The more you know, the better you will be able to make the right decision. This maxim when applied to business expresses the necessity of collecting a lot of data, that we are well aware of today. But simply having a lot of data is not everything and by itself does not guarantee success. Even more important than quantity is the quality of data. To extract the most value out of data, businesses have to understand, measure, and be able to ensure its high quality.
Knowing what works
When we talk about tangible products, like chairs, automobiles, and washing machines, the concept of quality is quite easy to grasp. Sometimes we can tell something about an item’s quality just by looking at it. Visible damage would indicate lower quality, compactness, or even good looks if it relates to the purpose of the item – higher quality.
Determining the quality of something that exists outside of the physical world, like data, seems a bit more complicated. But even though of a specific kind, data is still a product and a tool that is meant for certain purposes, thus in a way, the same rules of measuring quality can be applied to it. Data that is “damaged” is of worse quality and data that is fit for its purpose – is of higher. We just need to know the specific ways to check for “damages” and fitfulness for the purpose of the datasets.
Of course, with data, the “damages” are not easily visible to the naked eye. But we can check for empty fields in the datasets, for inconsistencies, redundancies, and clearly outdated or inaccurate data units. All these faults reduce data quality.
And they also have an effect on the dataset’s fitfulness for purpose. To determine further how well data can be used for its purpose, naturally, we look at the purpose itself. But generally, whatever the purpose, high-quality data sets will be accessible and easy to use. For it matters not only that the purpose is achieved, but that it is achieved efficiently, without unnecessary costs of time and effort.
Inspecting one’s datasets by their conformity to these standards will help to understand the quality of data stored. Now let us look at why it is crucial to understand it.
The importance of data quality
As mentioned, data is not just something we have, but something we use. And as with any other item that we use, maintaining a certain standard of quality has its advantages. Here are the 5 key benefits of ensuring the high quality of the data that we use.
- Avoiding crucial errors. Naturally, wrong information leads to wrong decisions. If the data is inaccurate or incomplete, making moves based on it one is always at the risk of making mistakes. Even small errors adding up can be very costly. But sometimes one simple error can turn into a major drawback, thus everything should be done to ensure that such mistakes are avoided.
- Workflow efficiency. Any tool that has quality issues is sure to slow down the processes that it is used for. The same goes for data, as all the problems with datasets will turn into obstacles to use it efficiently. Ensuring high data quality will allow smooth usage and keep the workflow going at a satisfying pace.
- Cost efficiency. Improving the quality of a dataset might cost more than just letting it be at first. But the investment is sure to pay off, as fixing the issues when they can no longer be ignored might demand a halt in business procedures and lost profit.
- Achieving goals. One cannot be sure that something will be done when the tools used to do it are not very good. Whether data is used to generate leads, create marketing strategies, or for competitive or investment intelligence, one can only be sure that its goals will be achieved if a certain standard of data quality is maintained.
- Brand and employer reputation. Being constantly misinformed does damage to one’s reputation. If your interactions with customers, partners, or the general public are based on bad data, it will soon start to show, and your brand will be known for the wrong reasons. Similarly, working with bad tools lowers employee job satisfaction. Ensuring data quality will keep the people who work with it happy and increase employer reputation.
Work smarter, not harder
It is easy to see that ensuring data quality is important and beneficial. But what if it is too hard to achieve?
The answer is that maintaining high data quality requires attention, but it does not have to be hard work. One does not have to constantly manually check datasets for issues and drawbacks. Many processes, such as data standardization and data cleansing have been developed to ensure high data quality with the assistance of software tools.
Thus, as it is usually the case with doing the job right, what it really takes is a conscious decision to aim higher.