As computers grow more powerful and ubiquitous, electronic data is playing an important role in more and more industries. The odds are excellent that your own company already relies on data for important operations. If you don’t now, you probably will soon! Your data needs to be reliable in order to make intelligent decisions, and that means you need a good data quality audit tool and a well-documented data auditing process in place.

Defining High-Quality Data

Even though electronic data can take a virtually endless number of different forms, telling the difference between useful and useless data is surprisingly constant across every industry. Good data always displays a few universal traits: It’s accurate, it’s complete, it conforms to it’s required format, and it’s unique (i.e. it’s not a duplicate).

Since even very modest businesses can find themselves handling thousands of individual records comprising millions of individual data fields, you can see that checking and improving the quality of data can rapidly become prohibitively expensive if it’s done entirely by employees. This is where data quality auditing tool comes in.

Why Data Quality Auditing Tool Makes Sense

As noted above, the sheer volume of information that needs to be inspected to verify data quality puts the job outside the purview of humans. Rather than confirming the quality of individual records, you need to use your team’s time and expertise to codify effective rules for determining data quality and procedures for dealing with low-quality data.

With a workable set of rules and procedures, the lion’s share of the work involved in data quality auditing can safely be turned over to automated tools. The specific tools that will be most useful for you depend heavily on the sort of data you’re using and the software you already use to store and manipulate it. Any good auditing tool needs to be heavily customizable, though. Your tools should always conform to your own auditing procedures, not the other way around!

Splitting Resources Between Auditing And Improvement

Despite the important role that automation plays in data quality auditing, it’s not possible — or indeed wise — to remove human decision making from the process entirely. Even the best auditing tools will be stumped by certain questionable records. You’ll probably even build human judgment into your auditing procedures; e.g. “if a record fails quality tests x, y, and z, forward to analyst for correction.”

You need to develop a workable balance between improving your auditing process and improving the quality of your data itself. Both of these tasks should proceed in concert. For one thing, as time goes on and conditions change, you’ll probably want to change the way you record and manipulate data. This means your auditing process needs to change as well.

Keeping your company’s data accurate and reliable is rarely the most glamorous job in the building. It’s a necessary task that you can’t afford to ignore, though. By thinking at the proper scale and using the right data quality auditing tool, you can ensure that everyone in your company is creating and working with trustworthy data. Once you have the system set up properly, the amount of labor involved in auditing your data quality should be minimal.

By Kar

Dr. Kar works in the interface of digital transformation and data science. Professionally a professor in one of the top B-Schools of Asia and an alumni of XLRI, he has extensive experience in teaching, training, consultancy and research in reputed institutes. He is a regular contributor of Business Fundas and a frequent author in research platforms. He is widely cited as a researcher. Note: The articles authored in this blog are his personal views and does not reflect that of his affiliations.

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