Becoming a Data-Driven Organization: A 5-Part Framework for Sustainable Data Quality

In a previous post, we’ve established that data needs to be clean in order for organizations to make sound decisions, gain a competitive advantage, and improve the bottom line.

But, before jumping to fix your data issues, it’s important to establish a framework that ensures the data will be usable in the long run—not only immediately after a big cleanse, which is often time consuming and expensive. This 5-part framework provides a comprehensive approach for addressing existing data quality issues, and prevent issues from arising in the future.

5-Part Framework for Sustainable Data Quality

Let’s explore each step in detail.

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Data Is to Business as Water Is to Life

It goes without saying that data is critical to make strategic decisions, to run operations, and to perform business functions.

  • Healthcare companies derive analytics from clinical and claims data to meet quality measures, improve care, and better manage high-cost and high-risk populations.
  • Manufacturing companies rely on performance data to improve efficiency, increase yields, and lower costs.
  • Retailers rely on data to predict trends, forecast demand, and optimize pricing.
  • Financial services organizations perform advanced data analytics to drive revenue and margins through operational efficiency, risk management, and improved customer intimacy.

All of these scenarios require vast amounts of data. Regardless of industry or company size, nearly every business is relying on gathering and leveraging data. Being a data-driven organization is an absolute necessity to gain a competitive advantage.

IT is uniquely positioned to have access to a comprehensive set of data which is stored on or passes through the company’s infrastructure. IT, therefore, carries a responsibility to provide end users access to this data, and to play a vital role in its effective use.

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