Data is critical in any sphere of organization. At every stage, data is validated and generated at all levels and exchanged between a number of systems and processes. The data here should be consistent and accurate with the data that is stored anywhere else. This is where data integrity comes into picture which states that the piece of data is complete, consistent and from a trusted source.
For integrity in data, the individual data values must be according to a particular data model. The other attributes too like business relations, definitions, dates must be complete and correct.
A huge organization, which thrives on data, always insists on reliable and correct information. After all, data is any company’s biggest asset. But achieving data integrity is not a child’s play. There are many obstacles in the path. Some of them are:
Maintaining and improving data integrity is of utmost importance as data analytics has effects on decision making. Strong quality management practices need to be followed for protecting and maintaining data in its pure form.
Some of the steps that can be 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 be followed as an ongoing approach which maintains the system health so that there is improvisation in data integrity. For example, a tool, Data Integrity Gateway, automates processes, monitors clean up and ensures the quality of data throughout its cycle.
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 a number of systems to come up with a number. This leads to different answers each time you look up for the same information. The same person too might not be able to come up with the same figure twice. To eliminate this, companies should invest in a single sourced data warehouse. A data warehouse normalizes data so as 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 which reflect on the end result. The end 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 fiscal month or Gregorian month, which kind of revenue, etc. This is accompanied by a number of work sheets 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 being used on a common platform should be common. It has to be about the data and not about how it is read.
When manual data entry methods are used, there is always a possibility of entering inaccurate data, irrespective of the training. Thus validation rules should be 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% fool proof. They are in fact 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 can be converted and made available in a system. When misleading data gets added over a period of 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 the 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 it was carried out. Frequent updates contribute to better quality and increased 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 time periods.
Data forms a vital portion of the company’s success factor. As an employee, you are equally responsible for the success. Taking care of the data and its integrity becomes your duty. These tips should be able to guide you in the right direction.