Data democratization explained: Benefits, challenges, and best practices

Democratizing data is like adding burpees to your workout. Everyone knows they should do it—but that doesn’t mean it’ll be easy. In fact, 89% of companies see challenges when adopting data democratization. 

Experian’s Data Democratization report, which brings together insights from more than 500 data practitioners, found that four out of five respondents said data democratization would be a key initiative over the next 12 months. 

Let’s talk about data democratization in a business context, how to implement it, and why it matters. 

Most companies know that data democratization is a good thing, but many face challenges when they start trying to build the cultures, processes, and environments that make data democratization possible, as we mentioned above. 

Let’s start with the fundamentals: What does democratizing data mean, exactly? 

At its most basic, data democratization is simply making data accessible to everyone, including non-technical teams, so that they can contribute to data-driven decision-making. With the right tools, anyone at your company can interrogate data and uncover insights that lead to better business outcomes without needing to write SQL queries or master advanced technical skills.

Your company benefits from a wider range of perspectives and your data analysts aren’t stuck spending hours on ad-hoc queries that distract them from more valuable work.

Successfully implementing data democratization requires adopting user-friendly, self-serve analytics tools. 

As Human37 co-founder Glenn Vanderlinden explains in his article about data democratization, getting your data into a product analytics tool is an important step, but it’s not the only one. 

To truly democratize access to data, it needs to be “optimized and organized in a way that makes it accessible to as many people working on your product as possible.” 

In other words: Tools are important, but organizing your data, creating processes that make it easily accessible, and building a culture that promotes and values data democratization are all equally as important. (We’ll talk about this more in detail below when we look at implementing data democratization.)

Data democratization is critical for product teams in today’s digital product landscape. It gives them access to more insights than they would have access to otherwise, and makes it possible to investigate ideas more quickly and effectively. 

When you don’t have to send every query to busy data teams—and wait days or weeks for them to come back with answers, which will probably be outdated by then anyway—you can test theories and speed up decision-making significantly. 

Better access to more data can also drive innovation, by enabling product managers to act on real-time information, see what effect those actions have immediately, and then take more steps based on these new insights. Fast access to data creates a positive feedback loop for experimentation. 

Reducing your reliance on data teams for smaller product decisions is also beneficial for data analysts, who are almost always juggling competing priorities and queries from many different sources. 

Finally, democratizing access to data is important for collaboration and communication across teams and functions. When you’re all looking at the same source of information and can easily share any insights you find, it becomes much easier to collaborate and build a common understanding. 

German streaming platform Joyn has always championed a culture of data democratization.  But the organization realized that employees were using data to justify existing opinions rather than embracing exploration and looking at the data to create hypotheses. They needed to shift from this everyone-for-themselves mentality to a culture of collaborative and data-driven decision-making. 

Joyn adopted Mixpanel to give all teams access to product data. Now, Mixpanel is used throughout the company, including marketing, content editors, product, engineering, CRM, and more. Users across the company utilize Mixpanel to ask the right questions and experiment, from product changes to customer communications and marketing. 

With easily accessible and queryable data, people have stopped using data to justify their hunches after the fact and started to look at the data first. Joyn’s unique approach to its data infrastructure means it can link all of this data to ask even deeper cross-functional questions, increasing collaboration across departments. 

Read the full case study to learn more.

We’ve talked a bit about why data democratization is important and what it brings to product teams. That doesn’t mean it’s easy to put into place. 

Here are a few common challenges we see when it comes to data democratization, and some advice on how to solve them. 

If you want everyone to use the data, you need everyone to trust the data. Even more importantly, you need accurate, shared data that comes from a single source of truth—if different teams are working with different numbers from disparate data sources, it will be impossible to know which data to trust or build consensus. 

Most companies today use their data warehouse to store data and keep it clean and organized. Tools like Warehouse Connectors make it possible to access data stored in the warehouse in near real-time and use it to affect business outcomes. There’s no point in democratizing access to data if that data isn’t clean and correct. 

One of the main objections to data democratization is a concern that it will compromise data governance. If everyone has access to data, critics say, doesn’t that mean the data is insecure? 

To a certain extent, we agree with you: Making data accessible willy-nilly would be risky. Anyone who wanted to analyze that data would be buried under the mountains of information available. When it comes to useful analysis, too much data can be as bad as too little. 

Features like Data Views help solve that problem. Data Views allow you to manage data access for a group of users within a single Mixpanel project. Project Owners can create and edit Data Views and determine access for both privacy and productivity purposes. This ensures that teams have access to data that pertains to them, not the myriad of events being tracked for other teams. 

It’s easy to be intimidated by data. People who are used to sending their queries over to the data team or operating on hunches might be resistant to changing their behavior. They already have enough on their plate, and now they’re expected to learn a new tool and conduct their own analysis, on top of everything else? No, thank you. 

The only way to overcome this resistance to self-serve analytics is by offering accessible tools and training incentives to employees who want to learn more. Once people see the types of insights that can be gained, they are much more likely to buy into the idea. 

In Kast’s early days, decisions were based on feelings rather than facts. The screensharing and online watch party app grew quickly during the start of the COVID-19 pandemic, but decisions began to feel like a shot in the dark. It was as hard to understand and learn from mistakes as it was to identify what they were doing right. 

The team adopted Mixpanel to inform decision-making, allowing them to gather data they didn’t have the resources or knowledge to access before. That helped them validate hunches and discard assumptions that turned out to be false. By embracing a data-driven approach, Kast discovered that a feature they suspected of being underutilized was actually one of their most popular, averting a disastrous decision. 

Learn more by reading the full case study. 

Despite the challenges we mentioned above, there are several things you can do to improve access to data at your company. 

One of the most important steps to data democratization is creating a culture where everyone is encouraged to seek insights. Founder and CEO of Turbine Emilie Schario calls this “org-wide curiosity.” 

Instead of separating employees into technical and non-technical hires, she focuses on creating opportunities for everyone to look at the data by providing the tools and time for them to do so: 

“It's about fostering a mindset where data is seen as a tool for understanding and improving the business. A ‘data hire,’ in this context, is anyone who embodies this curiosity and actively seeks to leverage data for decision-making,” she says. 

Using data for business decision-making is just the first step. To build a data culture, it’s also important to focus on the outcomes you want to achieve. 

“Simply using data to inform decisions won’t make you succeed at building a product or business any more than buying expensive ingredients will make you a world-class chef… A healthy data culture is one in which everyone is aligned on the outcomes you want to achieve and is clear about how to measure success.”

Italian real estate website Immobiliare needed more insights into how people use its product, without hiring a team of expensive data analysts. Chief Product Officer Paolo Sabatinelli realized the company needed to build a data-informed culture to surface the product insights. 

To do that, he convinced Immobiliare’s CEO to be the executive sponsor. He realized that becoming “data-informed” required a cultural shift driven from the top down. 

He chose Mixpanel as Immobiliare’s product analytics solution because it’s robust yet intuitive, and fully customizable. These attributes meant Immobiliare could democratize data access across the product management, engineering, and design teams.

Sabatinelli also ensured there was data literacy across the business by establishing training programs for existing employees, and ensuring Immobiliare hired candidates with data expertise.

Thanks to these changes, Immobiliare boosted app traffic mobile app user traffic from 9% to 26% and improved engagement, with an increase of 40% in listings viewed per month.

Read the full case study to learn more. 

Using a single source of truth ensures data is centralized, accurate, and accessible. If you haven’t already, take stock of your organization’s data storage and make sure you aren’t storing data in a bunch of different tools. 

One of the key tenets of data democratization is making data accessible to everyone, even without a technical background. Choosing intuitive tools with user-friendly UI makes it possible for product managers (and anyone else) to explore data without coding.

Even intuitive tools have a learning curve. If you want your team to explore data, give them the training and support they need to embrace self-serve analytics. Most platforms offer webinars, documentation, how-to videos, and other support. Make sure your teams know where to find these resources and have the time and access to use them so they get the most out of whatever platform you choose.

Genealogy company Ancestry wanted to grow its subscription business. Adobe Analytics, the tool the company was using, was too complicated for non-technical users to master and required the product analytics team to pull insights, which limited speed and depth of user understanding. 

Switching to Mixpanel freed up analysts’ time and allowed Ancestry to integrate analytics into the organization’s workflows. Now everyone can access, understand, and leverage data to gain insights and be more strategic.

Read the full case study here. 

Choosing a self-serve product analytics platform is one of the keys to promoting data democratization at your organization.

Not to toot our own horn, but Mixpanel is the leading self-serve analytics platform out there. Intuitive Boards and Reports make data available for all team members in real-time.

You can create rich, collaborative content in a variety of formats, including written summaries, images, GIFs, and hyperlinks to other reports and tools, which makes it easier to communicate data and add context. Data governance and data classification tools like Lexicon make it easy to organize data into a data dictionary that stores descriptions of events and their properties. This allows all team members to understand what your data means so that everyone stays on the same page.