In one of our previous articles, “Data errors. What are the consequences of using poor-quality data?”, we investigated the causes of data errors and the effects of using incorrect data for business purposes. This time we’re going to talk about ensuring high data quality and explain what data validation is. Read on to learn what are the stages of data validation and why you should never forget about it during your work!
Data validation is a process of verifying, correcting, and deleting incorrect, incomplete, damaged, duplicated, or improperly formatted data.
Data validation explained
Validation is a crucial part of working with data. Well-prepared data make it easier to work with them. Assuming they end up being handled by a competent person, such data ensure your work results are reliable.
Validation has many stages and completing the process manually is very time-consuming. So if you work with large datasets or often need to verify their quality, you should consider automation. It completes the stages described below without you even realizing it and allows you to quickly start your proper work with data.
What are the stages of data validation?
At this stage, you can also add further attributes based on the data you already have, for example, define gender based on the person’s name, add a missing zip code or county based on the town name, etc.
High-quality data – what does it mean?
Let’s say it again – it’s extremely important to use data of high quality, whether for working, shaping opinions, or making decisions. But what does it mean? What are the features of fine data?
They are reliable
When you search for recent news, you most likely try to get them from sources viewed as reliable and truthful. You should also pick only those datasets that you know are credible.
They are up-to-date
It mostly depends on a particular industry or domain as to which data can be considered up-to-date. What’s important is working with the most recent data. Analyses based on obsolete data aren’t too useful.
They are consistent
This means that data from different sources related to a single phenomenon complement one another and together, they provide a broader perspective. Consistency can be also related to data within a single set. In this case, it’s about the integrity of e.g. nomenclature or methods for defining specific parameters.
They are relevant
Data need to be important and valuable for the project. They should also help to solve a specific problem.
They are accurate
Data need to be saved in the right format and using an adequate data model as well as consistent standards and units.
They are complete
Data need to contain all necessary elements or attributes and can’t be missing any of them.
Why you should ensure the high quality of data
As we already mentioned, you can’t achieve desired results in working with data if you use poor-quality sets. If your analysis is based on incomplete, outdated, unreliable data in the wrong format, the effects will be just as poor.
Verifying data before using them in your work is not just recommended – it’s necessary. Why?
So that used and shared information is correct and reliable
Manual data collection and updates are very prone to errors: typos, duplicated records, and wrong values. The results of analyses based on such data are incorrect.
So that your projects and analyses are correct and useful and you can make accurate business decisions
The most successful companies have a wide data usage – their data are used every day by internal teams but also managers, directors, and other decision makers. Only reliable data can ensure accurate business decisions and secure competitive advantage.
To improve your process effectiveness
Working with neglected data that contain many errors is very tedious and frustrating. Correcting them is time-consuming and tiring. Moreover, if data aren’t fixed at their source, it needs to be done all over again whenever you use the given dataset. Along with the growth of your database, it’s increasingly hard to work with data that aren’t organized and regularly updated.
To lower costs and increase profits
Processes that are ineffective due to poor-quality data also result in financial loss. This is related to inefficient work of employees, delayed projects, contractual penalties, and the simple fact that you can’t move on to the projects for the new clients. Controlling data and ensuring their high quality help you increase the effectiveness of work and lower costs.
To gain new customers
Up-to-date, reliable data open new possibilities. Customer behavior can change quickly and information about these trends helps you react fast and adjust your offer to the new expectations. Therefore, you increase your chances of gaining new customers.
7 steps to achieve high-quality data
Finally, we share a short guide on how to ensure your data are always high-quality.