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Data is critical in any sphere of organization. At every stage, data validated and generated at all levels and exchanged between several systems and processes. The data here should be consistent and accurate with the data 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:
- Many sources of information: Data in business organizations come from all sorts of sources like public files, intranet, etc. Not all are genuine.
- Several BI tools: Organizations use multiple business intelligence tools that have common functionalities. This leads to a change in the data facts.
- Haphazard data workflow: This is very rampant and companies miss out on maintaining a standard workflow for collection of data as well as its processing. It leads to loss of data.
- Human errors: As they say, ‘To err is human’. Most confusions in data integrity arise out of human mistakes.
Maintaining and improving data integrity is of the utmost importance as data analytics has effects on decision making. Strong quality management practices followed for protecting and maintaining data in its pure form.
Some of the steps taken to ensure data integrity are:
1. Cleaning and Maintenance:
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. For example, a tool, Data Integrity Gateway, automates processes, monitors clean up and ensure the quality of data throughout its cycle.
2. Get a single source of data:
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. 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 to provide quick and correct facts and numbers. A data warehouse greatly improves data integrity.
3. Data entry training and liability:
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:
- Active and responsive to operational needs
- Easy to understand. The procedures to be followed must be available as and when needed.
- Employees should have access to information as per their roles and responsibility.
- Audited at regular intervals. When employees are held responsible for incorrect data being entered into the system, that is when things will fall into place.
4. Standard data definitions:
Questions about the revenue often have more questions following. Like a fiscal month or Gregorian month, which kind of revenue, etc. This 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.
5. Data validation:
When manual data entry methods used, there is always a possibility of entering inaccurate data, irrespective of the training. Thus validation rules 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 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.
7. Update the data regularly:
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 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 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.