Credit Management
How do you know if you’re using quality data? By Roni Millard MICM*
Roni Millard MICM
Wherever there is data, there are answers. But there are right answers, and there are wrong answers. Good-quality data can be relied upon to give valuable insights for better decision making, but the same can’t be said of low-quality data. Data that is incomplete, dated or not fit for purpose can result in inaccurate or misleading outcomes. A business decision based on flawed information can alienate customers and cost organisations dearly in lost revenue, missed opportunities and compensatory overspending. Estimates by Gartner1 place the average cost to organisations of poor data quality at $12.9 million each year. Bad data equals wrong answers. Data quality is a gamechanger for building competitive advantage in today’s digital, customer-driven economy. With more data available than ever, it is a business imperative to find value in this sea of information. In the financial services industry, the harnessing of newly available income and expenditure data, for example, offers the opportunity to improve credit risk and segment customer bases to deliver tailored products for greater brand loyalty and financial literacy. When the Economist2 announced in 2017 that the world’s most valuable resource is no
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longer oil but data, organisations were already leveraging data to understand their customers, predict future trends, and unlock new revenue systems to drive business growth. Five years later, there can be little doubt that organisations who don’t prioritise the accuracy, completeness, consistency, credibility and currentness of data will be left behind in the global race to be data-driven. So how can businesses increase their data maturity? A useful starting point is to find out more about the reliability of the data that informs your insights. When seeking to partner with a thirdparty provider, understand that data quality is not the same from one provider to the next. Be sure to ask these three qualifying questions at minimum to help compare data integrity between different providers.
1. What is the depth and detail of data sources? Variety matters when it comes to data quality. The more depth of data and breadth of data sources, the better. When sources are limited, and datasets lack relevant detail, there’s a good chance that part of the data story is missing. This lack of clarity may lead to inaccurate conclusions. Diversity and breadth of data
