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How can you ensure the high quality of data and why is data validation so crucial?

By November 24, 2022FME
data-validation

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.

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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?

Verifying data – searching for basic errors that are easy to detect at first glance.
Parsing data – a process in which a string of data is converted from one format to another. A good example of data parsing is extracting information from an electronic form as separate attributes: a name, last name, zip code, city, street, etc.

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.

Deleting duplicates – getting rid of repeated data. During this stage, it’s possible to search for many entries related to a single client, even if data are written in different types. You can also merge several databases and standardize entries so that a single client record includes all of their information from different sources.
Completing data or deleting records missing data – this stage ensures that your database is complete and missing information doesn’t influence the results of the performed analyses.
Standardizing data – defining a standard format for data and transforming all records so that they match it. An example could be a data format dd/mm/yyyy or a phone number format +1-XXX-XXXX.
Detecting conflicts in the dataset – this stage aims to eliminate values and data that are mutually exclusive. For example, a company’s database may include information that a particular customer has no pending payments. However, this information is known to not be true, as the same customer has just been invoiced for provided services. In this case, the database hasn’t been updated yet or the value has been omitted.

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.

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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.

data-completeness

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.

1

Use verified, reliable data sources

2

Take time to validate your data

In case of large datasets or frequent validations, automate this process.
3

If you want to use data in a project, analyze several sources

Verify how complete, up-to-date, and useful your data are for the project.
4

Share your thoughts, problems, and results of the validation with your team members

During the next project, you won’t need to validate and analyze the same dataset over again.
5

Make your business decisions only when you’re sure that the data you use are reliable, correct, and up-to-date

This way, you won’t risk making wrong decisions.
6

Limit the number of your data sources and tools

This helps you mitigate the risk of errors related to source integration, as well as system and format incompatibility.
7

If you use many different data sources, integrate them and collect your data through integration platforms

They allow you to control data, fix errors, and fill in the missing information.

Would you like to take better care of your data quality? Contact us to talk about your needs.