The Path to Data Democratization

Man and Woman Working on Same Laptop

The democratization of data refers to the process of making data more accessible, understandable and usable for a wider range of people, regardless of their technical expertise or background. The main goal of data democratization is to empower individuals and organizations to make data-driven decisions by giving them access to relevant information.

Key aspects of data democratization include:

  1. Data Accessibility: Ensuring that data is available to those who need it, typically through user-friendly platforms or tools, such as a data visualization software, self-service analytics or cloud-based storage solution.
  2. Data Literacy: Developing the necessary skills and knowledge for individuals to understand, analyze and interpret data effectively. This involves offering educational resources, training and support to help people become more data-literate.
  3. Data Governance: Implementing policies, processes and standards to manage and maintain data quality, privacy and security. This is crucial to ensure that democratized data is reliable, accurate and adheres to relevant regulations.
  4. Cross Collaboration: Encouraging the sharing of data, insights and expertise across teams and departments within organizations, as well as with external partners or stakeholders.

By democratizing data, organizations can foster a culture of data-driven decision-making, promote innovation, improve efficiency and ultimately gain a competitive advantage in today’s data-driven world.

How Do You Make It Happen? 

Implementing data democratization can require a significant shift in corporate culture, particularly in cases where data ownership is spread across different business functions. In such cases, managers may restrict employee access to data due to concerns about their ability to interpret and apply it accurately. This may lead to establishing internal data governance policies that limit access to executives, IT staff and data scientists.

This is where data literacy becomes important, as it goes hand in hand with data democratization. Providing full data access alone is not enough; education and training within the organization are essential for getting the most out of the data and achieving the desired outcomes.

So how do you truly actualize data democratization in your business? Meenal Iyer, VP of Data at Momentive AI, has a very clear plan for that.

 

Creating a Data Strategy to Actualize Data Democratization

Developing a successful data strategy requires a clear vision of the end goal or purpose that an organization wants to achieve within one year to eighteen months. This vision should be comprehensive and ambitious, considering every aspect of the investment and budget. Once the vision is set, the organization should begin developing an action plan, pulling on several critical levers to the strategy’s success.

Step 1: Technology as an Enabler

The first and most important lever is technology, which needs to be carefully selected to support the organization’s vision. However, technology alone cannot drive a data strategy. It is just an enabler and not enough to drive a data strategy. 

Step 2: Defining a Strategy 

The second lever is defining the organization’s data roadmap and strategy. It is important to determine the strategic priorities for the organization and identify the first steps to achieving those priorities. It is important to work with leadership to ensure that the data roadmap aligns with the organization’s overall goals. 

The analytic strategy is another critical lever to consider. The organization is most effective when business users and data analysts are empowered to be productive. This means providing self-service capabilities to business users, reducing their dependence on the data team. To achieve this, a semantic layer or business view layer should be built that provides easy access to data for business users. It is also important to build a center of excellence for the organization to provide technical guidance, training, upskilling, literacy and governance.

The focus should shift to empowering the team by cross-training or upskilling them to be effective in the new world. The team’s functions and time should be analyzed to determine how they can most effectively achieve the organization’s data strategy.

Step 3: Empowering the Team

The first step here is to understand the team’s current state, including how they allocate their time and how to make them more effective. The strategy should then focus on the organization’s processes, such as communication protocols, prioritization, product testing and experimentation. Metrics enablement is another crucial component of the strategy, as it involves creating success metrics, continually measuring progress and ensuring consistency in defining metrics across different departments.

Additionally, to promote the data strategy to the broader organization, a literacy plan is necessary, which involves making the team and other stakeholders data-literate and insights-driven. 

Step 4: Empower the Enterprise

The next step is to empower the enterprise by providing data as a service and building data and analytics products. This can involve using data science, machine learning and AI if appropriate for the organization’s needs.

Finally, the organization’s culture needs to be shifted towards being insights-driven and innovative, with data as the primary driver of decision-making. This requires a vision for the end state and a roadmap for the journey towards that vision, with clear objectives and action plans for the near term. 

Overall, developing a comprehensive data strategy involves empowering the team, promoting data literacy and insights-driven decision-making and building a culture of innovation and data-driven decision-making.

Main Challenges of Data Democratization

There are three main challenges when trying to work with data democratization: 

  1. Security and privacy
  2. Executive sponsorship
  3. Lack of adoption of C-level executives
  • Security and Privacy

Ensuring that the integrity of the data is maintained while also allowing end-users to access it can be a daunting task, but it is an important one. Data anonymization and tokenization are two approaches that can be used to maintain privacy while still providing access to end-users. It is the responsibility of data leaders to be mindful of access control and to manage data securely.

  • Executive Sponsorship

The second challenge is gaining executive sponsorship for the enterprise data strategy. If the strategy has not been effectively communicated and promoted throughout the organization, it can be difficult to achieve adoption once the platform is built. Data democratization involves educating the organization on the benefits of the strategy and why it is the right approach.

  • Lack of Adoption by C-level Executives

Another major challenge is the lack of buy-in from executives and managers. While upper-level executives may fully understand and support a new data strategy, managers and employees may view it as a threat to their job security and may resist change. They may argue that the current way of doing things has been working for a long time, and changing it could result in inefficiencies or uncertainties about how to spend their time.

To overcome this challenge, look for champions within the organization who can help to articulate the benefits of the new data strategy and rally support among their peers. These champions tend to be power users or experienced data analysts passionate about using data to improve their work. By showing them how the new strategy can help them achieve their goals more efficiently and effectively, they become enthusiastic advocates for the new approach and can help to sway others to support the initiative.

Finding champions is not always easy, but it is essential for success. Meenal Iyer found that identifying potential champions early on in the process and involving them in the planning and designing of the new strategy can be particularly effective. This helps to build trust and engagement and ensures that the champions are invested in the success of the project.

However, it’s important to note that champions can change positions or leave the company, which can cause setbacks in implementing the strategy. As a data leader, it’s crucial to continuously look for new champions and be ready to adapt the approach as needed.

Building a successful data strategy requires more than just executive sponsorship and top-down direction. It also requires grassroots support from the people using the data daily. By finding and empowering champions within the organization, data leaders can build a strong foundation for successful implementation and ensure that all embrace the new strategy.

Utilize the Center of Excellence (CoE)

Center of Excellence (CoEs) refers to a team of experts who act as the hub of knowledge, best practices and support in an organization or focus area. CoEs have a deep understanding of the technology being used across the enterprise and within their teams and are familiar with the various tools employed within the organization. They guide how to use these tools effectively and ensure the data is being used to its full potential. They also offer to coach and train to help teams become more productive. 

Centers of Excellence are used in various ways within organizations. Some organizations create CoEs within their business units to directly support their teams. 

For instance, if a team needs information on sales and is unsure how to access it from the data system, they can contact the CoE within the sales department to obtain the necessary insights. In this type of decentralized CoE, the sales team has direct access to the experts in the CoE, bypassing the need to go through the central data team.

In contrast, some organizations prefer to have CoEs reside within the data team itself, where they have access to all the knowledge necessary to provide support for the entire business. In this setup, if the data team is unsure about certain aspects of the business, they can reach out to the relevant teams to gather the necessary information and provide answers back to the teams.

Both types of CoEs can be effective, but their main function is to ensure that the data produced by the organization is validated and governed rather than just a haphazardly assembled dataset. They are responsible for ensuring the data is adopted and used correctly, per the organization’s objectives.

Conclusion

Data democratization isn’t as easy as it seems. But you have to find a way to get your strategy done and get to your vision of what you want in a data strategy itself. Sometimes, you have to do some out-of-the-box and creative thinking about how it will work for your organization, but the benefits of data democratization are well worth the cost. 

Data may be the new oil, but a well-crafted data strategy serves as the refinery that transforms it into valuable fuel. Discover how you can refine your data strategy and leverage data as a powerful asset.

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