Business Intelligence should help organizations improve business outcomes by making informed decisions. The problem is that Business Intelligence is the overarching term applied to the tools, technologies, and best practices that supposedly help organizations make sense of data. Where should you start? What tools should you use? What are the best practices? How do you manage the mass of data flowing into your organization? To which buzzwords should you pay attention? Typically, Business Intelligence is a technical implementation driven more by speeds, feeds, and glossy brochures. Perficient’s Enterprise Information Solutions group helps organizations determine how to put business and intelligence back into Business Intelligence. This post tries to define what Business Intelligence means today.
Business intelligence has a storied history with many claims to having coined the phrase. One of the earliest is by IBM Researcher H.P. Luhn in 1956. The more modern attribution is to Howard Dresner then of the Gartner Group. At that time, it covered things such as Decision Support Systems (DSS) and Executive Information Solutions (EIS). Today Business Intelligence is no less phraseology or acronym laden. It is rife with terms such as Big Data, Business Analytics, Visualization, ETL, and MDM all playing a part.
Historically, Business Intelligence tended towards adding graphical components to the static tabular reports that have always been used. It was still just Reporting. That is providing an accounting of what happened in the past. More advanced organizations would follow Edward Tufte’s advice and add an “As compared to what” component. The comparator could be a prior period or future target.
The Future of Big Data
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The next stage in the Business Intelligence evolution is to know what is happening. Dashboarding emerged to present information graphically showing the state of things now. Dashboards are great for performance monitoring the present a collection of information, usually graphically, in a small area and often use a gauge paradigm.
Today, leading organizations are leveraging the increased performance of commodity computers and new technical capabilities, such as in memory databases, to enable visual analytics. Visual Analytics goes beyond Reporting and Dashboarding by allowing users to explore, analyze, and collaborate on large interconnected datasets. Well-done visualization increases the amount of information a user can analyze beyond the normal four or five data points. It allows users to compare a greater range of information by easily adding dimensions or slices. By providing multiple different view of the information, the different perspectives allow a user to recognize things that otherwise would not be apparent.
Increasingly, organizations are using Business Intelligence to tell them what we do not know. Data Mining is the use of a variety tools and techniques to analyze large quantities of data to identify unknown patterns in the data. Data mining can be used to find new data groupings and clusters, exceptions and anomalies within the data, and unknown dependencies or associations. The “data ore” produced in data mining is typically brought back in for further analysis, rarely is there a “data nugget” that stands alone.
Leading organizations are using Business Intelligence to ask what will happen and answer what should we do? Predictive analytics is the use of a variety of techniques to methodically examine and investigate data and use what is learned to forecast what will happen in the future. Predictive models exploit patterns, which could be found through data mining, in historical and transactional data to identify both risk and opportunity.
I will follow up shortly with another post on where Business Intelligence is heading.
