Catch Bad Data Before It Wrecks Your Business
Basically, by merging a number of data sets together, all records can be enriched as a byproduct of exposed transitive relationships. We can add to this another tool: approximate matching. This matching technique allows for two values to be compared with a numeric score that indicates the degree to which the values are similar. This is particularly valuable, because the exposure of embedded knowledge can, in turn, contribute to our other enhancement techniques for cleansing, enrichment and merge/purge, ultimately improving business value.
6. Achieving Proactive Data Quality Management
Standardizing the approaches and methods used for reviewing data errors, performing root cause analysis, and designing and applying corrective or remedial measures all help ratchet up an organization's data quality maturity a notch or two. This is particularly effective when fixing the processes that allow data errors to be introduced in the first place, totally eliminating errors altogether.
When the root cause is not feasibly addressed, we still have another standardized approach—definition of data validity rules that can be incorporated into probe points to monitor compliance with expectations and alert a data steward as early as possible when invalid data is recognized. This certainly reduces the "reactive culture," and better governs data stewardship activities. In fact, many organizations consider this level of maturity as proactive in data quality management because they are anticipating the need to address new issues on an ongoing basis.
Many organizations are looking at drastically increasing their consumption of information with "big data" analytics programs. At the same time, people are exploring many different ways to reuse and repurpose data for both operational and strategic benefit. However, to truly be proactive, companies must anticipate the types of errors that they don't already know. Instead of only using profiling tools to look for existing patterns and errors, they might use these analytical tools to understand the methods and channels through which any potential errors could occur. The true proactive win is to control the introduction of flawed data before it ever leads to any material impact.
Greg Brown is vice president of Melissa, provider of global contact data quality and identity verification solutions that span the entire data quality lifecycle and integrate into CRM, e-commerce, master data management and Big Data platforms. Connect with Greg at email@example.com or via LinkedIn.