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Enterprise Analytics: Big Data Measures to Better Business

big data measuresI’ve reviewed books written by one author, but rarely have I come across a good compilation of business experts in one text. Leave it to business intelligence to yield a solid compilation such as Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data [1].  

Edited by analytics expert Thomas Davenport, the book gives an overview of business intelligence that can make or break strategic big data development. This past summer I picked up a free copy from the Chicago stop of a SAS road show for its new data virtualization solution.

Because of the different authors involved, I will highlight the sections that I feel are worth a read.

The first chapters outlay analytics in its various forms.  Davenport begins chapter one explaining the various forms of analytics and their differences, while chapter two, by Keri Pearson, provides a financial example of ROI. A list that appears at the end of the chapter has some great lessons learned that consider an order of potential occurrence.  Such an approach can help organization frame which project to address.

To show what I mean, here’s an example of selecting the projects with the largest ROI (return on investment):

Start with the high ROI project, not with the low or hard-to-quantify one.  The first project normally bears the biggest cost because the start up usually involves setting up the data warehouse. If it can be done with a large ROI project, future projects are much easier to justify…

The most relatable chapter for small businesses is Chapter 4. The author, Bill Franks, gives a good foundation of how Web data is the basis for doing more than accounting Web traffic. He offers a refreshed look at the worth of non-conversion traffic – the 96% of website visitors that do not click an intended button or submit a fill-out form.

This segment is worthwhile for small businesses seeking a deeper reasoning behind the cost to modify an analytics solution or create a custom dashboard. Many still treat analytics as a form of accounting. As they say in commercials “Wait, there’s more!” Well, Franks explains the “more” with the chapter segment, Web Data In Action. He mentions a few models such as attrition and response modeling. I liked how imaginative Franks take is for emphasizing the customer segments that businesses can develop, such as this comment:

Consider a segment called Dreamers that has been derived purely from browsing behavior. Dreamers repeatedly put an item in their baskets but then abandon them. Dreamers often add and abandon the same item many times…So what can you do after finding them? One option is to look at what the customers are abandoning.

Another solid segment is Chapter 12 Engaging Analytical Talent. This was written by Jeanne Harris (who co-wrote Analytics at Work [2] with Davenport and Robert Morison) and Elizabeth Craig. It gives a brief overview of how to set assignment objectives that shows your organization understands analytical talent:

Arming analysts with crucial information about the business is one way to keep analytics talent engaged.

The ideas were spot on to what’s happening.  I recalled a well-known recruitment firm’s study that indicated analysts changing jobs partly from lack of engagement and meaningful support.  Moreover, Harris and Craig show how to identify “4 Breeds of analytical talent” that deftly conveys the value of each talent.

Privacy issues are noted in Chapter 4, but advocates should read Chapter 13, Governance for Analytics.  Stacy Blanchard and Robert Morson lay out the process for establishing analytic management, the processes that ultimately protect data as much as it extracts value:

Establishing governance is a mix of science and art, where the specific power dynamics within the organization play a significant role.  There is no single right governance model for analytics, but a number of good principles and practices are commonly found among the organization with high-performing analytical capabilities.

Concepts, while meant for large organizations, can still fit a medium sized business, such as guiding principles and understanding why governance is important.  The list “You Know You’re Succeeding When…”  can be modified for smaller businesses that uses analytics and have stakeholders remote from their operations.



Later chapters present cases of large enterprises. A few note the impact of analytics on specific industries, such as retail (Sears) and pharmaceutical (Merck).

Again, this is a book meant for managers of large organizations. But for small businesses looking to grow, it can give an overview that encourages a deeper appreciation for detailed books like Web Analytics 2.0 [3] or Performance Marketing with Google Analytics [4].

Analytics, in general, forces a business to look critically at how it operates.  Books like this one will provide the right framework for managing those operations for your best business performance.