The leaders of big organizations, especially businesses, can pay a terrible price for driving forward while gazing in the rear-view mirror. Many who did this at the turn of the millennium raced headlong into the Permafrost Economy on the fuel of wishful thinking and soothing official lullabies. Meanwhile, data in their own systems could have shown them both the coming chill and actions likely to buffer it. That is, the data could have if these leaders had invested in a software category called BA (business analytics).
A term that first appeared in 1997, BA was quickly muddied by overzealous marketers and clueless pundits, usually by confusing BA with BI (business intelligence).
From BI to BA
BA software expresses a broad range of applications and designs. Typically it sucks up vast quantities of data stored in data warehouses and complementary sources, then runs advanced math and statistical operations on it in search of relationships. Unlike BI, BA software can examine every possible interaction, sometimes finding relationships analysts wouldn’t have seen if the software was limiting the fields it imported. Most clients deliver a mixture of graphic and numeric windows for visualization, support the graphics to spot and select attractive exceptions, and then iteratively run tests on the selected items.
Too many otherwise intelligent business managers get distracted by BI when they should be paying attention to what BA can tell them, says Herb Edelstein, founder of the Data Warehouse Institute and president of Two Crows, a consultancy.
“BI practice is a global name for all kinds of backwards-looking querying and reporting. Sometimes the tools are very intelligent, and usually they’re attached to a data warehouse,” Edelstein says. “The past is useful and necessary, but too many people use reporting to look backwards, not to ask the question, How do we handle our business in the future?
BI’s binding to the past and BA’s focus on the future points out the essential difference between the tools, explains Anne Milley, the SAS Institute’s director of analytical intelligence.
“It doesn’t matter how many different reports you have if they are pre-built,” Milley says. “Good analysis requires follow-up … not just ‘how much’ and ‘how many,’ but an attempt to understand ‘why.’ Analytics tools are iterative and interactive. It’s not just presented data. BA tools exist for exploring directions and finding answers. It’s a platform for continuous learning.”
In traditional shops with BI, the responsibility for analysis falls on experts armed with the delivery of whatever reports and OLAP manipulations they can execute. The business analysis goes on in an expert’s cranium. The questions that experts trigger are based on their presumptions, making it unlikely they will discover a relationship or trend they hadn’t previously considered.
“With BI, you see one report, or one graph, at a time. With BA you get every possible report evaluated, and then it delivers you the most relevant ones,” says Joerg Rathenberg, senior director of marketing at BA software vendor Kxen.
The mechanics of OLAP cubes looking into hundreds of field’s worth of data usually overwhelms researchers’ systems, impelling them to exclude fields. This radically reduces the computing requirement but also radically reduces the possibility of uncovering subtle interactions among dozens of factors. The human-only process of business analytics will miss some real trends.