6 actionable practices for putting data democratization into practice

There has been plenty of talk in recent years about the concept of “democratizing data” – which means giving everyone in the organization, not just data scientists and IT professionals, the ability to access and analyze business data to drive effective decision-making.

There is also plenty of high-level guidance on how to democratize data. It often centers on practices like providing employees with no-code data analytics tools or self-service Business Intelligence (BI) features.

But in many cases, these conversations are light on specifics. They don’t dive into the details of exactly how businesses can empower non-techies to leverage data effectively. They tell you which types of tools to use, but not which type of data management practices to develop.

While the approach taken by every business will be different, the guidance below can help most organizations to reach the point where all employees are able to take full advantage of data without requiring them to earn Ph.D.s in data science.

Actionable practices for democratizing data analytics

There is no “one simple trick” for democratizing data. Instead, achieving this goal requires a multi-pronged approach that draws on several key practices.

1. Deploy self-service data analytics and BI tools

As I mentioned, one key practice for data democratization is giving employees tools that allow them to analyze data and generate reports and visualizations based on it without having to code. No-code analytics solutions and self-service BI platforms provide these capabilities.

They let employees do things like select the data they want to analyze, then summarize key trends within it automatically. In some cases, modern self-service BI platforms also let users pose questions about data in natural language, which the platforms then translate into data queries that allow them to parse a data set.

2. Automatically select data for users

Self-service data analytics tools are a start for democratizing data. But they’re only useful if your employees can actually connect them to data that is relevant for the questions they want to answer – and that’s challenging for the typical non-technical user, who often doesn’t have a strong sense of where different types of data reside, let alone how to connect them to complex BI systems or data analytics tools.

For this reason, businesses that want to take full advantage of data democratization should automatically select relevant data and integrate it with analytics tools for their users. For example, accountants shouldn’t be expected to determine where to find financial information about the business. This data should be pulled into self-service BI tools automatically from the accounting applications and databases where it resides.

In some cases, employees may benefit from the flexibility to select additional data sources. But they shouldn’t have to start from scratch; key data sets should be pre-integrated for them.

3. Integrate with the tools employees already use

In some cases, non-technical employees generate their own, custom data in places like spreadsheets. To ensure that they can analyze this information effectively, businesses should integrate the tools that employees use on an everyday basis with BI platforms. This is another practice that eliminates the need for employees to contend with the technically complex task of setting up data pipelines on their own.

4. Leverage AI

Sometimes, the simplest and most powerful way for employees to get answers about data is to use AI tools, rather than traditional data analytics and BI platforms. For instance, using a generative AI model trained on your business’s data, employees can ask questions in natural language to query a database, and receive a response that is also in natural language.

This approach completely eliminates the need for employees to select manually or determine which type of query to direct at it.

5. Enforce data governance automatically

Just as it’s unrealistic to expect non-techies to master data integration and data analytics, you also should not make it their job to understand and enforce data governance rules – such as which types of data are accessible to which users, or how data is stored and retained. Instead, these policies should be defined by engineers, then enforced using automated data governance tools.

Using this approach, organizations can implement automated “guardrails” that allow business users to leverage data effectively, while still adhering to data governance priorities.

6. Give “power users” more capabilities

Typically, some business users have more extensive technical skills than others. Some may have a limited ability to code, for example, or to tweak the behavior of machine learning (ML) models.

To accommodate these users, data democratization practices should give stakeholders access to more advanced tools when necessary. If some users want to write their own Python scripts to process or analyze a data set, for example, let them do so. Don’t force everyone who is not a professional data scientist to work with basic self-service tools.

This point is important because too often, data democratization strategies assume that every business user is almost totally clueless when it comes to data management and analytics. In actuality, skill sets vary widely, and the best data democratization strategies accommodate a range of capabilities on the part of users.

When you do these things, you turn data democratization into not just a buzzword, but an actionable means of enabling better decision-making for your organization.

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