Best Practices for Turning BI Into ROI
Business Intelligence (BI) is the blanket term we all use to describe a broad field of tools designed to organize all the data generated and/or gathered by a business and then using it in ways that will be of benefit to the organization. In that sense the theory behind isn’t all that different from the one behind most other segments of business software: being an asset in the pursuit of evermore optimization of processes.
It’s easy to get lopsided view of just what BI can do for an organization though. In the wake of the explosion of work on and interest in big data, the mainstream focus as of late has been specifically in this area. While big data analytics is indeed a valuable function, it’s not the only way that a business intelligence engine can (or should) be used. Elegant dashboards with pivot tables and visual modeling of your company’s sprawling data sets is all well and good, but as with any other piece of software the real value provided is in translating those data insights into tangible action items.
Unfortunately, the marketing copy for many BI solutions seems to gloss over more specific action items in favor of generalized bullet points about “unparalleled data visibility” and the dashboards you’ll be able to generate based off a decade’s worth of old convenience store expense reports stashed away in your data warehouse.
Below are some of the biggest ways in which BI data can best be used for real ROI:
When high-profile Silicon Valley investor Marc Andreesen wrote in the Wall Street Journal that “software is eating the world,” he used as his rationale the fact that more and more of the world’s biggest industries are transforming themselves by moving their central customer-facing functions into the realm of software and online, remotely accessed services. As a result of that transition industries are seeing their internal processes become more digitized.
Digitization means that information can be organized into data sets more easily and thereby tracked and reported on. This ability opens up all sorts of possibilities in terms of obtaining highly detailed metrics on the performance of campaigns, internal audits and any number of other internal procedures. That material can go a long way in terms of providing a quantifiable framework on which to build plans for optimizing the ways in which departments share information and get their work done.
Leveraging Your Unstructured Data
The divide between structured and unstructured data is one that many BI solutions set out to address in their own way. Speaking broadly, unstructured data can be anything from Post-It notes to audio-video content and HTML-format files, the unifying principle being that the information that they contain is structured in such a way as to make it difficult to be analyzed and indexed.
BI technology has been working to make these kinds of qualitative data capable of integration into its highly quantitative functions, analytics in particular. This is the true substance behind the aforementioned boasts about unparalleled data visibility. In a 2004 article for Information Management, Geoffrey Weglarz illustrates the real-world impact of this effect through the example of tracking sales call content at a call center:
“To gain true insight from the data, it is necessary to consider not the number of calls, but the calls themselves. Necessary data includes the details of each call, the tone of the call, the length of the call and the participants on the call. Was it a voicemail, a secretary or an “on a conference call, can you call me back” call? Or, more important, was it a follow-up call detailing a proof of concept in progress that lasted for more than an hour? Obviously, this last type of call contains more value than the three prior calls.
A slight reality check: the technology for BI is most likely never going to get to the point where one can actually predict the future, but think back to your college statistics courses and you might remember that when your data sample is representative enough you can use correlations in the distribution to get a fairly reliable prediction of where the data is going to plot in the future.
BI analytics in conjunction with reporting tools can achieve the same effect on the material drawn from a business’ warehoused data. By analyzing the distribution of warehouses worth of sales or transaction data (or any other number of other types of data) one can model a range of the most likely outcomes for future campaigns, projects or other actions. These predictions are valuable resources in the development of process optimization tools like decision trees and best practices.
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