Data Quality Coverage and Readiness Part 1

How to Assess Data Quality Readiness for Modern Data Pipelines

Photo by Mike Cohen on Flickr

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For growth-minded organizations, the ability to effectively respond to market conditions, competitive pressures, and customer expectations is dependent on one key asset — Data. But having just massive troves of data isn’t enough. The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses — a figure that can cripple most companies. Unfortunately, ensuring — and maintaining — data quality can be incredibly difficult. This is being exacerbated by the data architecture choices of an organization. Legacy architectures often lack the ability to scale to support the ever-increasing volumes of real-time data and cause data silos that slow the necessary democratization of data needed for an entire organization to benefit from.

Now more than ever it’s critical that the highest quality and reliable data drive business decisions. But what is the best approach to ensuring this? Do you need to improve your data quality implementation? And where should you start and what quality metrics should you focus on? This two-part blog series provides a step-by-step guide to help you decide for yourself where your organization stands from a data quality readiness standpoint.

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