Figuring Out How IT, Analytics, and Operations Should Work Together


Here is an excerpt from an article written by Gahl Berkooz for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

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A new set of relationships is being formed within companies around how people working in data, analytics, IT, and operations teams work together. Is there a “right” way to structure these relationships?

Data and analytics represent a blurring of the traditional lines of demarcation between the scope of IT and the responsibilities of operating divisions. Consider the core mission of the modern IT department: Taking in all the technology “mess” (often from several different divisions), developing the necessary competencies, and delivering savings and efficiency to the company. Many IT organizations, having achieved this original mission, now are turning to the next thing, which is innovation.

Enter data and analytics, which provide an opportunity for such innovation. However, data traditionally is owned by the business, and analytics is valuable only to the extent that it is used to make business decisions, again “owned” by the business. For IT to operate in the data and analytics space often takes realigning roles and responsibilities.

Let’s look at four examples of how different corporations responded when faced with this question.

[Here is the first example]

The integrated operational data and analytics function. This example comes from an entertainment company that makes a handful of large bets a year — worth several hundred million dollars each — on entertainment properties. The company’s goal for its data and analytics function is to provide decision support before making the “big bets”, and to optimize social media engagement to maximize revenue from the properties once they are launched. The company set up a data and analytics function with full operational responsibilities. The unit has fewer than 100 people divided among data science/analytics, technology, and social media.

In this case the data and analytics function created its own proprietary consumer data base, stitched together from many sources, and developed a proprietary cloud-based environment that allows it to engage consumers across all their social media platforms. The data and analytics teams use the technology, data, and analytics assets to evaluate potential investments, test campaigns, optimize social media buying, carry out the media buying (bypassing agencies), carry out the campaigns, forecast business outcomes, and analyze effectiveness. The data and analytics function is completely independent from corporate IT.

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There is no universal right answer to the structure of the relationship between IT, data, analytics, and operations. The poles of the trade-offs are clear. First is a central function versus agility and integration of embedded functions (with a COE where analytics teams can learn from each other). Second is a service function to support existing operations versus a fully operational digital and analytics function focused on the areas of greatest opportunity.

Having a decision-making framework and taking into account these critical questions can help companies navigate their choices. While there is no one right answer, asking the right questions can help organize these increasingly complex relationships.

Author’s note: The opinions herein are my own and do not reflect any views of my employer.

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Here is a direct link to the complete article.

Gahl Berkooz is the Chief of Analytics for General Motors’ Global Connected Customer Experience Division. Previously he established the Information Management and Analytics function at Ford Motor Company. Gahl holds a Ph.D. in Applied Mathematics from Cornell University, is a Six Sigma Black Belt, and an alumnus of the Harvard Business School General Management Program.

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