Every large or small business needs to handle voluminous data at different levels every single day. They need to filter and maintain the quality of this data for the smooth functioning of their organization. Governing your business data is a continuous quality improvement process. You can’t afford blunders in business decisions just because of insufficient, inaccurate or incorrect data. That’s why the maintenance of data quality is so important. Data quality management helps organizations adhere to their quality standards as well as streamlines and integrates the whole business process.
Businesses follow various strategies to maintain data quality. It’s better to prevent data errors rather than face data failures later. Data quality management is a cyclic process and should be implemented step-by-step. These steps help in standardizing the data management practices used by various enterprises. In this article we have covered the 5 best practices of effective data quality management here:
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Best Practices of Effective Data Quality Management
1. Establishing Metrics
In order to improve the quality of data over time, organizations need to measure their data quality. The metrics are used to measure data quality. Data quality metrics include information such as the number of incomplete or redundant data entries or the amount of data that cannot be analyzed due to formatting incompatibilities. Organizations may use different data metrics for assessing their data quality.
2. Performing Data Quality Analysis
Poor data quality may lead to disruption in the normal data functioning from time to time. For example, you’re unable to import data into Hadoop because of formatting problems or you delivered the marketing material to the wrong set of people. All of these are data quality errors. It is important to be equipped to deal with such problems. But equally important is to try to understand the underlying cause of the issue so that such errors don’t recur. Do a detailed post-mortem of the problem so that similar problems never occur again.
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3. Educating Your Organization
Organizations deal with data at different levels and every employee has a role to play in data management. Everyone works with data in one way or another such as
- Administrative assistants entering manual data in an appointment book.
- IT personnel making decisions about which machine data logs to keep and where to store them.
- Marketers designing websites that automatically collect data about customers.
All these people and others in an organization should be educated in the basics of data quality. The data collected should be fit to serve the goals they intend to meet with them. They should know the importance of avoiding data errors, inaccuracies and incompleteness.
4. Establishing Consistent Procedures
Make the data input, storage, extraction and analytics processes as consistent as possible to ensure that your data itself also remains consistent. Prepare documents that clearly mention the steps that everyone at various levels in the organization needs to follow. This will bring consistency in the procedures involved in data handling and will also help to avoid common data quality problems.
5. Data Quality Assurance Audits
Perform routine and recurring audits so that you can avoid data quality problems from occurring. Knowing that the problem is most likely to occur beforehand is the best way to prevent it. Audits don’t involve much manual work. There are various automated data quality solutions such as those from Syncsort.
The organizations must follow the above best practices for maintaining their data quality. The various data management tools facilitate and provide a sound framework to implement data quality measurement, monitoring and subsequent improvements in the data. An independent and focused assessment of data is a must to ensure that the business has been able to maintain its data quality and find the areas which needs immediate focus and improvement. The businesses should go for that quality management solution which closely aligns to their unique business objective.
Ensuring the quality of data is a joint responsibility of producers, consumers, business application designers, developers and the business leads. But for this first of all the top-level data entry process has to be in place and error-free. Without data quality, it is not possible to deliver any reliable insight into your business. Thus make sure that your data is consistent, complete, accurate, error-free and easy to translate to the formats you require.