By David Loshin

  Most business applications are originally designed to serve a specific purpose, and
  consequently, the amount of data either collected or created by any specific application is
  typically just enough to get the specific job done. In this case, the data is utilized for the
  specific intent, and we’d say that the “degree of utility” is limited to that single business application.

On the other hand, business often use data created by one application to support another application. As a simple example, customer location data (such as ZIP codes) that is collected at many retail points of sale is used later by the retail business to analyze customer profiles and characteristics by geographical region. In these kinds of scenarios, the degree of utility of the data is increased, since the data values are used for more than one purpose.

In fact, data sets are constantly targeted for repurposing, but one challenge that emerges is that sometimes the data that is collected or created is not of sufficient quality for the secondary uses. Errors, missing values, misfielded data, or any number of other data flaw detract from its potential utility.

Fortunately, many of these data flaws are easily addressed through data enhancement, which (informally) is a process that adds information to a data set to improve its potential utility.

There are a number of different ways that data sets can be enhanced, including adapting values to meet defined standards, applying data corrections, and adding additional attributes. In the next few posts we will look at scenarios in which specific data enhancements help businesses to improve value along a variety of value drivers.

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