Data is now a core part of almost every business operation. The quality of the data that is gathered, stored, and used for business processes will determine the success achieved in doing business.
The Data is said to be of high quality, if the data is fit for the intended purpose of the use or if the data correctly represent the real-world construct, that the data describes.
If organizations consistently achieve high-quality data, there are high chances of being positioned better to make strategic business decisions that yield valuable business insights.
Several best practices for ensuring and improving the quality of data have emerged and this article highlights the most important ones.
1. Establish metrics
By establishing the metrics that are relevant and applicable for a goal or a target aforehand helps in improving the quality
Measuring quality is essential to:
- For a better understanding of how accurate your data is
- Determining missing, incomplete, or inconsistent data
- Quality data enable you to take corrective action to improve the quality
2. Investigate your failures
According to Experian’s 2019 Global data management research, the top causes of inaccurate data are human error, too many data sources, and lack of communication between departments.
Failing to investigate previous data failures may lead to the continuous occurrence of the same errors all the time which reflects a bad quality of data.
Once the cause of the error is recognized, you can take action to prevent similar errors in the future. Identifying and correcting the errors might be difficult and time taking, but once done, the quality of the data improves.
3.Invest in internal training
This could be a game-changer too. Attaining good data quality requires deeper understanding and expertise which is not easy for an entry-level executive to understand. This particular knowledge is best obtained through formal training.
Encourage your teams and executives to learn:
- Basic concepts, principles, and practices of quality management
- How quality management principles are applied to data
- How to think through both the benefits of high-quality data and the costs of poor quality
- The key principles in building data quality organizations
- The quality challenges that are inherent in data integration
4.Implement a data governance strategy
Data governance is the key to maintain consistent quality of the data in procedures as well as regulatory compliance. Even the smallest organizations should make sure that people and processes are in place.
Every organization should focus on establishing a set of data governance guidelines specific to their processes and use cases.
Avoiding the duplication of effort with a business should be a key goal.
You might not be able to afford to have different departments using different tools and strategies or silos of data that don’t talk to each other. This affects your external brand as well as your internal costs because customers increasingly expect to have a seamless experience across your entire organization.
5.Establish a data auditing process
With great value in quality, implementing processes to create and maintain it plays a critical role.
Two questions to be answered are:
- How do we know that those processes are effective?
- How do we gain the trust of others that the quality of our data is good?
Performing data audits within data repositories are the best way to build trust in the data. The data audit process should check for any cases of poor quality of data including but not limited to:
- Poorly populated fields
- Incomplete data
- Inaccuracies
- Inconsistencies in formatting
- Duplicate entries
- Outdated entries
The frequency of audits is important to the acceptance and success of the data audit process.
Auditing once a year may not help as it takes a very long time to find, correct, and investigate a full year’s worth of errors.
Automating the audits continuously with a specific time period could help in early detection.
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