3 best practices for improving and maintaining data quality
To excel in business today, organizations are increasingly relying on insights generated by data analysis. And they realize that insights are only as good as the data they come from. More data does not mean better insights. It’s not the quantity, but the quality of data that impacts the value of insights.
KPMG’s 2016 Global CEO Outlook highlights that 84 percent of CEOs are concerned about the quality of the data they’re basing decisions on. As business decisions are very much affected by the quality of data, what they need is a reliable data foundation that always assures high data quality.
Today’s economy is completely data-driven. Customers generate enormous amounts of data at every point of interaction. Businesses use enterprise-wide systems and third-party tools that again push more data. The volume, variety, and velocity of data available to companies increases by the hour. What matters in the end is how this data is used effectively.
Data helps businesses profile their customers, communicate well with them, and present them the right products at the right time. Data helps improve business operations and make them efficient. Moreover, companies always explore ways to leverage data to generate more revenue and increase their profitability.
Data today can indeed help businesses stand out from the crowd. But only when that data is reliable, updated, and of high quality.
Data quality is the degree to which completeness, validity, accuracy, consistency, availability and timeliness of data fulfills requirements appropriate for a specific use. In short, a measure of how fit the data is for the intended use.
But it’s never easy. Data quality is one of the biggest challenges every company faces. The high volume and complexity of data collected across multiple sources creates an enormous task for companies when they want to manage data quality.
Issues in data quality directly affect downstream processes, revenue, compliance, and analytics; while missed opportunities and higher operational costs are also often attributed to poor quality of data. Managing the quality of data is imperative for complete data governance as well as regulatory compliance.
Quality data ensures confidence in the analysis and generated insights. If companies want to lead, they need to be one step ahead of the competition, and that can be achieved only with reliable, actionable insights.
Poor data quality makes extensive impact on business including wrong product delivery, off the mark forecasts, inadequate planning, rework, poor customer experience and loss of reputation.
Most of the factors affecting data quality are the defining elements such as accuracy, completeness and consistency. In the case of healthcare services, for example, inaccurate patient information and health records lead to adverse health outcomes. For retail business, inconsistency in the customer contact details not only creates delivery issues and customer complaints but also misses marketing opportunities.
For all data, validity is always crucial. If data is not validated against the defined parameters such as format, range, and source, it is as good as absent.
