Data is becoming increasingly important for modern businesses.
As it becomes more accessible, using data to drive business decisions is no longer a good case practice — it’s a new norm. If you don’t use data to inform your strategy, you’ll be quickly outcompeted by others who do.
The ever-increasing importance of data in driving product outcomes led to the birth of new data approaches. One of these approaches, recently gaining in popularity, is data democratization.
Let’s dig deeper.
Table of contents
- What is data democratization?
- Why is data democratization important?
- The risks that come with democratizing data
- Four elements of data democratization
- Data democratization doesn’t replace data analysts
What is data democratization?
There’s a common misconception that data democratization is all about giving everyone access to data. Although data accessibility is a cornerstone of data democratization, it’s just a tiny part of the bigger picture.
If I was to describe data democratization in one sentence, I’d say it’s “an ongoing process that helps everyone in the company make data-informed decisions effectively.”
Data democratization is an ongoing process that helps everyone in the company make data-informed decisions effectively.
To effectively make data-informed decisions, one needs more than just access. They need proper skills and knowledge to use the data, tools that support the effective use of data, and the whole company embracing a culture where talking about data is a daily thing.
We will dig deeper into these in later chapters. One thing for you to remember now: you can’t just give everyone read access to the database and call it data democratization.
Why is data democratization important?
Giving everyone access to data brings risks, training is time-consuming and expensive, and shifting company culture is tough. Why would one embark on such a treacherous adventure?
I’d say there are four main benefits of democratizing data: team empowerment, waste reduction, improved discovery, and outcome orientation.
We often talk about empowered, self-organized teams. But to truly manage themselves and drive product outcomes, the team needs resources, and one of these resources is data.
You can’t talk about an empowered team if they have some outsider telling them where the metrics are going down or what KPI they should improve at a given moment. And even if you have dedicated data analysts on the team, having only one person responsible for analyzing and interpreting the data is like having only one person worry about the user or quality. It’s just not enough.
Specializations are okay, sometimes even necessary. But they shouldn’t create internal silos. If you want empowered teams that drive product outcomes, all team members should be able and encouraged to use data to inform their decisions.
Everything goes smoothly when everyone has access to the data they need — no more waiting for the data analysis department to respond to your request.
With data democratization, whenever you need more data to make a better decision, you can check it on your own without waiting for someone to do it for you.
The more people work with and talk about data, the more insights they can recall during the discovery process.
Brainstorming and creativity go to a different level when everyone in the room understands the product from a quantitative perspective. Instead of just throwing random ideas or guesses, they can recall what they learned from data in the past weeks, or even quickly check metrics to confirm or pivot ideas on the go.
People who understand users and data will make better discoveries than those who brainstorm random features.
The phrase “outcome over outputs” has recently become as common as “we are agile!”
But it’s easier said than done.
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You don’t build an outcome culture by just telling people to focus on outcomes.
Data democratization is a significant step toward building an outcome-oriented company. If your teams talk about key metrics daily and make decisions based on that, it’ll shift their thinking over time. New ideas will no longer be judged purely from the “how cool it sounds” perspective but from their impact on team KPIs.
When you stop just talking about key KPIs and objectives on quarterly plans and company reviews and build them into the daily culture of every team, magic happens.
|Siloed data||Data democratization|
|Limited access to data||Open access to data|
|Only a few people know what happens to key metrics||Everyone knows what happens to key metrics|
|Working with data as a specialized skill||Working with data as a common skill|
|More bottlenecks||Less bottlenecks|
|High data security||Increased chance of security breaches|
|Data misinterpretations are rare||Easy to misinterpret the data|
|Data analysts’ primary job is to answer data-related questions||Data analysts are working on high-level, strategic work|
The risks that come with democratizing data
Nothing is perfect, though. Data democratization comes with its own set of challenges that need to be tackled.
The more people have access to data, the more challenging it is to meet GDPR and privacy requirements. Where is the balance between democratizing data and protecting users’ privacy, anyway?
How would you feel, say, using a royalty app for a retail store knowing that your friend who works there can easily check how many beers you buy per week?
Sharing data with everyone increases the chances of data breaches, whether by malicious behavior or accidental mishaps.
Not much is needed, really. All it takes is one person leaving their laptop without logging out first or sharing the wrong window during a video call.
Properly analyzing data and drawing proper conclusions requires skills and experience.
When everyone can access and analyze data, it’s easy to come up with the wrong conclusions and recommendations. And, in many cases, misinterpreting may be worse than having no data at all.
All these risks, however, can be mitigated by properly implementing data democratization.
The four elements of data democratization
The healthy data democratization process consists of four core building blocks:
Since not everyone on your team is a data expert, you can’t just give them access to raw data from the database and expect great insights.
People need a set of proper tools to get the job done. It includes
- Analytics suites: for example, implementing self-service tools such as LogRocket will make it a few times easier for the team to work with data
- Dashboards: sometimes all it takes is one well-thought-out dashboard to empower the team and unlock their potential. Especially at the beginning when people are still learning how to work with data, make it as accessible for them as possible
- Filters and segments: at some point, people will be ready to explore data on their own. Make it as frictionless as possible. Predefine filters, key metrics, and other useful configurations to make the exploration easy
- Refined data sets: process raw, unactionable data into more refined sets of refined data to make it easier for others
Being a data-driven, data-democratized organization doesn’t mean everyone should know SQL or how to work with raw data.
Make working with data easy. And fun.
As I already mentioned, drawing the wrong conclusions from data might be worse than no data at all.
Invest time in proper data analysis training. People should understand concepts like seasonality or the differences between correlation and causation.
Not everyone needs to become a data expert, but they should have decent knowledge in their primary area. For example, the monetization team should deeply understand how retention, churn, revenue, and CVR are calculated, what impacts these metrics, and how to react to changes there.
Make sure people know what they are doing.
There’s no point in preparing tools and training people if your organizational culture doesn’t support data-centricity.
Start with breaking the boundaries of individual specializations and promoting self-service. Do you have a data question or doubt? Try to check it out yourself first, and only then ask for help.
Encourage people to talk about data regularly. Start sprint reviews with data checks, use data to help you assess potential opportunities and solutions, and so on.
Start expecting data-informed opinions from people. Encourage people to gather data to support their hypotheses when proposing a new idea or feature.
The best training and tools won’t do any good if you don’t make data part of your day-to-day culture.
Data democratization isn’t a one-time endeavor. It will take time to properly train the team and establish the right organizational culture.
Plus, you need to experience a lot of screwups and mistakes to truly find the best balance between empowering teams and relying on experts.
Review your processes regularly. There’s always something to improve.
Data democratization doesn’t replace data analysts
Data democratization is not a way to save a few bucks by firing your data analysts. You’ll need them even more. One can’t replace years of education and experience with a how-to manual.
What changes is their role. With data democratization, your data analysts are no longer just human interfaces that produce answers to requests. They become data coaches and transformation leaders.
They ensure you still process and use data correctly and help the whole organization learn how to work with data effectively. Data analysts also become your SpecOps team for mission-critical analysis — sometimes, having the right data is just too critical.
The goal of data democratization isn’t to replace data analysts but to free up their time from mundane, day-to-day work and allow them to work on a more strategic level. Democratization is a way to unlock data analysts’ full potential.
Data democratization is a long-term process, and the final results look different in different contexts.
Before embarking on the journey, ask yourself what your objectives and expected outcomes are, and then adjust your democratization strategy to fit those objectives.
And don’t rush it. Data democratization comes with its own set of risks and challenges, and if you try to tackle them all head-on, you can do more harm than good. It’s a process that takes time.
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