Business executives, managers and analysts have wrestled for over two decades with the problem of understanding how to leverage data to improve business results. For much of that time, the umbrella term “business intelligence”—or “BI” for short—has been used to describe a family of business analysis techniques ranging from standard reports to highly sophisticated advanced statistics. More recently, terms like “big data” and “analytics” have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. 

The power of BI in terms of production has become increasingly clear over the years, and there is no debate that it plays a key role in achieving competitive advantage. At the same time, one must consider industry, company, business model and competitor’s actions when determining the strategic importance of BI. Generally, the more complex and information-intensive an industry is, the greater the strategic importance of BI and the greater the opportunity for competitive differentiation. Through understanding the strategic importance of BI in the context of industry and business model, a clear BI mission and strategy can be formed. These considerations affect the pace of capital investment, funding for the BI program, pace of resource acquisition and/or utilization, pace of BI deployment and ultimately the pace and magnitude of business value creation.

To succeed in leveraging business intelligence (BI), it is critical to move beyond vague and/or generic value propositions. Purported benefits such as “enabling better decisions,” “increasing customer intimacy” and “enhancing supply chain agility” are, for the most part, insufficient in convincing executives to invest in BI. Executives want to know precisely how BI will benefit the company. How do proposed investments in BI relate to enterprise and/or functional business strategies? Which business processes will be improved and by what type of BI? What economic return can be expected over what general timeframe? Accordingly, to obtain funding and shape an affective BI program, it is necessary to identify, define and document BI opportunities, or BIOs for short. BIOs provide the investment hypotheses, value propositions, and/or business cases for investing in BI to improve profitability. The process of identifying BIOs is called BI Opportunity Analysis, and there are a number of techniques that can be used.

Most companies are able to identify multiple business intelligence opportunities (BIOs)—opportunities to leverage BI to increase revenues, reduce costs, or both. Realization of these BIOs requires a joint effort between business sponsors, business subject matter experts, and the technical teams who execute BI projects. Given finite resources, the identified BIOs need to be prioritized for execution over time—typically via a series of rapidly executed projects over the course of a few years. There are a number of factors that can be considered when prioritizing BIOs, and different companies employ differing degrees of formalization in doing so. For some companies, structured conversations about the BIOs are sufficient for agreeing on the priorities. For others, more rigorous multiattribute scoring techniques are favored. And of course many companies require some form of return-on-investment (ROI) analysis.

Business intelligence (BI) is ultimately about improving business performance. The primary way that BI can increase revenues, reduce costs or both is by improving the business processes that drive those results. Improvements can be in the form of enhanced process efficiency and effectiveness, and/or more effective, streamlined, and automated decision support. The most relevant business processes to target for improvement generally fall into three broad categories: 1) performance management processes; 2) revenue generating processes; and 3) operating processes. Performance management processes include planning, budgeting, performance monitoring, variance analysis, scenario analysis, and economic forecasting/modeling. Revenue generating processes include marketing, sales, produce development, product management, and customer service. Operating processes include purchasing, manufacturing, logistics, demand forecasting, sales and operating planning, order management, human resources development, asset management.