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Enterprises have more decision-making power over their data than ever before because of the ability to optimize aggregated data. But at the end of the day, data aggregation is only as valuable as the insights of those who use it. It’s not enough for IT to provide users with slice and dice data aggregated from diverse sources, and then let them “have at it” with easy to use analytics tools.

For example, the purchasing department aggregates information and determines from analytics to hold off on new orders to suppliers because it sees that certain items are mounting up in inventory. Or, purchasing decides not to reorder item components, ultimately reducing the quantity of finished inventory for the company to sell. These are viable strategies — unless unbeknownst to purchasing, marketing and sales run its own analytics and then plan to run a promotion of that same end item to a population segment that is mostly likely to purchase it; marketing and sales assume normal inventory levels will be on hand to meet the uptick in demand.

There is an immediate disconnect in the company, because departments are aggregating data differently and seeing their analytics in different business lights. Consequently, they arrive at different results and at conflicting conclusions.

In cases like these, data aggregation and the ability of business end users to take analytics into their own hands with easy to use tools is not fail-proof. The question is: Who should step in to make sure that aggregated data and self-service analytics efforts are orchestrated in a holistic way that makes sense for the entire company?

The historical equalizing agency in many organizations has been IT, which provides data services to everyone and also is in the best position to see across all of the enterprise’s data. However, as analytics work becomes increasingly business driven, it is more incumbent upon executives in end business functions and even the CEO to assume responsibility for overreaching analytics decisions. After all, these are the people who are in the best position to understand how the business and its markets work.

Synchronizing business decisions with the analytics efforts that are being conducted independently in different corners of the organization can best be done by holding regular inter-departmental meetings that focus on analytics work and findings across the company. This creates opportunities for different departments to discuss potentially conflicting objectives and come up with the “actionable truth” of these diverse analytics efforts. It also encourages a more holistic understanding of the end business across business functions.

There is no formal word to describe this exercise outside of calling it a kind of internal “people architecture” that complements an organization’s best data aggregation and analytics efforts. As data must ultimately be brought together to present a holistic picture of what is going on in the business, so must diverse corporate departments and stakeholders.

This task begins with the CEO, who must work with his or her direct reports on the key strategic and tactical goals of the business and what analytics are required to produce the necessary insights. One of the underlying analytics facilitation objectives should be regular cross-departmental communications and collaboration on new analytics findings. In short, the culture should foster cooperation and not competition when it comes to analytics.

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CEOs must orchestrate holistic data aggregation efforts