What Is Data Democratization? Definition and Principles

Data democratization is much more than making data accessible. Learn what it takes to build a true data democracy in an organization.

November 8, 2024
Founder, astorik
Data democratization definition

Originally published on January 27, 2022

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Ask, “What is data democratization?” and many people will start talking about access to data.

However, just giving data access—whether as raw data in a or as beautiful visualizations inside a or a business intelligence tool—is certainly not data democratization.

So what is it?

Data democratization is the ongoing process of empowering everybody in an organization, irrespective of their technical know-how, to work with data comfortably, feel confident talking about it, and, as a result, .

An organization that truly wants to democratize data needs to embrace the following principles (also known as the trifecta of data democratization):

  1. Empower employees to feel comfortable asking data-related questions
  2. Provide to enable everybody to work with data
  3. Perceive the democratization of data as an ongoing process that might even require an organization-wide cultural shift

Data democracy vs. data transparency

Data democracy empowers teams to access and use data in their daily work. In contrast, data transparency focuses on building trust in data through visibility. Data transparency is an approach to data management that enables stakeholders to see where data comes from, how it’s used, and who has access to it.

Benefits of data democratization

Making data democratization a reality is a serious investment—educating employees, implementing tools, and managing change are not trivial undertakings. So, why do companies care so much about democratizing data?

Data democratization exists to solve data challenges

At its core, data democratization is all about people face daily. Due to the pace of change in the data landscape and people’s needs, even the best data teams struggle to meet the expectations of various stakeholders.

I spend a lot of time hanging out in communities and talking to non-data people—particularly product and growth professionals from across the globe working at companies of all sizes.

The most common data challenges that people share can be summed up as follows:

  • I don’t have access to the data I need
  • I can’t trust the data
  • I have access to data but lack the skills to find answers to questions
  • The analytics tools my company provides aren’t designed for product teams
  • Data experts at my company are too busy to help me

If one or more of the statements mentioned above are deemed true by your employees, it’s safe to assume that data democratization at your organization needs work.

Interestingly, these challenges map to the abovementioned principles (the trifecta of data democratization).



Democratization Principles

Data Challenges

Empower employees to feel comfortable asking data-related questions.

I don’t have access to the data I need. 

I can’t trust the data.

I have access to data but lack the skills to find answers to questions.

Provide the right tools to enable everybody to work with data. 

The analytics tools most companies provide aren’t designed for product teams. 

See the democratization of data as an ongoing process that might even require an organization-wide cultural shift. 

Data experts at my company are too busy to help me.

 

Data democratization principles

Data democratization starts with understanding the basics. Read to get started.

Other benefits of data democratization

In solving those data challenges, data democratization leads to:

  • Faster (and better) decision-making. When people can find answers quickly and independently, they don’t get blocked waiting for answers and can make data-driven decisions.
  • More innovation. Teams can run experiments and test new ideas, resulting in greater innovation.
  • Greater efficiency. By reducing dependency on specialized data teams for every query, you can lower your data processing and analysis costs or free up those data teams to work on higher-impact analysis.

Why data democratization can be difficult

A lot of organizations are sold on the idea of data democracy. But they hit hurdles along the way. Barriers to data democracy include:

  • A lack of trust in data. Not all data is equal. Teams might have access to a lot of data, but they will not trust their decisions if they believe that , organized, or accurate.
  • Data governance and security challenges. Organizations need to implement strong governance practices so that everyone has access to the data they need, but no one has access to data they shouldn’t. To meet regulatory requirements, it’s also important to have records of who accessed what data, when, and why.
  • Data silos. When data is stored in separate systems across teams, it becomes difficult to understand what is happening altogether. This makes it harder for teams to improve products and customer experiences. To overcome this challenge, it’s important to have robust systems for data sharing.

Organizations must address these challenges to realize the full benefits of data democratization. Let’s review the strategies for democratizing data.

How to get data democratization right

To solve challenges and avoid risks, stick to the three principles of data democratization.

Start by setting data literacy table stakes in your organization.

Data literacy should no longer be considered a luxury. Everybody should be given access to the resources they need to become as data-literate as they wish to be.

Understanding what data the company collects and might be enough for some. Others might find it worthwhile to go beyond that and discover why specific data is tracked, how it’s done, where it is stored, and in .

Data literacy solves one of the biggest bottlenecks in data democratization: access to data.

Defining data access

When someone says that they don’t have access to data, they can refer to raw data in a database, transformed data in a data warehouse, data in the form of visual dashboards, product usage data inside a product analytics tool, transactional data in a subscription analytics tool, demographic data in a customer engagement tool, data about marketing campaigns in a customer data platform, and so on. You get the picture.

Providing access becomes much less complicated when that person can specify where they wish to access what data. Also, if that person is given access to the right data in the right tools at the right time, it is far more likely that they will trust the data.

So the next time someone says they don’t have access to data and can’t specify where they want access to what data, you have a data literacy problem to solve.

Different shades of data literacy

Data literacy is not limited to knowing how to write SQL queries or how to analyze complex reports.

Every team needs some form of data to execute daily tasks or analyze their work's impact. However, different teams with different data needs require varying levels of data literacy.

Very different skills are required to implement data tracking, derive insights from data, and act upon those insights. Further, acting upon those insights by running data-led marketing campaigns requires a different skill set than that required to identify the right prospects to go after by looking at the same data inside a CRM.

Similarly, building predictive models and delivering personalized experiences in real time rely on and require different skills. The former requires training in data science, while the latter is a problem for data engineering to solve.

Data literacy, in some shape or form, has become a prerequisite for individuals to excel at their duties. Companies that invest in making data literacy accessible to their employees are sure to make their competitors play catch-up.

Now that we agree that data literacy is table stakes, the next principle in the trifecta of data democratization is enabling everybody to work with data by investing in the tools that enable them to do so.

2. Provide the right tools to enable everybody to work with data

Let’s look at how different teams typically work with data:

  • Marketing works with data to create engaging,
  • Growth works with data to
  • Product and Engineering work with data to build features that drive customer value and sunset the ones that do not
  • Support uses data to deliver faster resolution (by observing what a user has done or not done inside a product)
  • Customer Success works with data to deliver a better customer experience (by asking customers the right questions based on usage patterns)
  • Sales work with data to identify prospects who are likely to convert (e.g., by looking at the actions they have performed during a free trial)
  • Executives work with data to understand how the business is performing and where future investments should be made

Can a couple of tools really do all of the above?

This is just a very high-level overview of the most common ways teams work with data. It doesn’t even include the requirements of data teams, which need additional tools to ensure that the right data is made available in the right format in the right systems at the right time.

Product and Growth teams alone often use at least half a dozen tools to do their jobs well (I’m talking about best-in-class tools and not do-it-shabbily-but-do-it-all ones).

A craftsperson is only as good as their tools

Today, data literacy is a prerequisite for successful Product and Growth teams, as most tools rely on customer data to deliver on their core premise.

Whether a product analytics solution or tools to deliver contextual in-app messages, run lifecycle email campaigns, gather qualitative data, or run —accurate data being made available in these tools is the only way to derive value and justify the investment.

Depending on its size and , every company must invest in additional data tools—often a modern data stack—to cater to the entire organization's needs. Businesses that capture a lot of data often invest in:

  • A data warehouse like Snowflake, BigQuery, or Firebolt to make data available for analysis and activation
  • A business intelligence (BI) tool like Looker, Mode, or Superset that sits on top of the warehouse and enables self-service analytics
  • An ELT tool like Airbyte, Fivetran, or Meltano to move data from third-party tools (like the ones mentioned above) into the warehouse
  • A reverse ETL tool like Census, Hightouch, or Grouparoo to move modeled data back from the warehouse to third-party tools

Buying, implementing, and maintaining a modern data stack is not trivial, though—you need a data team or a dedicated data person to manage this ongoing process.

Investing in products that enable individuals to work with data efficiently to derive insights and make data-led decisions without relying on others is crucial. It makes everybody productive and keeps the team morale high.

Moreover, implementing best-in-class tools that do the job well is better than spending countless hours looking for the ideal tool or, even worse, deciding to build something that can easily be bought.

“Build versus buy” is a topic for another day, but I must say that whichever route you take, evaluate how your decision impacts your teams—particularly the effects on their day-to-day work and long-term goals.

It’s time to address the third principle:

3. Treat data democratization as an ongoing process that requires a cultural shift

I’d like to start by saying that a company's size and growth trajectory heavily impact the pace at which data democratization takes place. Building a data democracy is much easier in a company's early days since it’s easier to mold the culture that supports it.

Larger organizations are likely to face a slew of challenges, making data democratization appear to be a "data democracy.”

Considering that, the larger an organization, the sooner it should invest in data democratization. If your organization is gearing up to set up product analytics, .

So, why is data democratization an ongoing process?

Data democratization is a continuous process because it relies on data literacy, which is also ongoing. The world of data is experiencing unprecedented growth, and the rate at which tools and technologies evolve is fascinating. But this change is also hard to keep up with and a tad bit annoying for most people outside the data space, especially due to its impact on their work.

At the very least, everybody in an organization, regardless of their role, should be able to effortlessly get answers to their data-related questions.

Additionally, how various teams work with data—and to what extent—should become general knowledge within an organization. It should be easy for employees to know who has access to what types of data, where the data resides, and what the process is of getting access to the data or asking questions of that data.

—Airbnb’s home-bred data discovery solution that enables the entire organization to find and understand data assets in a self-serve fashion—is a fine example of how larger companies can democratize data by allocating dedicated resources to solving this mammoth problem. Projects like Dataportal certainly require ongoing investment, but the payoff seems to be worth the effort for a company the size of Airbnb.

Finally, what kind of a cultural shift are we talking about?

One of the data challenges mentioned above is, “Data experts at my company are too busy to help me.”

Data democratization needs a cultural shift that makes this challenge obsolete—a thing of the past in your organization:

  • Everybody who relies on data to excel at their job and meet their goals should become a data expert.
  • Everybody in the organization should feel confident talking about data and be equipped with the tools and the knowledge to work with data and get answers to their questions without dependencies.
  • Lastly, everybody in the organization should be given the opportunity to make meaningful contributions to data projects.

There is no proven one-size-fits-all approach to building a data democracy, but empowering people is a crucial step in that direction.

How data architecture affects data democracy

In traditional data architectures, data is stored in one central place and controlled by specialist data scientists. While this helps with efficient data storage and simplifies security, it often creates bottlenecks, making it difficult for non-technical teams to access data easily. To overcome these limitations, new approaches to data architecture, such as data mesh and data fabric, have emerged to support more decentralized and accessible data management.

Data mesh

Data mesh is an approach to data architecture that shifts data management to individual teams within an organization. Each team is responsible for maintaining data quality and ensuring their data is usable and accessible to other teams. In theory, decentralizing data in this way makes it more accessible across the organization.

Data fabric

Data fabric is the infrastructure that enables data mesh initiatives. It connects, integrates, and manages data across sources and environments using automation, metadata, and AI to create a unified data layer. Data fabric helps teams to easily find, trust, and use data, thus supporting data democracy.

Ready to democratize data access for your team? Read , or .

About the Author
Founder, astorik
Arpit is growing databeats (databeats.community), a B2B media company, whose mission is to beat the gap between data people and non-data people for good.

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