Many companies and organizations are constantly at war with their data system and quality. Everyone wants to move from poor to good data quality.
Good data quality ensures that your reports are accurate, assets are efficient, programs are effective, and your company steps align with your goals. Meanwhile, poor data quality speaks of inaccurate, incomplete, and unreliable data. So, how do you move your company from having poor data quality to a good one?
Well, this article reads about best practices for data quality and maintenance.
1. Data Assessment.
Data assessment involves looking into the tiniest details. Some questions you could ask include; What data is being collected and how? What is the process of data entry? Does it tell a complete or incomplete story? How well does it conform to the guidelines? Was it collected at the appropriate time? Are these data safe and readily available for use? Is there data deduplication software in use? These and many more questions should be asked specifically and continuously. Data assessment isn’t a one-time maintenance procedure. It would help you identify gaps and loopholes in data collection, entry, processing, and recording within the shortest time.
Thus, data assessment would be accurately done if established metrics are targeted at achieving your company’s goals.
2. Investigate Data Quality Failures.
While carrying out data assessment, you must be open-minded and ready to face it if you have discovered any loopholes. Peradventure, your organization experienced a downturn in data quality; you have to make proper investigations and fish out where the loophole is. Data errors can be difficult to rectify, and that’s not all; they would also keep occurring unless the root cause is solved. And some of the questions you might ask during the investigation are the following:
- Are there too many data sources?
- Are the inadequacies due to human errors?
- Is there a lack of communication across departments?
With that, fish out the root cause and rectify them to avoid future hitches.
3. Automating Data Entry.
No matter how much you try, humans are prone to making errors. This fact should be well understood and accepted. If you observe poor data quality constantly due to human errors, why not consider automating as much as possible? Human errors could be from employees, multiple users, or even your customers. There is only a little you can control when dealing with human errors. Therefore, look into automating the data entry process. Any investment in automating your data is worthy as it would help improve its quality.
4. Constant Training.
The work that has to be put into achieving good data quality cannot be undermined. It requires a good understanding of data principles and technologies. It is why formal training must be conducted regularly for data staff to keep them updated about data management. Firstly, the need for quality data has to be well taught. Your data staff must understand why they need to prioritize data quality and maintenance. After that, the need to become vast in areas like; the principles and practices of data quality management, benefits of good data quality and costs of poor quality, the challenges involved, and how to break through them. They must also be trained to use software to make their work easier, faster, and free from errors.
5. Data Auditing.
It is important to set policies and guidelines to make data processes smooth and improve quality. However, times and events change, and that is why there is a need for data auditing. It helps you to see if the set guidelines and processes have been effective. It also helps you and others to build trust in the data you export.
Data audits are like the checks and balances put in place to ensure that the expected results are achieved. It’s possible to set metrics and objectives that are ineffective in meeting your company’s goals. That is why a data audit is necessary. Data audits should check for incomplete data, duplicate entries, outdated entries, data inaccuracies, poorly populated fields, etc. Again, data audits are not a one-time procedure. The more you audit, the better the success you record.
6. Data Quality Tools.
You might have heard that two good heads are better than one. In this context, you should use many good tools in your data management process. It would ensure you a greater quality of your data output.
Conclusion
Data quality management and maintenance is a complex and continuous process. However, every effort is worth it compared to poor data quality costs. Assess your data frequently using the right metrics to help you achieve your goals. Explore the best tools and train your data staff to help accelerate and maintain the growth of your data quality.
