Data quality has become increasingly critical in recent years. The processing of large amounts of data linked with business operations has increased, and more business leaders are choosing to use data analytics to help drive their business decisions.
Data quality management is vital to the overall data management process. It is also closely tied to data governance programmes aiming to ensure data is formatted consistently and used throughout businesses.
What is data quality?
The Government Data Quality Hub defines data quality as “good quality data that is fit for purpose.” The definition of the quality of data is based on such things as accuracy, completeness, consistency, reliability and being up-to-date.
Why is data quality so important?
Basing business decisions on poor-quality data can have catastrophic outcomes for businesses. Data quality issues can cause problems such as:
- added expense when products are shipped to the wrong customer
- fines for improper financial or regulatory compliance reporting
- lost sales due to incomplete customer records
An estimate by IBM in 2016 stated that poor data quality costs US businesses around $3.1 trillion annually! In 2017, data quality consultant Thomas Redman said, “bad data costs companies 15% to 25% of their annual revenue on average.”
Research has also shown that a lack of trust in data quality among business leaders has led to a slow take-up of business intelligence and data analytics tools to help improve business decision-making.
What are the traits of good data quality?
In our recent blog post “5 key traits within data quality,” we mentioned that there are five key traits of data quality that businesses need to be aware of, and these are:
- Accuracy: is every detail of the information correct?
- Completeness: is the information comprehensive?
- Reliability: is the information contradictory to other trusted resources?
- Relevance: is this information critical to your business?
- Timeliness: is this information up-to-date, and could it be used for real-time reporting?
How is data quality determined?
Various steps are involved in determining data quality. The first step invariably undertaking a data asset inventory to measure the accuracy of the data being collected against a baseline study.
The next common step is creating data quality rules for analytics and operational data based on your business requirements. Once these rules have been established, you can complete a data quality assessment to ensure you maintain the highest data quality levels possible.
One well-known example of data quality rules is the “Data Quality Assessment Framework (DQAF)” that has been put into place by the International Monetary Fund (IMF). This framework focuses on accuracy, consistency and reliability in other countries’ statistical data submitted to the IMF.
What are the benefits of good data quality?
There are many benefits associated with good data quality within an organisation, and these include:
- A reduction in the cost of identifying and fixing bad data within a system
- An improvement in the accuracy of data analytics applications leads to better business decisions
- An expansion in the use of data analytics and BI tools also leads to better business decisions
- Freeing up the data management team to focus on more productive tasks than cleaning up data sets
If you want to access the benefits of good data quality by learning how Agile Recruit can help you shore up your data quality team, or are looking for your next data quality job, contact us today.