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Poor Data Quality - Increases Risk of Business Failure



This post discusses the need for businesses to make data quality a board room discussion and should be part of the business strategy. Data is what underpins the value of any digital or technological investment and the need to get the correct high quality data is critical - getting it wrong has shown to have a diametrical opposite effect, increasing costs and resulting in wrong decision making with fatal consequences.


In addition data quality has shown to be a key link to successful digital adoption and improved efficacy. This post concludes by emphasising the importance of creating a objective risk framework that focuses on developing better benchmarks for data quality.


What is Data Quality?

On a basic level, Poor quality data is not fit for purpose, is inaccurate, incomplete, or out of date data . Poor quality data increases risk and can cost you time and money. It also falls short of GDPR rules this highlights the far reaching consequences in a business of poor quality data.


75% of businesses waste an average of 14% of revenue on bad data quality which equates to £197.788m every year!

From a technical perspective, poor quality data slows down the entire data architecture, caused by poorly structured data in any storage space such as cloud, warehouse or lake infrastructure. Duplication of data can mean increased storage capacity and cause systems to have errors. Data not having correct values is also one of the key reasons AI or Automation technology do not deliver correct results. The long tail effect is bad experiences by staff members and customers - having negative impact on retention and growth.