Data quality is ever important and most people probably can understand the logic of "waste in, waste out". Therefore, it is not a surprise that it is important to have focus on the data quality that you use for generating insight and use as foundation for decisions to develop your business.

 

Data quality

 

Data quality is measured by six factors; accuracy, completeness, consistency, timeliness, uniqueness and validity. Data Quality management is an exercise of conditioning data to meet these factors.
Regardless of size, any organization needs to pay attention to data quality to understand both internal and external factors and make sound decisions.

 

Is having quality data a trend?

It can be discussed if focusing on data quality is a trend, but it has topped the report on BI and analytics trends by BARC the past four years (2021). Having quality insights has been crucial since using data to drive decisions. However, the emphasis on better data management and quality showed today could be seen as an expression that most organizations still have not reached their goal.

There is no doubt that data quality and effective data management are a long-term mission that demands a structured and automated process to be efficient.

 

Bad data is costly

Data underpins every process, and data quality rules should consider the value that data can provide to an organization. The higher value or risk data has in a specific context, the more rigorous data quality rules are required. Therefore, companies must agree on data quality standards based not only on the dimensions themselves — and, of course, any external requirements or standards that data quality must meet — but also on the impact of not meeting them. 

 

A study from MIT shows that bad data quality costs 15% to 25% of most companies' revenue. That should make any business leader think, is the data we rely on good enough? And who is responsible for the quality in my organization? - We can reveal that the answer to the last questions is = everyone is.

 

 
Bad data is...

Inaccurate
Data that contains misspellings, wrong numbers, missing information, and blank fields yields invalid analytics.

Uncontrolled
Data left without continuous monitoring become polluted over time.

Static
Data that is not updated becomes obsolete and useless.

Noncompliant
Data that does not meet regulatory standards exposes organizations to penalties.

Unsecured
Data left without controls and correct governance becomes vulnerable to attack by hackers.

Dormant
Data that is left inactive and unused in a repository loses its value as it is neither updated nor shared.