Analytics has come a long way in a comparatively short period of time and is no longer limited to large businesses. Since today’s world has entered the age of analytics, it is now prevalent as more enterprises using it in some capacity. In a survey from Deloitte, 49 percent of respondents said analytics helps them in making better decisions, while 16 percent reported that it better enables key strategic initiatives. Conversely, 10 percent claimed it helps them improve relationships with both customers and business partners.
Business decisions also now are gradually based on data of such intricacy where humans struggle to interpret it. So, business leaders now must look into a more automated approach for how analysis is done and the more ways wherein it can be utilized. However, in the coming days, it will be difficult to integrate new capabilities into business operations and strategies as the volume of data continue to double due to information pours in from digital platforms, wireless sensors, virtual reality applications, and a vast number of mobile phones.
There is certainly not a deficiency of data availability but the quality of that data still much to be desired for companies. This is challenging because low-quality data, in some way, impact various areas of business performance. Particularly, it can convert into an imperfect customer or prospect data, wasted marketing and communications efforts, increased spending and, eventually, worse decision-making.
So, before seeing many benefits of data quality, companies need to assess it.
Measuring Data Quality
Data quality refers to the ability of a dataset that serves with an intended purpose. And several best practices can assist businesses to improve the quality of their data into the company’s overall success strategy.
Track the Ratio of Error Data
Tracking the number of known errors like missing, incomplete or redundant entries within a dataset can be helpful for businesses to leverage the quality of data. Once getting outcomes, the quality level of data should become obvious. But, if more than two-thirds of a company’s records have errors, mean data quality is hurting its performance and needs improvement.
Transformation of Data
Data transformation is a process of taking data that is stored in one format and moving it to a different format. It often causes data quality issues. By assessing the number of data transformation operations that fail or may take unacceptably long to complete, companies can gain insight into the overall quality of their data.
Data Storage Costs
Data storage costs is another potential sign of data quality issue. If a company is storing data without realizing it, it could harm overall business performance because the data has quality issues. On the other hand, if the company’s storage costs decline while its data operations remain the same or rise, they need to improve the data quality front.