Using Master Reference Data to Unify Your Business
A Dataflux whitepaper
Information and Value There is no doubt that both information technology (IT) and business professionals believe that information has value – otherwise we would not spend millions of dollars each year capturing, storing, archiving, backing up, restoring, using, and generally managing data. But unlike other seemingly ethereal concepts that have calculable amounts inserted into an organization’s financial balance sheet (e.g., goodwill), there are no commonly accepted accounting practices for measuring and assigning a value to data.
And while we are continually bombarded with ideas about how the benefits of successful Business Intelligence (BI) applications can add significant value to the bottom line, it is easy to forget that there are numerous bumps in the road to BI success.
Consider that: • In 2001, analysts at Giga warned that “Seven out of 10 customer relationship management (CRM) initiatives will fail” over the succeeding 18 months.1 • In 2005, Gartner analysts were quoted in an article indicating that “More than 50 percent of data warehouse projects during the next two years are doomed to outright failure or have limited acceptance” due to a lack of attention to data quality issues.2 • An oft-repeated benchmark is that 70% of the effort of building a data warehouse involves data extraction and integration. Clearly, attempting to extract value from information through a BI implementation is challenging. Yet the value of information may be increased even in the absence of a data warehouse or BI program. For example, many top executives would be hard-pressed to be able to answer the simple question, “How many customers does your company have?” using anything other than a ballpark estimate.
However, simple questions like these are critical to effective operations, reporting, and business productivity metrics. Because information is used for multiple purposes for the benefit of the organization, gaining a basic understanding of how data is used, shared, and exploited throughout the enterprise is the first step in information value improvement. In this white paper, we will explore how to get back to the basics to establish a fundamental, holistic view of enterprise information and then outline the case for information value improvement via master reference data management. This approach provides for semantic convergence, or the consolidation of both the metadata and the business meanings associated with enterprise information. Semantic convergence encompasses the knowledge embedded within the data and its “connectivity network,” and describes the processes and tools needed to pull it all together into a shared meta-knowledge repository.
Get the complete Dataflux whitepaper here