Power in Data: Turning Facts into Discoveries
Blog: Smarter CMO Solutions
Deepak Advani - Mr. Advani owns strategy and development for the BA product portfolio, which includes software for business intelligence, performance management, predictive analytics, and risk analytics. From 2005 to 2009, Mr. Advani was the Chief Marketing Officer and SVP of eCommerce for Lenovo.  Before joining Lenovo, Mr. Advani worked at IBM for 13 years where he held several global executive positions.

Chief Analytics Officer: The Newest Member of the C-Suite

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A lot of clients I talk to want to know the best way to deploy analytics across their enterprise. Most companies use analytics at the departmental level for use in cases such as to improve ROI for marketing campaigns, reduce downtime by predicting failures, and increase inventory turns through optimized assortment planning. Few use analytics pervasively across the enterprise, which is where I'm convinced we're all headed.

First, let's discuss why deploying analytics pervasively matters. Most of us use different channels as we go through the purchase and ownership cycle. We may do research by talking to our friends on Facebook, walking into a store to touch and feel the product, comparing prices on-line, and then wind up buying the product by talking to a sales rep over the phone. Having a positive and consistent experience across all touch points is likely to strengthen preference.

Similarly, if you have a large money market account with a bank, it would be nice to be treated as an existing customer when applying for a mortgage. Often you have to start from scratch because the different lines of business have their own IT systems and customer databases. Growth through acquisitions further amplifies this issue of disconnected islands of duplicative and inconsistent customer data.

The best way to enable pervasive deployment is through a centralized analytics group. This group can create and maintain a trusted information management platform from which analysis can be done in a consistent and an accurate way. At some of the more advanced clients, this centralized group is not just made up of statisticians and modelers. It also has representation from various functional and lines of business. To truly optimize business outcomes, it's essential to blend analytical skills with domain and industry knowledge.

A big advantage of having a centralized analytics group is that it can ensure that the enterprise is operating from a standardized set of reports, dashboards, and models, which can drive greater alignment and faster decision-making across the enterprise. For example, as a demand-generation campaign executes, the marketing department can ensure that the right offers are made to the right prospects at the right time. At the same time, the supply- chain department can ensure that there's adequate inventory to meet the anticipated demand, the finance department can help with dynamic price optimization to manage the revenue and profit metrics, and so on.

A centralized analytics group can also address a key inhibitor to analytics deployment: the lack of skills. To get the most out of analytics, you need different analytic techniques and algorithms for different types of problems, and to analyze different types of data. By standardizing a set of analytic tools, and by building core competencies in analytic techniques ranging from data mining to text analytics to mathematical optimization, you can create a critical mass of analytical skills that can be leveraged across the enterprise.

In fact, several studies provide evidence to the benefits gained through centralization. One study found that the companies that have a consolidated center for analytics outperformed those who didn't--in every measured category. And this is already starting to occur. One of our recent surveys showed that 65% of the companies that deploy business analytics are starting to consolidate their operations, in an effort to deploy analytics more consistently and pervasively across the enterprise.

The natural question then becomes, where should this group reside? We've seen this group reside in finance, marketing, operations, IT, and even under corporate. The key, in my opinion, is for the group to be led by a strong and well-respected leader, and for the group to reside with the group that has the enough influence and the mission to drive change across the enterprise.

In fact, change management is a key element to the successful deployment of analytics. Analytics is all about changing the way decisions get made. And key to that is creating a culture that values and celebrates fact-based decision making. For many companies, this requires a cultural change.

Perhaps time has come for a new member of the C-Suite: the Chief Analytics Officer (CAO). And perhaps the CAO should have as much influence as the CFO or the CMO and report directly to the CEO. For the right individual, this could be an opportunity to create significant shareholder value.

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