What is Data Quality and Why is it Important?
The availability of enormous amounts of data comes with one major downside: management difficulty. So much information is being pumped in that finding the crucial bits and working on their quality is extremely difficult.
The quality of the data you have will be reflected in the business decisions you make both in the short run and in the long run.
Data quality will make or break your business, as the insights you get from it dictate the business moves you make. The higher the quality of data a company has in its hands, the better the results its campaign strategies are going to produce.
In a word, data quality is the whole multi-faceted process of styling data to align it with the needs of business users. A business can optimize its performances and promote user faith in its systems by working to improve the following six metrics of data quality:
Accuracy
Consistency
Completeness
Uniqueness
Timeliness
Validity
Bad data are inaccurate, unreliable, unsecured, static, uncontrolled, noncompliant, and dormant. While poor data can be a significant threat to data-driven brands, from another angle, it can be seen as a market gap and an opportunity for businesses to improve. Let’s take the example of a self-driving vehicle that makes use of artificial intelligence (AI) and machine learning to find directions, read signs, and maneuver streets. If the car lulls the user into driving into a traffic snarl-up, we can say that the data that led to that is inaccurate and unreliable. This will take a toll on the car maker’s reputation, especially if it happens to more than one person. They must be quick to redress the issue, or it will ultimately cripple the company and create an opportunity for rival businesses to rise and fill the void.
