What is Data Democratization?

When you Google “What is data democratization” definition, the top results will be shown “access to data” as the key to data democratization.

However, simply providing data access—whether in the form of raw data in a data warehouse or beautiful visualizations within a product analytics tool or a business intelligence tool — is not data democratization.

So, what exactly is it?

Data democratization is the process of enabling ordinary, non-technical users of information systems to access digital information without IT involvement. It is the foundation of self-service analytics, an approach that enables these less-skilled users (i.e., industries) to collect and analyze data without having to seek help from data stewards, system administrators, or IT staff.

By adopting data democratization best practices, organizations can avoid the pitfalls of the past.

This trend continues, with a recent study by MIT Sloan Management Review reporting that more than 77 percent of respondents reported an increase in access to useful data since last year.

The benefits seem obvious, so why didn't this happen sooner, and what's the reason now?

The main challenges of Data Democratization

There are many reasons why data democratization has not taken over the data workspace. Here are the ones we've identified as the most well-known:

Data in Silos

Enterprises have had some success integrating data into data warehouses, data lakes, and business intelligence systems. That takes care of the storage issue. However, with data stored in multiple silos, it is difficult for an enterprise to establish a single trusted source of data on which everyone can rely. What is required is the creation of a data inventory.

Restricted Access

The problem of not knowing whether you can trust the data is related to the problem of not being able to find it. The traditional approach is to establish a top-down governance system in which IT manages the data, ensures data quality, establishes business rules, and performs the analysis.

Access controls are typically provided to established administrators. However, by erecting a barrier between the people who managed the data, ensured data quality, and performed the analysis - and the business users who needed to act on it - they prevented the free flow of information required for agility.

Incomplete Tooling

Existing business intelligence and data analysis tools were simply not designed for the self-service analytics world we now inhabit.

5 Best Data Democratization Practices

As previously stated, there are numerous advantages to data democratization, but the next question is how to proceed. Here are a few examples of best practices:

Gain a Comprehensive Understanding of the Data Ecosystem

The volume, variety, and velocity of incoming data, as well as the challenges associated with managing it, increase as an organization grows. Information becomes siloed in systems and is only accessible by relevant teams, providing users with a myopic view of the data space.

An in-depth understanding of the data ecosystem and the disparate systems that make it up is essential for designing an integrated data space that provides all users with a holistic view of the information assets, as well as the metadata and context they require to feel more confident about the relevance and trustworthiness of data.

Make Data Available to Everyone

In most organizations, data integration and analysis tools are housed in IT departments, which serve as data gatekeepers, with business users relying on data scientists to access relevant BI and analytics data. This can lead to a data management process that is slow, inefficient, and heavily reliant on IT.

Businesses that want to benefit from data democratization must invest in data integration and analysis tools that are usable and performant for everyone, from developers to end-users with limited technical knowledge. These integration tools are required for an organization's democratization of data and analytics.

Control Your Legacy Data

Data democratization entails more than simply making new data available for analysis and reporting. It also entails releasing data trapped within legacy systems to answer questions that the people who initially gathered this information did not consider.

Legacy systems, however, are inherently rigid and can stymie any organization's data democratization efforts. To overcome the challenge of integrating legacy data into modern infrastructures, businesses must invest in data integration tools that provide instant API connectivity to not only popular databases but also cloud-based systems and applications.

Empower Users with Self-Service Analytics

Organizations must empower their users to not only access data but also make data analysis and reporting part of day-to-day operations to reap the benefits of data democratization.

Although data integration and BI tools and technologies have advanced significantly in recent years, most businesses still struggle to find a data management platform that allows for easy access, analysis, and reporting of data.

The solution is to find a data integration solution that allows you to leverage data from previously disconnected systems, provides out-of-the-box connectivity to BI and analytics tools, and allows employees without technical knowledge to easily manipulate and analyze data.

Educate Employees on How to Use Data Most Effectively

Data governance is inextricably linked to data democratization, and the absence of a data governance strategy can quickly lead to information overload, poor decisions, and reputational risk. To avoid these data democratization drawbacks, everyone in the organization should be trained on how to best use the data, the importance of understanding data lineage, and how it can be transformed for BI and analytics.

Conclusions

The latest generation of data visualization and reporting tools has democratized data access and made them more accessible to learn and use (providing better visualizations) than a previous generation of spreadsheet and charting applications. As a worthy example of such tools, we introduce a new EPAM solution - TDspora. This data democratization example helps to explore and simplifies test data generation and delivery while keeping it agile, representative, safe, and compliant with privacy regulations.