What Do You Mean By Data Validation

Data validation has become a genuine buzzword in the business world, but too many entrepreneurs, managers, and sales specialists still don’t understand its true value. Data validation is fundamental because people, things, and organizations produce enormous amounts of data every day.

According to the report, it would take you approximately 181 million years to download all the data from the Internet. Another study reveals that 1.7MB of data will be created every second for every person on earth. The numbers are mind-boggling, but they give businesses the opportunity to analyze potential customers and learn almost everything about the target audience.

The only thing that matters is to keep your data sets neat and clean. In this post, we will discuss the concept of data validation and explain to you all of its features and benefits. Let’s begin!

Data Validation: Definition and Benefits

If you want to understand data validation, you need to learn what it really means. By definition, data validationrepresents the process of checking the accuracy and the quality of source data before using, importing or otherwise processing information.

Although it may seem like a routine and more or less irrelevant process, the truth is that data validation has the power to make or break entire businesses. How come?

Well, it turns out that data validation can bring you a wide range of practical benefits and drastically improve professional efficiency. Here are some of its major advantages:

  • Improves data accuracy: The first benefit of data validation is fairly obvious – it helps you to clear and systemize datasets. That way, your customer-related details become better and more accurate, thus helping you to get a precise overview of your target group.
  • Enables more accurate decision-making: Every decision you make is (or at least should be) based on real-world information. But if you don’t have accurate data at your disposal, how can you hope to bring trustworthy decisions? You cannot do it without data validation, so you better invest in this process to ensure better decision-making.
  • Reduces time-waste: The worst thing you and your team members can do is spend hours or even days preparing special offers for prospects who do not exist. It’s a huge waste of time that can have a tremendous impact on your business results, but you can eliminate this risk through data validation.
  • Generates a higher ROI: It is much better to have 10 verified leads than to target 100 irrelevant prospects. After all, those 10 clients will generate a much higher profit in the long run. Therefore, the return on investments skyrockets when you validate data and reach out to qualified leads exclusively.
  • Boosts productivity: Data validation also allows you to boost professional productivity and make your team more efficient. Namely, your employees won’t have to waste time on unverified leads and so they will be able to make more in a shorter amount of time.

4 Steps of Data Validation

Data validation is a complex process that varies greatly from industry to industry, but we can recognize a few common patterns in almost every project. Here’s how the process functions in the vast majority of cases:

  1. Make a plan

“The first thing you need to do is to make a plan of work because it helps you to keep track of all data validation activities. The most important detail is to specify your goals and clearly define the targets of data validation such as schedules, KPIs, and so on.” – says Jake Gardener, Big Data expert at EssayMama, assignment help uk and essay help.

  • Conduct the validation process

The second step is critical as it suggests testing and validating all of your data libraries. This is where raw numbers turn into meaningful and well-categorized datasets.

  • Data formatting

It’s not enough to systemize information as you also need to format data so as to comply with the overall organizational procedures and regulations. Data formatting enables every user within the system to access and interpret information accurately.

  • Sampling

The last step is sampling, a process in which you take a small amount of data and test it to learn whether it really fits your data validation plans. In this case, it is important to clarify your methodology and expectations by determining sample percentages, acceptable error rates, etc.

7 Data Validation Methods

Data validation consists of multiple procedures and techniques, but seven methods make a quintessential segment of the process. All of the data validation steps we mentioned above can be done using one of the following methods:

  • Range check

This is one of the basic and most common data validation methods. Just like the name suggests, a range check is used to define the limits of a given category. For example, you can use it to set upper and lower limits and accept each number between 5 and 55.

  • Type check

Using this method, you can check whether the right type of data is used. For example, data validation is supposed to make sure that you used numbers instead of text-based inputs.

  • Check digit

This method is focused on number series in particular. It’s a valuable technique that depends on the nature of your number formats and entries, so it’s up to you do define parameters and check digits correctly.

  • Length check

Sometimes you’ll want to limit inputs to a certain number of characters. For example, datasets should contain inputs ranging from 10 to 40 characters only. Length check will make sure to filter and sort entries with the targeted limit.

  • Lookup

If your dataset is only projected to contain a certain number of values, then you can use the Lookup method to reduce errors. For example, there are only seven days in a week and you can use this fact to simplify data entering, enhance accuracy, and speed up routine procedures. 

  • Format check

You’ve probably guessed already that format check is designed to test the accuracy of a given format within your data list. You can use this method to check all sorts of formats, including highly tailored and classified inputs.

  • Presence check

Some input fields are more important than others, which is why you cannot afford to leave them blank. If your database should contain such valuable entries, you can utilize the presence check to see whether these fields are blank or not.


Data validation is a fundamental business concept, but it somehow gets neglected way too often. No matter what you do for a living, you should learn the basics of data validation and use it to improve professional performance. In this post, we showed you:

  • The definition and the benefits of data validation
  • Four steps of data validation
  • Seven data validation methods
  • Main features of data validation tools
  • The best data validation platforms

Do you already use data validation in your work? Which strategy do you consider to be the most important in this field? Let us know in the comments so we can discuss this interesting topic with you!