IBM recently staged a coming of age party for information governance. Information governance is nothing new for the company, its latest announcement simply heightens its growing emphasis. IBM, along with some other vendors, is increasing the drumbeat for a very good reason: governance is critical to leveraging information as effectively as possible. Since many enterprises are still unclear on what information governance is--let alone its benefits and how to do it--putting an information governance strategy in place is not intuitively obvious. But before we discuss the subject more fully, a little history lesson is appropriate to put in context how we have come to the need for a formal information governance strategy and program.
Putting the Use of IT for Decision Making in Perspective
In the dawn of modern data processing, when mainframes were synonymous with computing and the term "data processing" as an organizational name was not considered sufficient, IT organizations were often called management information systems (MIS). However, the reality was that those business systems (we are not examining the scientific track), such as accounts payable, accounts receivable and bill of materials, were clerical information systems and not management information systems. They implemented critical operational processes, but were, at most, used only indirectly for making management decisions. So MIS was shortened to IS and then later to IT to give a broader perspective.
But even though business applications have exploded far beyond structured (database-residing) transactional processing to include both semi-structured information (such as e-mails and word processing documents) and unstructured (bit-mapped) information, their emphasis still, for the most part, is on day-to-day transaction processing.
However, there has always been a parallel track to traditional business systems that tried to aid the management decision-making process. That track incorporated a small fraction of IT focusing on very narrow projects through the use of operations research (economic lot size, regression analysis, simulation modeling), decision support systems (DSS) and executive information systems (EIS). Those projects had very targeted objectives with the use of targeted sets of data.
The next step forward--data warehousing--involved a major leap of faith. Huge amounts of transactional processing data that had only been used for operational purposes was now collected, transformed in a number of ways, and stored in a large repository called a data warehouse. The leap in faith occurred because all of the uses of the data in the data warehouse could not be predicted a priori so it was not certain that the large investment in data warehousing would pay off.
Overall, though, taking that leap of faith seems to have been rewarded because at the same time the sophistication of analytical tools, such as data mining and multidimensional cube analysis, came into play. The whole field of business intelligence (BI) has evolved rapidly since then.