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At every business stage, data is validated and generated at all levels and exchanged between several systems and processes. In today’s data-driven business decisions, data integrity ensures if the data is complete, consistent, and from a trusted source. To improve data integrity, the individual data values must meet the quality checks of the data model.
Let’s look at the major deterrents in improving data integrity.
Strong quality management practices followed for protecting and maintaining data is required to ensure the best business results. Some of the steps taken to ensure data integrity are:
The quality of data gets highly affected by bad data. A data cleaning approach should be readily taken up by organizations to detect, remove, and correct all discrepancies. It should follow an ongoing approach that maintains the system’s health so that there is improvisation in data integrity.
Most business organizations have data all over. The simplest of questions like “What was our revenue in the last 2 years?” has several answers. Companies tend to use numerous excel sheets from several systems to come up with a number. This leads to different answers each time you lookup for the same information.
To eliminate this, companies should invest in a single-sourced data warehouse. A data warehouse normalized data to provide quick and correct facts and numbers. A data warehouse greatly improves data integrity.
Often data integrity starts from the very first level–at the user. As discussed previously, manual entry of data sometimes leads to errors that reflect on the result. The results are taken into consideration to make potentially dramatic business decisions. Employees thrust with data entry should be properly trained, and they should know about the protocols to be followed. The ideal training approach must be:
Questions about the revenue often have more questions following. Like a fiscal month or Gregorian month, which kind of revenue, etc. This is accompanied by several worksheets sent through mails, with minor adjustments done by people at all levels. Such situations lead to chaos.
Here, a more appropriate solution is to have a single client to define and calculate a metric. All reports must have a uniform look and feel so that one look at them will give all the answers. Reports used on a common platform should be common. It has to be about the data and not about how it reads.
When manual data entry methods are used, there is always a possibility of entering inaccurate data, irrespective of the training. Thus validation rules are adopted in such cases, where the admins can control and restrict the data values that are being entered by any employee into the system. This prevents accidental modification of data, providing additional security and better quality, which automatically leads to more accurate data analytics.
Manual statistics are not always 100% foolproof. They are one of the reasons for bad data quality. If there is a possibility, ensure to automate the process of entering the data. Most of the data is converted and made available in a system.
When misleading data gets added over some time, people stop using it. Do not let that happen. Automate the processes as much as possible, to achieve the highest degree of data integrity.
When data is being discussed, the foremost question that comes to mind is “When was the last update?”. Whichever reporting system you are using, it should have the latest updated data in it and also the time when carried out. Frequent updates contribute to better quality, increase and improve data integrity.
If the updates are not possible in real-time, the second option is to let people know about the last update and its details. This way people have a window to the data values and their respective periods.
Data forms a vital portion of the company’s success factor. We hope these tips should be able to guide you in the right direction to improve data integrity.
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