Strategizing How to Improve Data Quality in Business 

Technical measures when strategizing how to improve data quality in business can seem very attractive, yet their end result may be undesirable.

The world has advanced in every possible way. As a result, businesses have evolved as well. Henceforth, their owners and workers have too taken a step forward in knowledge and wizardry in many cases. One way of making a business successful is knowing what your competitors are up to, how well you stack against them, who is more successful currently amongst you, and who will be more successful in the future. To enable all this, data kicks in. Not just data, but quality data. So the question arises, how to improve data quality in business and what data quality actions can be taken to do so?

When someone thinks about taking on data quality actions or projects, they think of pleading a case where a lot of technical measures, be it completeness, correctness, consistency, reliability, accuracy, timeliness, redundancy free, relevance, consistency, clarity, understandability are involved. However, business owners and management are concerned with just one thing, the bottom line (money). Therefore, this makes it quite difficult to improve data quality since the upper management is not willing to design or dedicate a budget or any money for data quality.

The budget or money is needed to design or utilize the requisite software to establish, monitor, or control actions or habits of workers and consumers that leverage the requisite and appropriate level of data quality. For example, ensuring that data taken from a supermarket of all the various consumers is accurate and complete requires a company to hire someone who can ensure this.

So, let us then think about how to strategize on how to improve data quality and convince our upper management in doing so. The way one can do this is not by shedding light upon how the technical measures can be achieved through fulfilling the various needed KPIs or data quality actions and how they work, but by only, and solely focusing on their result in generating revenue. Explaining to your management why and how data quality will bring in more profit for the company (its importance in revenue generation) is what will lead to the funding data quality actions.

Now that we know the basis of the argument that we shall be putting forth to convince our bosses, we must learn to explain the simpler outer parts of a technical work - the reason for us performing the work, instead of how the work is done. Some ways of going about such a task are mentioned below.

Data is the New Oil Only if it has Quality

A statement you may hear a lot in this day and age is ‘Data is the new oil.’ The reason for this is that data can be sold to other companies once a wide enough variety of it is present in your hands, or it can allow you to do wonders when it comes to bringing in revenue. However, none of those things would be possible if the data lacked quality. 

To put a simple argument to your boss, you could say if we want revenue, we need to bring in the level of quality into our data just as we do with our other services and products. In summary, imagine selling anyone an unreliable or incomplete product, they would give it back. Data works in the exact same way. Improve data quality, and see your profits increase.

Use the best strategies and tools that make you a data quality expert.

Facilitation of Data Quality in Future Forecasting

Imagine if you as a company know precisely what your revenue will be and when your sales rise and drop. Not only will you have the cushion of knowing when to market your product more, but also understand where you may be going wrong and what new strategies you can employ in order to fill in the gap at that time of the year. 

This will obviously result in an increased revenue. In fact, it can also lead to proper know-how of how much to expand, which will result in greater revenue down the line. However, for this to happen, the data must obviously be accurate and complete. In the case that quality is compromised, serious losses can happen. Therefore, taking data quality actions will prove immensely monetarily beneficial for the company. Such an argument will surely sway the upper management’s mind on wanting to fund ways to improve data quality.

Ability of Data To Track Competitors

Staying ahead of the competition is of utmost importance. It gives a business the necessary edge needed to do things more efficiently and prosperously. Having data that is vast and over a long period of time can help you keep track of exactly what other companies doing the same thing as you are up to, their launching strategies, and what and when they launch their products.

This means that you can launch your products when you know your competitor will not be or at a time when they would not expect it. Henceforth, you would sell your product more. Therefore, your sales and, obviously, your revenue will rise. Telling your boss this instead of how the technical measures of data quality work will actually make them want to fund those data quality actions, ironically speaking, one could say.

Untapped Markets/Target Consumers 

Having data that is accurate and that shows what consumers are demanding in the market but not finding can certainly show areas that are untapped. Once you as a company invest in such areas, since you will not only not be fulfilling the demands of buyers, but also have no competition, you will thrive in a dual sense monetarily speaking. 

Once your boss understands this, he will undoubtedly want to improve the data quality. Just as it was for future forecasting, the accuracy of data matters greatly here. This is because a lot of investment may be needed to do something new, and if it turns out to be wrong, the company could suffer major losses. All the more reason to fund such data quality actions, don’t you think?


There are, of course, many other arguments that can be put forth to support data quality actions and reasons to improve data quality, such as improved customer relations, lower mailing costs, and more effective marketing, they all tell the same story. Focus on how data quality brings in money, or saves money. Everything surrounds the result of ensuring data quality and not how its technicalities work. Therefore, making a business case with such strategies will lead to them surely being convinced on providing funds for data quality actions since these arguments show a direct effect on the revenue. The other technical side does not manage to show an immediate or direct impact.

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