TDWI Upside - Where Data Means Business

Data Democracy Now: Advantages, Issues, and Implementation

Bringing analytics closer to decision makers has long been a goal of both IT and business leaders. Data democracy is one important step toward maintaining competitiveness and promoting growth.

Bringing analytics closer to decision makers has long been a goal of both IT and business leaders. This was partially addressed by self-service BI, beginning with fairly rigid dashboards offering limited scope for experimentation. We are now seeing value in making analytics available to a much wider range of workers. This requires not only advanced self-service analytics platforms but also making a much wider swath of data available in a usable form.

For Further Reading:

Balancing Self-Service, Governed BI, and Analytics

3 Best Practices for Becoming More Self-Sufficient with Self-Service

When It Comes to Data, Agility and Governance Are in Harmony

The combination of self-service and data availability has been referred to as "data democracy." Variations of this term have been under discussion for years.

Benefits

Data democracy can be an important part of the conversation between IT and business about what should be developed and how goals should be defined. The driving idea is that enabling business users should define their own information requirements, answer their own questions, and create their own tools. This will energize processes such as marketing and sales by engaging these users' innovation and business knowledge. Once provided with appropriate tools and access to vast quantities of data, business users should be able to act as "citizen data scientists" and build solutions that meet ever-changing needs. They will bring their specific business knowledge and insights to bear on defining analytics needs.

Striving toward data democracy is an important step toward maintaining competitiveness and promoting growth. Data has often been described as the lifeblood of modern enterprise. Change is happening faster and faster, and the "Red Queen Hypothesis" (everyone must run faster and faster just to remain level, otherwise falling behind) certainly applies. The need for innovation and efficiency is becoming increasingly acute as technologies such as machine learning (ML) and robotics process automation continue to develop.

Challenges

Although the proposed benefits are real, implementing data democracy involves serious caveats in technology, culture, and business requirements. Typical problems include cases where:

  • Users fail to understand the data or analysis
  • Users create unintended technical impediments such as personal data silos
  • Data is not properly governed or secured
  • User-created models are inadequate or incorrect
  • Data scientists must do more work preparing data rather than taking on higher-value tasks

The core issue is that neither data democracy nor self-service analytics can function entirely without intervention from IT departments and data scientists; yet oversight needs to be flexible enough to encourage innovation.

Data is a valuable resource that must be stored, conserved, secured, and prepared for use. Models and analysis need to be checked, training needs to be met, and citizen data scientists need guidelines for best practices within their particular organization. Although platforms are emerging that can significantly reduce IT involvement, problems of corporate culture, focus, oversight, and training are likely to remain important for some time.

Analytics Centers of Excellence

An increasingly popular way to handle these requirements is to create an analytics center of excellence (ACE). Such a center can mediate between the analytics needs of business and the data management and program-support mission of IT.

How the ACE is structured depends on the maturity of the analytics organization, systems in place, complexity of requirements, and expectations. In general, such an organization provides:

  • Data governance
  • Training
  • Consultation
  • Infrastructure advice and implementation
  • Data architecture management
  • Planning, monitoring, and oversight

The ACE should be capable of acting as an intermediary between the IT department and business users. It can also provide added flexibility as new technology is introduced that needs to be integrated with current analytics systems.

On to the Data Democracy Future

As the data environment continues to evolve, vendors are offering a greater variety of solutions that support the data management and analytics access required to enable data democracy. These range from gigantic platforms that purport to handle every aspect of analysis and data management to lean and innovative solutions that focus on doing one thing well and offer integration with other vendor solutions. Most of these tools include ML capabilities to manage the intricate problems of data storage and provide seamless use of self-service analytics tools.

Analytics needs, data availability, and data storage environments vary significantly from company to company and from industry to industry. There is no silver bullet for data democracy; it demands a careful examination of a company's data needs, its competition, its industry, available skills, and infrastructure.

As a first step, ensure you have an adequate understanding of data and analytics throughout your enterprise. Without curiosity and understanding, after all, the concept of data democracy fails to make sense.

About the Author

Brian J. Dooley is an author, analyst, and journalist with more than 30 years' experience in analyzing and writing about trends in IT. He has written six books, numerous user manuals, hundreds of reports, and more than 1,000 magazine features. You can contact the author at bjdooley.query@yahoo.com.

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