Ownership of the data itself is part of the issue. Data silos are often cited as holding up the benefits of analytics, and uneven investments in technology to store, integrate, and access the data may be at fault as well.

Analysis is not possible without access to the data, so that’s a parallel issue that must be considered.

With data analysis and the predictive power gained from it becoming such a powerful advantage for companies today, anticipating where analytics is best placed is an essential part of corporate strategy.

Here are examples of how a few different organizations structure their analytics function to suit their unique needs and cultures.

And for more on this topic, check out the Data & Analytics section of Econsultancy’s Internet Stats Compendium, or download the Digital Marketing Organizational Structures & Resourcing Best Practice Guide.


In many cases, we have seen the finance function of the business take ownership. There is certainly a case to be made for finance or the CFO of a firm to manage its analytics function.

At leading direct response firm Guthy-Renker, for example, finance owns the company’s data analytics.

“That works here because we’re exceptionally numbers- and finance-driven,” says Lisa Bratkovich, SVP of marketing.

Our desire to know the numbers and integrate them into our plan right away causes us to place marketing planning and analysis and customer analytics all under a centralized function.

They work together, with the added benefit of keeping things a bit more nonpartisan.

Information technology

We have also seen success in cases where chief information officers own data analytics.

For example, Rob Alexander, CIO at Capital One, analyzes customer spending patterns and demographic data to determine which products and offers to make. 

In his most recent keynote speech, Alexander mentions that the company leverages big data technologies to build mobile apps for its customers because this medium is growing at a much faster pace than web applications.

IT ownership can be helpful to firms still grappling with what data to use, where to store it, and how to best access it. All technology-related decisions are best left to the techies.

Closest to the customer

Marketing, sales, and customer service are the big three in terms of customer stewardship.

Depending on your firm’s emphasis on customer proximity and stewardship, there is a strong rationale for centering analytics here.

For companies that are truly customer-focused, the passion, talent, and commitment to read the customer tea leaves — from the customer’s point of view — are essential.

As Farmers Insurance Group CMO Mike Linton suggests:

The CMO is in a position to objectively look at the marketplace, and your job is to drive profitable demand to the company today and tomorrow, meaning you have to deliver this year’s results while projecting tomorrow’s growth agenda.

Taking that approach can yield powerful results. Mobile service provider T-Mobile focused on the key analytic of customer acquisition versus attrition in 2015.

In just one quarter, the company reduced the rate at which customers stopped subscribing to its services by 50%.

Discovery of what repelled, attracted, and retained customers, in addition to taking a strong customer-first perspective, resulted in the fulfillment of some key enterprise-wide goals.

In similar fashion, retail clothing chain Free People grew its net sales by 38% in one quarter by leveraging big data to invest more in social commerce, where most of its customers shop online. 

“There are huge levers within the customer data,” Bratkovich agrees.

I’ve worked in companies in the past where mining that customer data and coming up with models, analytics, and insights has been a huge priority because the company was educated on the gains that data could bring to the company.

The matrix

Still, other firms employ a matrix approach. This especially helps firms that divide the operating budget into specific profits and losses and instill strong accountability for each department to make its numbers, not overspend, and achieve the forecasted ROI.

But how does a potentially expensive but initially non-revenue-bearing R&D-like function such as data analytics survive in an environment like that?

Enter the matrix, where each impacted department antes up, but where accountability may have a lot of dotted-line management between its intent and action.

In these cases, it may be the chief strategy officer who sews together all of those dotted lines.

This scenario best suits firms that take an R&D approach to data analytics and don’t yet know precisely what they are going to get from the data, where it comes from, or where it’s going but are committed to letting the data speak for itself.

Of course, these firms need to honor the true relationships that data scientists can pull from it rather than dictate a destination based on a committed hypothesis.

The great advantage of this scenario is that it allows the data — rather than human bias — to determine the strategy around the organization.

If it’s possible for the many different data customers in the firm to share resources and agree on common analytics that will be helpful to each and all, then this approach can work.

Important questions to make the decision

Ultimately, to realize some of the successes illustrated in these examples, company leaders have to ask themselves how committed they are to an evidence-driven approach to the management of their businesses.

Do they have the culture to support that commitment, the data accessibility, and the analytics ownership structure to deliver?

Ideally, a data governance team will answer these questions and provide a sense of direction regarding how a company can use its data analytics to influence business operations. If uncertainty about analytics exists, the data governance team can provide answers.

Not every company has the resources to have a dedicated data governance team.

But big or small, they can all stand to learn how to best use data analysis to support decision-making.