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The Answer to “What is Data Quality?” Couldn’t Be More Timely

The Data Differentiator Infographic

Businesses everywhere are considering, testing, or adopting today’s emerging capabilities such as “AI”, blockchain, and predictive analytics for competitive gain, organizational efficiencies, and tech investment time to value.

For the business and data science professionals who are perfecting their organizations’ best formulas for data and technology synergies, data quality becomes paramount. It’s critical as they treat data as a high-value asset, building strategies and platforms to ingest, reason with, distribute, and present unprecedented business insights across their organizations for countless business use cases.

The Data Differentiator: How Improving Data Quality Improves Business, a report (see below) commissioned by Pitney Bowes and prepared by Forbes Insight, delves deeply into why and how data quality is vital for today’s organizations looking to reap the promised business gains from fully leveraging inextricably connected data and technology.

For those who live and breathe data, determining the best data sources to use based on the needs of the organization can be tricky. In this infographic, Anthony Scriffignano, Ph.D., Dun & Bradstreet’s Chief Data Scientist, shares his perspective. Leading a team of data scientists focused on advancing Dun & Bradstreet’s strategic thinking around data and related IP – as tens of thousands of customers around the world constantly use our data and analytics to make trusted data-driven business decisions – Dr. Scriffignano uniquely understands why data quality is mission critical.

When asked how companies can successfully differentiate between data sources and their appropriateness for a given purpose, Dr. Scriffignano replied with the deep knowledge of a data scientist responsible for one of the world’s largest, constantly changing commercial databases:

“Imagine a series of blocks named discovery, curation, synthesis, fabrication, and delivery. Those are the five steps in the mental model that I use, and then alongside those, quality assurance and governance. Every one of those steps has a quality assurance step and a governance step.”

ROI gains become more likely when co-dependent data and technology have equal footing. Data quality therefore stands to be the differentiator for organizations who make it a business imperative.

Learn more about data quality from leading data experts in The Data Differentiator: How Improving Data Quality improves Business.

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