In this age of digital revolution, organizations deal with large volume of data and information emanating from various sources. To maintain a competitive advantage, gain insights and make informed decisions, it becomes necessary to make proper use of the data. However, with increased data generation, the issue of data quality and veracity also becomes important. That’s why data quality management (DQM) has become one of the topmost priorities of business executives today.
DQM deals with the acquisition of data, implementation of data processes, distribution of data and overall monitoring of the acquired data. DQM prepares organizations to face the modern digital age challenges effectively. The key to data-driven analysis is provided by DQM.
Big data has accentuated the pace of the digital revolution. Moreover, with increased automation, there has been a large influx of unstructured data. These open up new avenues of potential revenue sources to be exploited. To harness these pool of unstructured data, a far-reaching DQM policy is required. Unstructured data sources are aggravating the need for a better DQM.
Moreover, the benefits of DQM has a trickle-down effect on the company’s overall performance. High-quality data will help to understand the customers better and gauge the impact of targeted marketing strategies. Better quality data will lead to the more accurate targeting of potential customers, which in turn will increase customer acquisition and retention.
However, like other breakthrough technologies along with new applications, the digital age has brought with it the ominous effects of poor data quality. Analysis and Insights derived from poor-quality data are of little value to the decision-makers. In fact, instead of facilitating decision-making, such analysis may result in misleading decisions. Moreover, analysis of poor-quality data is a waste of resources.
Poor data quality leads to ineffective optimization of resources, poor decision-making, deceptive perception about customers and lower prospective ROI on businesses. A Gartner study reveals that the surveyed organizations incurred a loss of on an average US$14.2 million owing to poor data quality. So, the cost of poor data quality is very high. The root of all these problems is substandard data quality and the solution is data quality management.
