The Case for End-to-End Enterprise Analytics


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There has been a lot of chatter in the analytics circles about the need for a Chief Analytics Officer (CAO). Increased focus on data driving insights that drive corporate revenue has brought this to the surface. However, the case for CAO highlights the need for End to End Enterprise Analytics. Organizations that have not been focused on analytics in the past see analytics as a system that spans operations to finance to marketing.

Let’s make the case for a CAO building an End to End Analytics platform for a B2B internet company.

1. The company has hundreds of customers but each customer has millions of transactions. So, data volumes are quite substantial.

2. With a small number of customers the finance department is small and they don’t have a large number of analysts.

3. Marketing and Sales division is quite large and is managed through leading CRM platform. At the same time they are not using any advanced analytics in their targeting.

In this case, the CAO would manage a team of analysts that can span marketing program analytics, financial forecasting and operational analytics including site analytics, content management and more. Even as analytics have taken off in the corporate environment there is still room to grow. Are there efficiencies that can be made by having your entire analytic team under the same roof?

Having the Analysts, Statisticians and Data Scientists reporting to a CAO, I believe team members can learn more about the business and give them greater situational awareness that can lead a more in depth analysis. Furthermore, having team members from finance and marketing working in proximity to each other allows employees to be cross-trained and allows greater flexibility in work for your analysts.

Please comment and let me know what other advantages there maybe. Also, do you foresee any disadvantages to such a structure?

Republished with author's permission from original post.

Jack McCush
Jack's background includes knowledge of Time Series and Multivariate and Bayesian Analysis. His specific experience and classical education in experimental design and implementation is a tremendous value. Jack has completed studies in longitudinal communication designs and their casual impacts on customer behavior. Additionally, he has extensive experience developing a variety of models predicting consumer response to direct marketing campaigns across multi-channels.


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