Capability

Organizations today are facing increasingly complex issues, a more competitive market place, increasing customer expectations and dynamic global work force and customer base.  At the same time, organizations are collecting and producing more data, which presents the challenges of data security, data accessibility, data quality, to name a few.  As a result, there is a growing need for organizations to become more proactive, fact-based and predictive in their decision-making.  They must respond to these forces faster and with more targeted strategies and approaches that will yield consistent results.

In responding to these complex challenges, organizations have historically turned to advanced analytics, query and reporting, and data warehousing technology and services to automate or enhance their decision-making processes.  Many have invested in Business Intelligence (BI) as a component of their strategic framework.  While Business Intelligence framework enables decision makers in organization to have near real time access to key information (great insight), it falls short of being predictive. Although an age-old practice, Business Analytics delivers the predictive component (foresight) that is absent in many BI systems.

Business Analytics Defined

Business Analytics can be viewed as a progression from BI.  Some view it as the next step in the evolution of BI.  However, unlike traditional BI, business analytics extends beyond software and systems.  It is a seamless amalgamation of culture, process and performance strategies.  It is both predictive and historical, and requires an organization to make a cultural shift to creating an environment that is fact-oriented, forward looking, and proactive.  An environment that places great value in it’s knowledge workers and their innate curiosity.

BA is age-old in the sense that it is a practice that many organizations have engaged in for many years, however, in a very manual and labor intensive manner.  In general, it describes the interplay between an analyst, the data being analyzed and a decision maker. It can also be viewed as the “common sense” approach to analyzing and using data.

Why should an organization implement a business analytics framework?

The ability of an organization to effectively analyze historical trends and patterns observed in its data; and predict future outcome is perhaps the main benefit of a business analytics framework.  Other benefits include improved operational process, faster decision making, better alignment of resources with strategies and greater agility in responding to market dynamics. But, it is the predictive aspect of business analytics that makes it such a powerful strategic tool.  As the organization’s data gets broader and deeper, there comes the realization that more progressive analytics can be performed with the data, which in turn can deliver more strategic knowledge.

Take for example a financial firm that is dealing with a financial crisis of exponential magnitude.  The firm’s portfolio default rate increased from less than one percent to over 3 percent of its portfolio.  The firm’s approach to stabilizing the situation was to provide relief to borrowers by modifying the terms of their loan obligation.  Typically, a modification would result in a lower monthly payment, which in turn would provide some relief to the borrower.

However, after analyzing its data, the firm realized that the modified loans were not performing well after 6 months of modification. In fact, the borrower’s payment obligation on the loan after modification was higher than before modification.  As a result, the modified loans went right back into the default status.  The firm then embarked upon a massive data collection effort as a means of gaining better insight into the borrower’s financial situation.  Real time credit data was pulled from credit bureaus; specific data about the borrower’s payment behavior, such as the actual dates they make their payment, how much of the payment was made,   and whether or not the borrower had their payment automatically drawn from their bank accounts.

All these data was collected not just on those loans that were modified, but also on loans that were not modified and were performing well.  Using all the new data it has amassed, the firm was able to develop the capability to predict how future modified loans would perform using historical data and payment pattern.  It was able to measure the Net Present Value of the modification to the firm, and drive future loan modification decisions.

Predictive analytics, built upon predictive models, has such broad applicability; one wonders why so many more organizations are not tuned to it.  Ultimately a knowledge worker is able to represent past behavior to predict future patterns by analyzing enough data and having the right questions in mind.

Example

If an organization responsible for fighting fraud was conducting a thorough exploration of data collected over the years by its detectives and field workers, they are likely to ask the question “what happened” and “what should we do”.  The data exploration or discovery will undoubtedly unearth hidden patterns and possible relationships to answer these questions.  But, with a solid business analytics framework in place, the organization is able to go a step further and address the question of “what will happen” or “what could happen” in the future.  By so doing, they are not only detecting fraud (retrospective), they are also preventing fraud (prospective).