESION wound or injury; a diseased patch of skin LETHARGY sleepiness, tiredness LEUKOPENIA low white blood cell count LIPID fat LIPID CONTENT fat content in the blood LIPID PROFILE (PANEL) fat and cholesterol levels in the blood LOCAL ANESTHESIA creation of insensitivity to pain in a small, local area of the body, usually by injection of numbing drugs LOCALIZED restricted to one area, limited to one area LUMEN the cavity of an organ or tube (e.g., blood vessel) LYMPHANGIOGRAPHY an x-ray of the lymph nodes or tissues after injecting dye into lymph vessels (e.g., in feet) LYMPHOCYTE a type of white blood cell important in immunity (protection) against infection LYMPHOMA a cancer of the lymph nodes (or tissues)

MALAISE a vague feeling of bodily discomfort, feeling badly MALFUNCTION condition in which something is not functioning properly MALIGNANCY cancer or other progressively enlarging and spreading tumor, usually fatal if not successfully treated MEDULLABLASTOMA a type of brain tumor MEGALOBLASTOSIS change in red blood cells METABOLIZE process of breaking down substances in the cells to obtain energy METASTASIS spread of cancer cells from one part of the body to another METRONIDAZOLE drug used to treat infections caused by parasites (invading organisms that take up living in the body) or other causes of anaerobic infection (not requiring oxygen to survive) MI myocardial infarction, heart attack MINIMAL slight MINIMIZE reduce as much as possible Page 4 of 7 MONITOR check on; keep track of; watch carefully MOBILITY ease of movement MORBIDITY undesired result or complication MORTALITY death MOTILITY the ability to move MRI magnetic resonance imaging, diagnostic pictures of the inside of the body, created using magnetic rather than x-ray energy MUCOSA, MUCOUS MEMBRANE moist lining of digestive, respiratory, reproductive, and urinary tracts MYALGIA muscle aches MYOCARDIAL pertaining to the heart muscle MYOCARDIAL INFARCTION heart attack

NASOGASTRIC TUBE placed in the nose, reaching to the stomach NCI the National Cancer Institute NECROSIS death of tissue NEOPLASIA/NEOPLASM tumor, may be benign or malignant NEUROBLASTOMA a cancer of nerve tissue NEUROLOGICAL pertaining to the nervous system NEUTROPENIA decrease in the main part of the white blood cells NIH the National Institutes of Health NONINVASIVE not breaking, cutting, or entering the skin NOSOCOMIAL acquired in the hospital

OCCLUSION closing; blockage; obstruction ONCOLOGY the study of tumors or cancer OPHTHALMIC pertaining to the eye OPTIMAL best, most favorable or desirable ORAL ADMINISTRATION by mouth ORTHOPEDIC pertaining to the bones OSTEOPETROSIS rare bone disorder characterized by dense bone OSTEOPOROSIS softening of the bones OVARIES female sex glands

PARENTERAL given by injection PATENCY condition of being open PATHOGENESIS development of a disease or unhealthy condition PERCUTANEOUS through the skin PERIPHERAL not central PER OS (PO) by mouth PHARMACOKINETICS the study of the way the body absorbs, distributes, and gets rid of a drug PHASE I first phase of study of a new drug in humans to determine action, safety, and proper dosing PHASE II second phase of study of a new drug in humans, intended to gather information about safety and effectiveness of the drug for certain uses PHASE III large-scale studies to confirm and expand information on safety and effectiveness of new drug for certain uses, and to study common side effects PHASE IV studies done after the drug is approved by the FDA, especially to compare it to standard care or to try it for new uses PHLEBITIS irritation or inflammation of the vein PLACEBO an inactive substance; a pill/liquid that contains no medicine PLACEBO EFFECT improvement seen with giving subjects a placebo, though it contains no active drug/treatment PLATELETS small particles in the blood that help with clotting POTENTIAL possible POTENTIATE increase or multiply the effect of a drug or toxin (poison) by giving another drug or toxin at the same time (sometimes an unintentional result) POTENTIATOR an agent that helps another agent work better PRENATAL before birth PROPHYLAXIS a drug given to prevent disease or infection PER OS (PO) by mouth PRN as needed PROGNOSIS outlook, probable outcomes PRONE lying on the stomach PROSPECTIVE STUDY following patients forward in time PROSTHESIS artificial part, most often limbs, such as arms or legs PROTOCOL plan of study PROXIMAL closer to the center of the body, away from the end PULMONARY pertaining to the lungs

QD every day; daily QID four times a day

RADIATION THERAPY x-ray or cobalt treatment RANDOM by chance (like the flip of a coin) RANDOMIZATION chance selection RBC red blood cell RECOMBINANT formation of new combinations of genes RECONSTITUTION putting back together the original parts or elements RECUR happen again REFRACTORY not responding to treatment REGENERATION re-growth of a structure or of lost tissue REGIMEN pattern of giving treatment RELAPSE the return of a disease REMISSION disappearance of evidence of cancer or other disease RENAL pertaining to the kidneys REPLICABLE possible to duplicate RESECT remove or cut out surgically RETROSPECTIVE STUDY looking back over past experience

SARCOMA a type of cancer SEDATIVE a drug to calm or make less anxious SEMINOMA a type of testicular cancer (found in the male sex glands) SEQUENTIALLY in a row, in order SOMNOLENCE sleepiness SPIROMETER an instrument to measure the amount of air taken into and exhaled from the lungs STAGING an evaluation of the extent of the disease STANDARD OF CARE a treatment plan that the majority of the medical community would accept as appropriate STENOSIS narrowing of a duct, tube, or one of the blood vessels in the heart STOMATITIS mouth sores, inflammation of the mouth STRATIFY arrange in groups for analysis of results (e.g., stratify by age, sex, etc.) STUPOR stunned state in which it is difficult to get a response or the attention of the subject SUBCLAVIAN under the collarbone SUBCUTANEOUS under the skin SUPINE lying on the back SUPPORTIVE CARE general medical care aimed at symptoms, not intended to improve or cure underlying disease SYMPTOMATIC having symptoms SYNDROME a condition characterized by a set of symptoms SYSTOLIC top number in blood pressure; pressure during active contraction of the heart

TERATOGENIC capable of causing malformations in a fetus (developing baby still inside the mother’s body) TESTES/TESTICLES male sex glands THROMBOSIS clotting THROMBUS blood clot TID three times a day TITRATION a method for deciding on the strength of a drug or solution; gradually increasing the dose T-LYMPHOCYTES type of white blood cells TOPICAL on the surface TOPICAL ANESTHETIC applied to a certain area of the skin and reducing pain only in the area to which applied TOXICITY side effects or undesirable effects of a drug or treatment TRANSDERMAL through the skin TRANSIENTLY temporarily TRAUMA injury; wound TREADMILL walking machine used to test heart function

UPTAKE absorbing and taking in of a substance by living tissue

VALVULOPLASTY plastic repair of a valve, especially a heart valve VARICES enlarged veins VASOSPASM narrowing of the blood vessels VECTOR a carrier that can transmit disease-causing microorganisms (germs and viruses) VENIPUNCTURE needle stick, blood draw, entering the skin with a needle VERTICAL TRANSMISSION spread of disease

WBC white blood cell

Government innovation

Governments today must be able to adapt to changing environments, work in different ways, and find solutions to complex challenges. OECD work on public sector innovation looks at how governments can use novel tools and approaches to improve practices, achieve efficiencies and produce better policy results.

• Global Trends in Government Innovation
• Tackling Policy Challenges Through Public Sector Innovation

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Key messages, innovation is a strategic function that must be integrated into broader public sector governance..

Innovation rarely happens by accident. Governments can increase innovation in the public sector through deliberate efforts using many different levers, from investments in skills or technology, to applying new policymaking methods or adapting existing processes. Our work helps governments assess their innovative capacity, providing practical and evidence-based steps to embed innovation in policymaking and administration. This means governments are better able to respond to changing environments and develop more impactful policies.

Behavioural science helps governments put people at the center of public policy.

Understanding cognitive biases, behavioural barriers, and social norms  is essential for the development of impactful policies and public uptake. Behavioural science is an interdisciplinary approach, providing insights that enable policymakers to design more effective and targeted policies that reflect actual human behaviour and decision-making. Our work encompasses research on context-specific behavioural drivers and barriers to support countries in the use of behavioural science from policy design to implementation and evaluation. Through the OECD Network of Behavioural Science Experts in Government, we further foster the exchange of best behavioural science practices and mutual learning.

Governments must anticipate, understand and prepare for the future as it emerges.

The nature of policy issues that governments are confronted by is volatile, uncertain, complex and often ambiguous. Governments need to consider a variety of scenarios and act upon them in real time. This requires a new approach to policymaking, one that is future and action oriented, involves an innovation function and anticipates the changing environment. By governing with anticipation and innovation, governments can prepare for what’s coming next. They can identify, test, and implement innovative solutions to benefit from future opportunities while reducing risk and enhancing resilience.

Innovation in public services unlocks efficiency, responsiveness and citizen satisfaction.

Innovating and digitalising public services can bring many benefits, including improving the quality, efficiency and effectiveness of services, enhancing equitable access and reducing administrative burdens. While it holds tremendous benefits for supporting the overall well-being and satisfaction of citizens and public trust in institutions, governments must ensure high standards of transparency and ethics, particularly when employing the use of data and artificial intelligence to improve or deliver public services. Our work is building towards an OECD Recommendation on the design of government services to effectively improve people's experiences including through life events and the development of more effective and equitable services.  

The public has a lack of confidence in public agencies adopting innovative ideas.

Governments must do better to respond to citizens’ concerns. Just fewer than one in four (38%, on average across OECD countries), feel that a public agency would be likely to adopt an innovative idea to improve a public service. Enhancing innovation capacity can strengthen resilience, responsiveness and trust in public institutions.

Confidence in governments’ adoption of innovative ideas is directly related to trust in civil servants.

People who say they are confident about innovation in a public office are more likely to trust civil servants. On average across OECD countries, the share of people who trust the civil service is equal to 70% among those who are confident about public sector innovation. This trust value is more than two times larger than among those who say that the public sector would not adopt innovative ideas.

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• Anticipatory governance In an era characterised by rapid technological advances, environmental shifts, changing demographics, geopolitical tensions, and evolving societal needs, traditional governance models are increasingly under pressure. Governments worldwide are seeking ways to not only respond to present challenges but also to anticipate and shape future possibilities. Anticipatory Innovation Governance is a proactive approach that integrates foresight, innovation, and continuous learning into the heart of public governance. Learn more
• Behavioural science Governments around the world are increasingly using behavioural science as a lens to better understand how behaviours and social context influence policy outcomes. At the OECD, we research context-specific behavioural drivers and barriers, and support countries in the use of behavioural insights, from policy design to implementation and evaluation. Learn more
• Digital government Digital government explores and supports the development and implementation of digital government strategies that bring governments closer to citizens and businesses. It recognises that today’s technology is a strategic driver not only for improving public sector efficiency, but also for making policies more effective and governments more open, transparent, innovative, participatory and trustworthy. Learn more
• Innovative capacity of governments Governments must develop their capacity to adapt and change the way policies and services are designed and delivered if they want to implement ambitious reform agendas, meet climate targets and respond to global crises. Without intentional efforts, innovation is left to chance, fuelled sporadically by circumstance and crises. Our work helps governments assess and improve their innovative capacity, providing practical and evidence-based steps to embed innovation in policymaking and administration. Learn more
• Innovative public participation Citizens must have a say in the decisions that affect them. Inclusive and impactful participation not only enriches the policymaking process by incorporating diverse views and harnessing collective knowledge, but also strengthens public understanding of outcomes, promotes policy uptake, and reinforces trust in public institutions. It is essential to institutionalise participatory and deliberative processes and better articulate them with representative democracies. Learn more

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A review of impedance spectroscopy technique: applications, modelling, and case study of relative humidity sensors development.

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da Silva, G.M.G.; Faia, P.M.; Mendes, S.R.; Araújo, E.S. A Review of Impedance Spectroscopy Technique: Applications, Modelling, and Case Study of Relative Humidity Sensors Development. Appl. Sci. 2024 , 14 , 5754. https://doi.org/10.3390/app14135754

da Silva GMG, Faia PM, Mendes SR, Araújo ES. A Review of Impedance Spectroscopy Technique: Applications, Modelling, and Case Study of Relative Humidity Sensors Development. Applied Sciences . 2024; 14(13):5754. https://doi.org/10.3390/app14135754

da Silva, Georgenes M. G., Pedro M. Faia, Sofia R. Mendes, and Evando S. Araújo. 2024. "A Review of Impedance Spectroscopy Technique: Applications, Modelling, and Case Study of Relative Humidity Sensors Development" Applied Sciences 14, no. 13: 5754. https://doi.org/10.3390/app14135754

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