[Blog] What is data quality and why is it important?

Data quality is a critical component of any business. It can mean the difference between making or losing money, whether you are sending an email to the right person, or if you are sending it at the right time. There are many solutions on the market today that allow companies to organize their data into logical structures in order to manage it correctly.

I guess you have heard the expression, GAGO - "Garbage in, garbage out". Maybe a similar one, but the logic behind is that when you put bad data into your systems, you will end up with a similar result. A bad result; inaccurate reports, sending wrong invoices to the wrong clients, spending time contacting the wrong person.

Inaccurate reports leads to wrong decisions or at least decisions based on wrong and inaccurate information. Probably needless to say, but wrong invoicing leads to unhappy clients and lost revenue combined with increased costs of business operations and having wrong contact information means wasting time and worst case sending confidential information to the wrong people.



What is data quality?

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 data quality 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...

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

Data left without continuous monitoring become polluted over time.

Data that is not updated becomes obsolete and useless.

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

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

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