In his bestselling book How to Become CEO (Hyperion, 1998), author Jeffrey Fox described how one of the U.S. automakers, under mounting pressure for fuel efficiency during the 1970s energy crisis, called on its engineers to redesign its cars to be lighter. But the seasoned, veteran engineers argued that doing so was unsafe, impractical and impossible. So, the automaker brought in younger, newly minted engineers who unceremoniously proceeded to shed hundreds of pounds off the cars without negatively impacting safety. Unlike the longtime engineers, the newer engineers succeeded because they were not constrained by preconceptions as to what was possible. Essentially, they succeeded because they didn't know any better!
Similarly, organizations are long overdue for a fresh look at how they can provide enterprise-wide business intelligence (BI), unbiased by long-held misconceptions as to what a BI implementation “must” entail.
The critical task of providing actionable enterprise data often falls victim to the same limited thinking displayed by those veteran automobile engineers, particularly when such flawed logic is applied to admirable but imprudent efforts to implement pervasive BI across an organization. Many IT professionals mistakenly believe the only way to get enterprise BI into the hands of those who need it is to implement complex BI tools supported by even more complex data management systems. Too often, these solutions prove to be overkill, placing undue burdens on the IT department and forcing companies to incur hefty, avoidable costs.
As BI expert Colin White has observed, disillusionment with existing BI software is already well under way. Some companies are starting to rebel, demanding easier and cheaper BI solutions. These companies, White notes, typically have fewer IT resources and skills necessary to implement BI projects. As a result, they may be struggling to implement even basic BI capabilities.1
This demand for “disruptive” solutions challenges the long-held misconceptions about BI. Companies today must pay new attention to the overlooked unstructured sources of actionable enterprise data, which can dramatically simplify the demands of providing the right information to the right person at the right time.
Unstructured Data: Separating Value from Hype
Traditional BI systems typically rely on structured information (databases), which usually comprises only a small portion of all enterprise information. However, the vast majority of enterprise information is found in unstructured data sources; that is, information sources outside of databases. Much hype has been devoted to the concept of harvesting unstructured data, while technology writers still disagree about what unstructured data is and whether it has any value at all.
Experts have a wide range of definitions for unstructured data. Some include any sort of potentially informative document, even hand-written notes. Others dismiss unstructured data as an “oxymoron” and as a “gibberish” term that is “not helping anyone solve their data problems.”2 The debate reveals an important kernel of truth: For an unstructured data source to be of authentic value as an enterprise BI source, the process of transforming a certain unstructured data source into actionable structured data must be very simple and reliable, without exception.
This key criterion eliminates the distraction of more exotic unstructured data sources, such as freeform text, handwritten notes, etc., and enables us to recognize that the single most valuable supply of unstructured data in the enterprise is the existing reports and business documents already produced by every organization.
Organizations running enterprise resource planning (ERP) systems, for example, already own a library of existing “canned” reports. All of the work is already done for them – there is no coding to do, no security to work out, and all the information that they need is embedded within the delivered standard report.3 For example, SAP offers canned reports numbering in the several thousands.
Organizations maintaining a huge historical database of an ERP (or other core system) often face a frustrating reality: most of the effort and cost associated with their complex BI tool is intended to allow the organization to create and work with customized views of the very same enterprise data that already appears within various existing ERP report outputs.
