Businesses have realized the importance of data. After all, in today’s world, decision making largely depends on the quality of data. This
is why it is important to measure data quality and apply reliable quality data management techniques.
We create over 2.5 quintillion bytes of data every data and the number is only going to increase in the future. Big data is changing how we do business.
In this article, we’ll talk about data quality management and its importance. Let’s get started:
What is Data Quality Management?
Here’s the data quality management definition from Techopedia:
“Data quality management is an administration type that incorporates the role establishment, role deployment, policies, responsibilities and processes with regard to the acquisition, maintenance, disposition and distribution of data.”
In simple words, data quality management involves maintaining a high quality of data. The process includes not just the acquisition but also the distribution of data. In addition to this, it also includes the implementation of advanced processes.
Related Reading: What Is Data Management? – The Comprehensive Guide
The Pillars of Data Quality Management
Data quality management includes several techniques. Let’s have a look at the five pillars that support it:
#1 The People
Technology itself will not be of much use if there are no people to implement it. Despite what everyone says, human oversight is far from being obsolete. Therefore, data quality management has several roles and positions for humans including data analysts and data managers.
They offer different services and perform unique duties to ensure the proper management of data. Some even need special training and education to fulfill their role.
#2 Data Profiling
Data profiling is one of the most important parts of the process. It involves:
- Having a complete look at the data and reviewing all the details.
- Contrasting and comparing the data to ensure correctness.
- Running different statistical models on the data.
- Measuring and reporting the quality of the data.
The main purpose of this process is to develop insights into the data. It helps develop a starting point in the process. Without data profiling, it would be hard to create standards since we wouldn’t know where we want to go with the data that we have.
#3 Defining the Quality of Data
This process can be very difficult to manage as it involves writing down and explaining quality rules. The rules should be written according to business requirements and goals.
This can be very complicated because the data must also meet the technical requirements and standards set by other bodies. In fact, business requirements often take the front seat in this regard.
It is very important to create quality rules if you want your data management efforts to be a success.
#4 Reporting Data
The fourth pillar involves recording and removing issues with the data so that you only have clean data to work on. Ideally, this should be used to identify quality patterns.
Reporting and monitoring make up the crux of this process.
#5 Repairing Data
Merely identifying the problem is not enough, one needs to take steps to correct the issue. The business needs to know the right and most efficient way to repair the data.
It is best to go deep into the cause and understand the reason. This will not only help correct the data but can also prevent similar problems in the future.
Why Is Data Quality Management so Important?
It will not be entirely wrong to state that you will have a difficult time in managing your business if you do not have well-maintained data.
Here are some of the benefits of data quality management:
Good data equals good leads. A lot of marketing campaigns fail due to the poor quality of data. You will not be able to reach potential clients if you do not have the correct information.
Data quality management ensures you hit the bull’s eye and get a high return on investment when you invest in marketing.
It costs to manage data but this is an investment you must make since correctly managed data would allow you to save money in the long-run.
You will not only get to save money but you’ll get to save time as well. Data scientists spend a lot of time correcting data. If you have correctly stored and managed data, they will be able to devote this time to other, more important tasks.
Have a Competitive Advantage
It’s all about having an edge over your competitors. Since all businesses deal with data in one form or the other, correctly managed data can give you an edge over others.
Keep in mind that poor quality data does not only affect internal matters but can also leave a negative impression on your customers as it affects customer service.
Hence, it is important to maintain the quality of your data.
In addition to this, it can also help you get a true 360 degree customer view. You will never be able to fully understand your customers if you do not have the latest and correct data.
How Can We Measure the Quality of Data?
So how do you know if your data needs assistance or not? The key lies in measuring. You will need reliable metrics for this purpose.
Most experts agree that data analysis is the most complex part of data quality management. Hence, you should study the few basic measurements so you can understand data in a better manner.
- Accuracy: This refers to all changes being implemented in real-time. This way the data will be accurate and up-to-date. The best way to measure accuracy is the ‘source document’. However, one can also count on other confirmation techniques.
Some common errors include an incomplete, missing, or redundant entry. The aim is to remove all these errors and ensure at least 99.9% accuracy.
- Consistency: When it comes to data, consistency refers to a lack of conflict between two or more values. However, it should be mentioned that consistency does not always mean correctness as these two elements are different.
- Completeness: Incomplete data is of no value. You will not be able to reach a conclusion if you do not have complete data.
The best way to ensure completeness is to check each entry. They must all be full. Even a single missing entry can lead to problems.
- Integrity: This refers to data validation. It is important that your data fully complies with all the procedures so that you do not have to face issues in using the data that you have secured.
- Timeliness: It is important for data to be available when you need it. For example, you will need an updated email list to inform users about Christmas discounts before the 25th of December. The list will not be of much use if it reaches you on the 26th.
What Causes Low Quality Data?
Companies gather data through various means. Plus, this data often goes through different stages before it is finally used. A mistake can occur at any stage and will lead to problems when it’s time to put the data to use.
During Collection: Errors can occur during the collection stage. These include software error. It is not very uncommon for software to malfunction resulting in issues.
Collection errors also occur during mergers and acquisitions when data has to be transferred from one firm to another.
During Transfer or Recording: This includes typos and other such errors. According to reports, errors during the second stage are the most common.
Some fields can go missing or get recorded incorrectly, resulting in errors.
During Use: Mistakes can occur at the last stage. For example, you may include outliers or miss one of the values when performing a function. Even a single missing figure can cause the output to skew in one direction.
What is The Best Way to Manage Data?
All that you need is a reliable tool to ensure data is recorded, edited, and used correctly. WinPure Clean and Matching solution can be very helpful in ensuring your data remains usable.
We also offer niche tools including Email Verifiers to verify email addresses. We cater to all kinds of clients and can help clean your data to ensure there are no errors.
Our customer service agents will respond to all your queries and explain how our software can help your business achieve its goals.