Quality Data Drives Quality Business Decisions

Bizmology_BiesadaThis post originally appeared on Hoover’s Bizmology.com on April 16, 2014.

Garbage In, Garbage Out (or GIGO) is a phrase coined by computer scientists that means something produced from materials of low quality will also be of low quality. Conversely, quality input will result in quality output. Indeed, quality — specifically data quality — is a subject that occupies the mind of Paul Ballew, D&B’s Chief Economist, and Chief Data and Analytics Officer. Ballew and his team are charged with arming D&B customers with the quality data and predictive analytics they need to make smart business decisions.

Ballew emphasizes data quality and why it’s so important to businesses in this interview, especially in The Age of Big Data. “Data quality is a multidimensional issue for today’s organizations. It equates to having and leveraging insights that can move your business forward,” says Ballew, adding, “The pace is defined by the quality of the insight.”

Good decisions rest on a firm foundation. And quality data is that foundation. The four dimensions of “quality data” are: completeness, accuracy, depth of insight, and data latency. “Decision makers today need that level of completeness, accuracy, depth of insight, and timeliness to make good decisions,” asserts Ballew.

If that sounds like a tall order, it is for most organizations. The business world is littered with the missed opportunities and casualties that result from poor data quality and lack of data management. (Case in point: Apparently, J.C. Penney’s ex-CEO Ron Johnson either failed to gather or consult data that revealed how wedded customers were to periodic sales events before changing the store’s pricing strategy and alienating shoppers in droves.) Successful companies rationalize data to make fact-based decisions and increasingly are using new sources of data, such as social media, sentiment data, and transactional data, all of which are valuable sources of insight that can be leveraged for competitive advantage. “The bar has really gone up,” Ballew says. “Twenty years ago, having any data was sufficient. Now we’re looking for high-quality data across all these dimensions.”

Adding to the complexity is the fact that data degenerates over time: Phone numbers change or are disconnected, CEOs are hired and fired, and bankruptcies happen. As data decays, businesses can find themselves struggling with data governance and stewardship. Proactive companies look to tools, including CRM systems, supply chain management, and business intelligence partners, to get the most from their data. Ballew notes that while these are all necessary tools, “What’s perhaps more important is having a good data governance structure.”

Creating a first-rate data governance structure is itself a multidimensional process. First, businesses must identify what they’re trying to accomplish. Then they need to bring all their data and analytic assets to bear in an environment that’s responsible for those activities. “Finally,” says Ballew, “it’s important to go beyond your data and analytics team. You should have your users be involved in the process, and that includes folks from marketing, finance, and IT.”

Creating and managing an integrated data program is a team effort, and a complex one at that. But the result can be summed up very simply: Base your decisions on quality data and they will be good decisions.

Posted in Big Data, Data Quality by Alexandra Biesada

Alexandra Biesada is an editor at D&B.

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