Making the Business Case for Data Democratization

The goal of data democratization is to enable free flow of information that powers business agility – anybody can use data at any time to make decisions without barriers to data access or understanding. When data is made widely available to people who are committed to a common purpose, amazing things can happen. Search and rescue teams have realized the benefits of sharing aerial images with the public to assist in finding lost hikers. Groups like Safe Kids International are crowdsourcing the process of locating missing children. The medical community has long understood the benefits of sharing data, and has accelerated those efforts to help combat the COVID-19 pandemic.

Of course, not all information sharing is driven by a pre-determined purpose. Very often, individuals and organizations create value from data in entirely unexpected ways. Governments around the world share census data that can be used by business and non-profit organizations. The US National Climatic Data Center provides data that serves a broad community of climate scientists. Even private companies have established data sharing platforms with an eye toward providing benefits to the wider community.

Many enterprises recognize the benefits of making data more broadly available within their organizations. This process of data democratization means that people throughout the business can access a larger data pool and analytics toolset. They can ask questions and get meaningful data-driven answers. With data democratization, the availability of data and associated analysis tools extends far beyond the limited group of experts who have a data science background.

Why data democratization matters

First and foremost, data democratization is about empowering employees to access the data that informs better business decisions. When access to data is limited to a select few, it limits an organization’s ability to ask questions, elicit insights from the data, and apply those insights to the creation of business value.

In addition to driving good business decisions, data democratization also provides for a better customer experience. In this age of the omnichannel, consumers expect businesses to have a complete picture of their past purchases, touch points with company personnel, and even their demographics. If their experience with a company seems disjointed, many are prone to take their business elsewhere.

Data democracy: Why now?

Data democratization has become a hot topic lately; all the stars seem to be in alignment. With the advent of location-aware mobile devices, IoT sensors, digital marketing automation, and ever-increasing volumes of unstructured data, there is so much more information available to be analyzed. Stuart MacDonald, former CMO of Expedia and Freshbooks, puts it this way: “Either you’re analytical and data-driven, or you go by what you think works. People who go by gut are wrong.”

Combine that with advances in technology such as cloud storage and scalable server capacity, AI and machine learning, and improved integration. Then add self-service business intelligence tools that are accessible to virtually anyone. The net result has been a rapid advancement of analytical capabilities, capacity, and usability.

Limitations and concerns

There are some caveats around data democratization that business leaders need to understand. First, there is the question of security. Every organization has measures in place to prevent employees and outside parties from unauthorized access to information. When people start talking about making company data more widely available, it naturally raises questions about how much is too much, who should have access, and what the appropriate level of granularity is for any given dataset.

The same kinds of issues arise around compliance. Companies are already struggling to comply with GDPR, CCPA/CCRA, HIPAA, and a host of other data privacy and security regulations. When considering data democratization, business leaders need to clearly understand downstream compliance implications.

Concerns may also arise around duplication of effort and unintentional misuse of data. In other words, if every department is doing its own work around data analysis, some of that work may be redundant. In the wrong hands, data may be misinterpreted, which can lead to faulty conclusions and poor business decisions. These concerns can be addressed by building transparency, governance, and quality control into the data democratization model, allowing departments to explore new ways to extract value from data while limiting duplication of effort and reining in misuse.

The path forward to data democratization

Data democratization begins with a clear understanding of its potential value across the organization. That, in turn, calls for a holistic and comprehensive strategy that broadens enterprise data access, rather than a piecemeal project-based approach. When analytics initiatives are treated as one-off projects, integration and data governance often suffer as they are tailored to the needs of a specific project on a case-by-case basis. This leads to a “just get it done” mentality that serves the need at hand but fails to address the longer term requirements of a broader audience. A better approach is to build a data governance structure that will outlast the immediate needs driven by any specific project.

Data stewards must also balance security and compliance with the expansion of data availability throughout the organization. Ultimately, the goal is to do both, but clearly security and compliance are not negotiable. Once again, good data governance provides the framework in which organizations can achieve both objectives.

Data democratization is ultimately driven by a three-part equation comprised of simplicity, scalability, and attention to data quality. Simplicity means that the producers of data have clearly understood targets for the structure and quality of the data they create, manage, and provision, while consumers of that information know how they can access it and what they can or cannot expect to learn from it.

Scalability is achieved by limiting the proliferation of integration points, streamlining the flow of information for timely results, and deploying smart strategies such as data federation where applicable.

Finally, data stewards must address the problem of “garbage in, garbage out.” Data quality demands that information be consistent, complete, accurate, and timely. As the volume of information managed by today’s enterprises continues to grow, data quality demands even greater attention. For the end-user who may be unaware of anomalies in the data, there is a substantial risk that poor data quality will lead to inaccurate conclusions and poor business decisions.

Precisely is a global leader in data management, data quality, enrichment, and integration; we offer best-in-class tools and expertise to help companies achieve excellence in enterprise integration and data management.

To learn more about how your organization can get data democratization right in 2021, download our e-book Managing Risk & Compliance in the Age of Data Democratization.