David Eller, Group Data Product Manager, Indicium, on processes and procedures for democratizing data.
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.
In this article, I address this gap by talking about specific processes and procedures for democratizing data. 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 a Ph.D. in data science.
Let’s start by briefly defining what data democratization means and why it’s important.
Again, data democratization is the practice of enabling all stakeholders within a business to analyze data effectively without requiring advanced technical skills. In other words, it allows non-techies – or business users, as they are sometimes called – to analyze data.
In this way, data democratization helps empower everyone to make data-driven decisions. It also reduces the burden placed on data analysts and IT teams because it allows employees to serve their own needs when it comes to data analytics. They don’t have to tie up IT resources collecting, preparing, analyzing and reporting on data for them.
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.
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.
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.
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.
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 eliminates the need for employees to select manually or determine which type of query to direct at it.
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.
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 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.
Conclusion
Successful data democratization requires more than simply deploying certain types of tools. Businesses must also build an effective data platform that gives employees easy access to the data they want to analyze within those tools, while also enforcing data governance standards. Just as important, data democratization strategies should be flexible enough to allow business users to analyze data using a variety of approaches.
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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