What is Data Quality? 

            Data Quality, in its most fundamental definition, is a metric by which the value of your data to your enterprise can be measured.  Data Quality though, is also an actionable philosophy, as Data Quality can be manipulated up or down, whereby it increases or decreases the value of the data upon which it acts accordingly. 

            Data Quality - by its close association with the true value (vs. percieved value) and usability of a company’s data – is an integral component of ROI determination and feasibility of the various uses to which the data is put; i.e. marketing, business intelligence, and so forth. 

            Since Data Quality is both a measurement and a process; manipulating Data Quality levels will result in a measurable change in the value of any initiative for which the data is used.  And since Data Quality is a constantly moving target, a permanent - albeit flexible - proactive program must be in place at the enterprise level to ensure that Data Quality in the organization is maintained at a predictable and reliable level.

           Since Data Quality - in it's simplest definition -is a measurement of the value of a specific set of data, utilized in a specific manner, towards specific goals – then the levels of Data Quality attainable are intractably tied to the specificities of the data itself.  Simply put; there is no simple, pre-canned approach to Data Quality that will work in all cases.  In fact, any such one-size-fits-all  approach is doomed to mediocrity at best, and outright failure at worst, in the majority of cases where such an approach is applied. 

            To put it another way, initiatives intended to measure and positively affect Data Quality must be designed and implemented with as much individuality and specific purpose as was employed in the planning and collecting of the data in the first place.  No two data sets are exactly the same.  No two companies collect, maintain, and utilize data in exactly the same way.  Thus, since the Data Quality initiative operates on a highly unique entity – that is, the data – it must also be unique and individualized.  Any attempt to address Data Quality issues in your organization with a one-size-fits-all approach will never achieve the highest levels of Data Quality possible for your particular situation. 

A true Data Quality program should rest on three basic pillars – Standardization, Validation, and Enhancement.