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You need high quality materials in order to produce high quality products. Just the same, you’ve got to have high quality data in order to create the best business insights for your company, but how do you evaluate data’s quality? What kind of tools should you use to make sure that your information is accurate? 

From this article, you will learn what factors are the most important in determining data quality and what kind of information is rather useless for your company. Moreover, we’ll tell you about the Data Quality Management process and advise you on the best practices in this area. Keep reading if you’d like to know what kind of tools you can use to improve your data quality and ensure reliable analytics output. And if you’re looking for best solutions for your company visit our Data Pipeline Automation services

Data Quality Management – what is it? 

Let’s pretend for a second that we don’t write about technology… As a person who runs a business, you receive all kinds of information every day. Surely you ignore a lot of it, as it’s not valuable for your company’s development, or there simply isn’t enough to make decisions based on. This is low quality information.

What is data quality?

Companies that deal with big data, use analytics tools in order to make short and long-term data-driven decisions and operate efficiently. It is crucial for those brands that their collected data is valuable and makes it possible to produce equally valuable business insights. Data quality is the ability of data to serve its purpose – to help create useful, reliable insights for your company. Information that leads to making the right business decision is high quality information. So, how do we evaluate data? Some articles may mention five, six or even ten factors you have to remember about. Here are some of the characteristics used to describe data’s quality:

  • Completeness incomplete data lacks some information that might be useful. Where there are gaps in datasets, there is the possibility of producing unreliable analysis and making the wrong decisions for your business, which could have awful outcomes. 
  • Accuracy and Reliability what if the data is not correct at all or comes from an uncertain source? Misleading information once again leads to bad decisions. Without accuracy, your data may do as much bad as good.  
  • Availability there are many people in the company that work with data and can use it to do their job better. If some data is not available for certain employees that may benefit from using it, it does not serve its purpose. Be sure that the experts who need particular information have access to it. 
  • Timeliness in some cases, using historical data to make decisions is not a good idea. If your company needs to produce insights a short time after collecting data, you should learn more about real time big data analytics. Bear in mind that relying on outdated information may bring you to an inaccurate decision. 
  • Granularity data may give you knowledge about details or a general state of something. Many times, you cannot make a good decision operating only on general data. 
  • Relevance some data may not be useful. So…, what is the point of storing it at all? Information you find totally useless should not be taken under consideration during data analysis.

Why is data quality important? Using false or inaccurate data, you won’t be able to make the right decisions for your company, and you’ll have to waste your time and resources in order to solve the problems you created yourself instead of investing in development.

When is data low quality?

You should not base your judgment on unreliable information if you don’t know the source, or you’re not sure if you can rely on some data, you should fix the issue by eliminating unreliable sources. Incomplete data should also be eliminated, as it doesn’t give you (or your analytics systems) a clear, real view of the situation. Some data may be ambiguous easy to misinterpret and therefore lead to bad conclusions.

Quite serious problems may be caused by duplicated data. Imagine having many profiles of the same customer in your database. Apart from confusing your staff and taking up additional storage, this also leads to an inaccurate customer count and weakens marketing analysis, etc. 

You need to remember about updating data if you wish to improve your offer and marketing campaigns. Some of your customers may have been students just a few years ago, but today they might have two children and a dog and completely different needs than before. You can use new information to recommend better suited products or optimize advertising.

The Data Quality Management process

Data Quality Management is a process whose goal is to eliminate useless data in order to maintain the high quality of the information sets to be used for analytics. 

Defining the required data quality 

Ensuring high quality data starts with defining what the data should actually be like. You do this by establishing thresholds and rules requirements for your information. Ideal data will be 100% compliant with your data quality characteristics (accuracy, etc.). As you probably suspect, reaching 100% for all the attributes is very difficult. Usually, a company decides which data and qualities are the most important. 

Checking data accuracy

After setting rules, you need to have a look at your data and see if it meets the rules you have set. This process makes it possible to separate low quality information from high quality information in order to ensure good business insights.

Identify what causes low data quality

There has to be a cause for collecting low quality data. Have you ever wondered why the information gathered by your company is not good enough to produce useful business insights? Eliminating the sources of incomplete or unreliable data is the first step towards improving the decision-making process. Sometimes this can be done easily, by making the form you use for collecting data clearer. You can set a validation rule in your system so it doesn’t accept data if it is wrong.

Monitor and control data

The data quality management process never ends you need to review your data quality regularly to improve it. What you can’t forget is that the business environment is changing all the time. Other data may become important or should be evaluated in a different way. 

Data Quality Management best practices

Data quality should be your priority. Start by improving it and then make sure that all of your employees understand how fatal it can be to make business decisions based on bad information. Ensuring proper data quality in your company is not easy though. You need to design the right strategy, choose the right tools and set up a quality process. Automating the data entry processes in order to reduce human error is a good idea as well. 

It is important to prevent repeatedly collecting bad data instead of having to eliminate it all the time. Creating special rules can help you detect duplicated data a piece of information cannot be input to the same database more than once. There are many methods to ensure the high quality of data used for analysis our best consultants can help you choose from the available data quality tools – contact us. Some let you divide data into components that can be worked on; others remove or modify duplicated information. You can’t forget about the proper tools for data monitoring. Invest in the best systems for your company.

Check out our blog for more in-depth articles on Data Pipeline Automation:

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