Most people think data democratization is about making data accessible but it is more than that. It takes effort to build data democracy within an organization. If you think providing data access with visualizations in a product analytics tool or a business intelligence tool is data democratization then you are certainly wrong. So, now you must be thinking about what data democratization is. Here, in this blog, we will give you data democratization definitions and the best ways to maximize its potential.

What is data democratization?

Data democratization is the process of enabling everyone in an organization, irrespective of their technical knowledge, to access & work with data comfortably to make data-driven decisions and create the best customer experiences based on data.

To democratize data within your organization, you should keep up with the following principles:

  • Make employees feel free to ask data-related questions.
  • Provide access to the right tools that enable everyone within the organization to work with data.
  • Take the democratization of data as an ongoing process that might lead to a wide cultural shift.

How data democratization can solve data challenges? 

Most companies are now investing in training employees, implementing tools, and making changes to create data democratization. But why companies are looking for methods by which data can be democratized? The reason is simple as more people are familiar with data, the more they’ll be able to solve complex data challenges.

As the pace of change is increasing, even the data teams struggle to meet the expectations of various teams within an organization. There are many common data challenges that people face that we have given here:

  • Don’t have access to the data they need.
  • Don’t know the accuracy of data
  • Don’t know how to comprehend the data.
  • The analytics tools that my company uses aren’t apt for the product teams.
  • Data teams are too busy to provide data.

If you find any of these statements are true for your organization that means you are still far from data democratization. It’s interesting that these data challenges map to the data democratization principles that we have mentioned above:


How can you make employees feel free to ask data-related questions?

To start off you can make data literacy of crucial importance in your organization. Data literacy should no longer be considered an additional skill. You should provide access to the right resources to everybody in your organization to make them data literate.

For some of your employees, only knowing what data the company collects and what it looks like is enough. For others, it might be worth knowing what data is collected, stored, and analyzed, and the format type.

In short, data literacy solves one of your biggest challenges in data democratization which is access to data.

Access to data but what and where? 

When an employee says they don’t have access to data, it might have many different meanings. They can mean raw data in a database, transformed data in a database, data in visual representations, product data in a product analytics tool, demographic data in a customer data platform, and so on.

However, when an employee specifies where and what data they wish to access, it becomes easier to provide them with data access. Also, if you provide them with the right analytics tool that gives accurate data, they can easily trust the data.

So next time when someone says they don’t have access to data, and can’t specify where and what data they want access to, you know it’s a data literacy issue.

Different forms of data literacy 

Data literacy is not an issue limited to how to write SQL queries or how to comprehend complex data reports, it’s more than that. Every team within an organization needs different forms of data to execute their everyday tasks or to analyze their work impact. However, different teams require distinct data for their specific objectives. This means they need varying levels of data literacy as well.

A set of different skills is required to execute data tracking, derive meaningful insights from data, and act upon those insights. Moreover, conducting data-driven marketing campaigns requires a different skill set than that required to identify the target prospects by analyzing the same data in a CRM.

Likewise, creating predictive models and offering personalized experiences in real-time requires different types of data and skill sets. For doing this, one needs to be trained in data science and in data engineering.

We can say that data literacy has somewhat become a requisite for professionals to perform their duties effectively. Nowadays, companies are investing in data literacy to make data accessible to their employees and to stand out from their competitors.

Now, you know that data literacy is an important factor in data democratization. Next, is to enable everyone to work with data by investing in the right data analytics tools.

How to choose the right data analytics tools that allow everyone to work with data? 

To select the right data analytics tool, let’s have a look at the different teams that work with data.


  • Marketing team works with data to create engaging and high-converting content. Data helps them to launch targeted marketing campaigns.
  • Growth team uses data to run experiments and offer personalized experiences.
  • Product and Engineering teams work with data to build features as per customers’ needs and wants.
  • Support team works with data to find ways to deliver fast resolution. (They can see what user has done or not done to offer them better support).
  • Customer success team requires data to provide a better customer experience. (They can ask the right questions to customers based on their buying patterns).
  • Sale teams utilize data to identify the prospects that have a high chance of converting. (They can look at their past actions).
  • Executives use data to analyze how the business is performing and what future investments need to be made.

Do you think only one tool can do all of the above? 

Well, this is just a brief overview of the data that teams require to work. However, it doesn’t include all the data requirements of a team. This means that you require quite a number of tools to ensure that the teams get access to the right data in the right formats at the right time. Product & growth teams alone often require half a dozen of tools to perform their functions well.

Utilizing the right tools for data democratization strategy 

As you know, data literacy is a must for product and growth teams to be successful. They rely on tools that provide customer data to carry out their tasks. Let it be an analytics solution or tools that collect qualitative data and run A/B tests- the accuracy of the data being made available is the only way to ensure they can derive true value.

Depending on the company’s size and data requirements, every company needs to invest in a set of data tools- often called a modern data stack- to help meet the needs of every team. Businesses that need to gather a lot of data often invest in:

  • Data warehouses like Firebolt, Snowflake, or BigQuery help to make data available for analysis.
  • Business Intelligence tools like Superset, Looker, or Mode are set on the top of the warehouse to enable self-serve analytics.
  • ELT tools like Airbyte, Meltano, or Fivetran help to move data from third-party tools to the warehouse.
  • Reverse ETL tools like Grouparoo, Census, or Hightouch help to move the modeled data to third-party tools from the warehouse.

Purchasing, implementing, and maintaining a modern data stack is not an easy task. You need to hire a data team or dedicated data personnel to manage this ongoing process. Therefore, it’s important that you invest in data products that enable employees to access the data efficiently to extract insights and make data-driven decisions instead of relying on others.

Why is data democratization an ongoing process?


The data democratization pace depends on the size of the company and its growth curve. We can say that it is easy to implement data democracy in the early days of a company. Since the culture is still forming and incorporating data for democracy is uncomplicated.

Do you remember playing the passing the message game in childhood? Often the message that reached the last person was different than the message with which the game started. The longer the chain, the more distorted the message. Similarly, larger organizations face more challenges with data democracy as it comes across as data “democrazy”.

Larger organizations should start investing in the data democratization process early on. Building data democracy is a continuous process as it relies on data literacy which is also a constant process.

The data world is persistently growing and data tools & technologies are also evolving. Somewhat, it is also difficult for companies to keep up with it. Most of the people who work outside the data space are annoyed by it as it has a significant impact on their work.

You must aim to build data democratization in your organization to such an extent that every individual is able to find data-related answers to their questions easily. Evaluate how various teams work with data to decide what should become data general knowledge within your organization.

Make sure that your employees know who has access to data and to what types of data, where the data is and what is the process to get access to the data. By making your employees well-informed, you can ensure that they make data-led decisions that drive growth.

The Bottom Line 

Data democratization can help you ensure a free and secure flow of data within your organization. If you think it’s just a one-time investment then you are wrong, data democratization is a continuous process. A company must optimize systems and train its employees on how to access data and do data analysis.

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