What Is Data Democratization? Definition and Principles

Originally published on January 27, 2022

Google “What is data democratization” and you’ll see the top results talking about “access to data” as the key to the democratization of data (unless you found this article, which is awesome).

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

So what is it?

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

An organization that truly wants to democratize data needs to embrace the following principles (referred to as the trifecta of data democratization throughout this guide):

• Empower employees to feel comfortable asking data-related questions
• Provide the right to enable everybody to work with data
• Perceive democratization of data as an ongoing process which might even require an organization-wide cultural shift

Before delving deeper into the above, allow me to digress.

Data democratization exists to solve data challenges

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

At its core, data democratization is all about that people face in their day-to-day. And 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 teams.

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 above-mentioned statements are deemed true by your employees, it is safe to assume that data democratization at your organization needs work.

What’s interesting is how these challenges map to the principles mentioned above (the trifecta of data democratization).

Let’s delve a little deeper into these data democratization principles.

How do you make employees feel comfortable asking data-related questions?

Start with making data literacy table stakes at your organization.

Data literacy should no longer be seen as a nice-to-have. Everybody should be given access to the resources they need in order to become as data-literate as they wish to.

For some, it might be enough to understand what data the company collects and . Others might find it worthwhile to go beyond and find out why certain data is tracked, how it’s done, where it is stored, and in .

In essence, data literacy solves one of the biggest bottlenecks in data democratization: access to data.

Access to data but what data and where?

Well, 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.

And when that person can specify where they wish to access what data, providing access becomes a lot less complicated. 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 they are unable to specify where they want access to what data, you have a data literacy problem to solve.

Different shades of data literacy

It is evident that 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 to analyze the impact of their work. However, different teams with different data needs require varying levels of data literacy.

Very different skills are required to implement data tracking, to derive insights from data, and to 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 different types of data and require different skills. The former requires training in data science while the latter is a problem for data engineering to solve.

It is safe to say that data literacy, in some shape or size, has become a prerequisite for individuals to excel at their duties. And 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 to enable everybody to work with data by investing in the tools that enable them to do so.

That begs the question:

How do you choose the right tools to enable everybody to work with data?

To answer this question, first 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 works with data to deliver faster resolution (by seeing 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 works with data to identify prospects that are likely to convert (by looking at the actions they have performed during the 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; this 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 job 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 of the tools they use 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, 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 needs to invest in a set of additional data tools—often referred to as a modern data stack—to cater to the needs of the entire organization. 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-serve 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 at least a dedicated data person to manage this ongoing process.

It is crucial to invest in products that enable individuals to work with data efficiently to derive insights and make data-led decisions without relying on others. 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, make sure to 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:

Why is data democratization an ongoing process that might require a cultural shift in your organization?

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

Larger organizations are likely to face a slew of challenges, making data democracy come across as data “democrazy.”

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

So, why is data democratization an ongoing process?

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

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

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.

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