Democratizing Analytics for Better Decision Making

Ten years ago, providing self-service business intelligence (BI) capabilities to democratize analytics power across the organization was high on the list of most companies’ CIOs. With the data and tools of the time, it was possible, and many companies did a good job of implementing self-service BI—at least to “power users.”

However, over the past decade the deluge of big data has swamped self-service BI at many companies. The complexity of data sets—not to mention the methods and tools used to analyze data—has made it difficult for business users to perform their own reporting and analyses. As a result, analytics democracy has receded at many companies, and data scientists tasked with extracting knowledge and insights from big data have often become de facto report providers.

Besides data complexity, there are other hurdles to democratizing analytics. At many organizations, data is fragmented across different platforms and systems. It’s difficult to standardize and govern data that’s distributed throughout different functions and systems. Finally, security is a concern with widely dispersed data and access. All these issues can be addressed, and if you do, you’ll be on your way to restoring analytics democracy and reaping the benefits.

Consolidate Data

Democratizing AnalyticsSiloed data is the first barrier to democratized analytics. Fragmentation in systems and data hobbles efforts at enterprise-wide analysis and creates an environment where there is no single version of the truth about organizational data. The remedy for this is data consolidation. The best solution for data consolidation is to [popup url=”” height=”400″ width=”400″ scrollbars=”yes” alt=”popup”]move data to the cloud[/popup]. However, if you’re not ready to make that leap, at a minimum democratizing analytics requires a common (or federated) data store with clean, standardized data that provides the same answers to the same questions, i.e., it creates borderless data.

Secure and Govern

Yes, I harp on this, but it’s true. It’s crucial to have good data governance—especially when you’re developing a borderless data environment. Clear and enforced data governance policies and procedures, as well as strong data security, is paramount. Data masking and encryption are fundamental security strategies, as well as implementing role-based access. If you do it right, sound data governance can actually drive business agility by giving the right people the right access to the clean data they need, when they need it, and it can ameliorate concerns about data falling into the wrong hands.

Get the Right Tools

Once you have all that consolidated, clean, secured data, you need tools that enable end users to access and interpret it without having to be data scientists themselves. I’m not going to discuss the technicalities of architecture here, but at a broad level, it’s crucial to have a [popup url=”” height=”400″ width=”400″ scrollbars=”yes” alt=”popup”]data architecture[/popup] that supports many different tool types, and that is flexible and scalable to change and grow with you as your analysis needs change.

Data visualization is also fundamental. Business users should have the ability to customize views, develop dashboards and graphical models—and change them as necessary—to view answers to their questions as visual representations rather than in rows and columns, or as the numeric results of statistical calculations. With the right architecture and tools, self-service access and collaboration is facilitated, and decision-making becomes a democratic process rather than a fragmented, competitive race.

The Upside

What’s the payoff? Faster, better decision-making that’s done collaboratively and securely. When you give business people access to information they need to do their jobs, and trust and encourage them to use their newfound power, you’ll realize the benefits.

For example, instead of waiting on IT/data science to feed them reports about lead-scoring models, your salespeople can pull the information themselves and follow the most promising leads to optimize their personal sales process and get a jump on maximizing sales volume. Your marketing people can see graphic representations of customer-channel interaction data and use that data to develop more effective segmentation strategies and customized campaigns and opportune, individualized offers. The uses of democratized analytics are as many and myriad as the companies that use analytics.

The bottom line is this: By putting analytics power in the hands of business people in sales, marketing, finance, logistics, etc., you empower them to make their own data-backed decisions, using their unique expertise and perspective. Data scientists are then freed up to focus on the high-value, deep data analysis activities that you hired them for in the first place. Finally, analytics democratization also encourages collaboration and cross-pollination of ideas and experiences across the organization, which will ultimately lead to quicker, more well-informed decisions that drive change and help you reach your goals faster. That’s not a bad payoff.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Leave a Reply