In Analytics, Visualization

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You may feel that the quality of your data is insufficient for driving decisions and actions using analytics or situational intelligence solutions.  Or, you may in fact know that there are data quality issues with some or all of your data.  Based on such feelings or knowledge, you may be inclined to delay an analytics or situational intelligence implementation until you complete a data quality improvement project.

However, consider not only the impact of delaying the benefits and value of analytics , but also that you can actually move forward with your current data and achieve early and ongoing successes. Data quality and analytics projects can be done holistically or in parallel.

“How?” you ask. Consider these points:

  • Some analytics identify anomalies and irregularities in the input data. This, in turn, helps you in your efforts to cleanse your data.
  • Some analytics, whether in a point solution or within a situational intelligence solution, recognize and disregard anomalous data. In other words, data that is suspect or blatantly erroneous will not be used, so the output and results will not be skewed or tainted (see this related post for a discussion about: “The Relationship Between Analytics and Situational Intelligence“). This ability renders data quality a moot point.
  • It is a best practice to pilot an analytics solution prior to actual production use. This allows you to review and validate the output and results of analytics before widespread implementation and adoption. Pilot output or results that are suspect or nonsensical can then be used to trace irregularities in the input data.  This process can  play an integral part in cleansing your data.
  • Some analytics not only identify data quality issues but also calculate a data quality score that relates to the accuracy and confidence of the output and results of the analytics. End-users can therefore apply judgement if and how to use the output, results, recommendations, etc. Results with low data quality scores point to where data quality can and should be improved.
  • Visualization is a powerful tool within analytics to spot erroneous data. Errors and outliers that are buried in tables of data stand out when place in a chart, map or other intuitive visualization.

You can be pleasantly surprised at how much success you can achieve using data that has not been reviewed, scrubbed or cleansed. So set aside your concerns and fears that your analytics or situational intelligence implementation will fail or have limited success if you do not first resolve data quality issues.

Instead, flip such thinking around and use analytics as one of the methods to review and rectify data quality.  In other words, integrating analytics into your efforts to assess and cleans your data is a great way to leverage your investment in analytics and get started sooner rather than later.

What are you waiting for?  Get started exploring and deriving value from your data no matter the status of its quality.

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