How much do you know about data quality management?
Data quality – measured in terms of consistency, accuracy, completeness, and other attributes – can make or break any organization. Data quality management encompasses a series of best practices to evaluate data quality and then clean and repair low-quality data.
Read on to learn everything you need to know about data quality management and how to implement it in your company or organization.
- Data quality measures the consistency, accuracy, completeness, and timeliness of a data set.
- Poor data quality can lead to misguided decisions, inefficient operations, and higher costs.
- Data quality management examines all data, identifies any issues, and attempts to correct those problems.
- Best practices for data quality management include encouraging a focus on data quality throughout your organization, automating data entry, and employing an automated DQM solution.
What is Data Quality?
Data quality refers to the accuracy and completeness of a set of data. Higher-quality data is more useful to any organization. On the other hand, lower-quality data can lead management to make poor decisions and spend money unnecessarily.
How Do You Measure Data Quality?
A variety of factors, all quantitatively measurable, define the quality of a given data set. The key metrics for measuring data quality are consistency, accuracy, completeness, auditability, orderliness, uniqueness, and timeliness.
Consistency specifies that individual data points pulled from separate sets of data do not conflict with each other. Inconsistent data indicates that one or both of the data sets is inaccurate.
Accuracy is different from consistency. Data can be consistent with itself but inaccurate – that is, consistently wrong. Accuracy measures the known errors in a data set in terms of the ratio of data to errors, represented as a percentage. The higher the accuracy, the higher the data quality.
Data can be consistent and accurate but incomplete. Completeness measures the amount of data missing from individual data entries. For example, a customer record that included only the person’s name and not their address is incomplete – even though the name may be accurate.
Auditability reflects the accessibility of the data. It’s important to be able to track any changes introduced to the original data.
Orderliness refers to the format and structure of the data. In general, structured data is higher quality than unstructured data.
Individual data points must appear only once within a larger set. Duplicative records result in lower-quality results.
Finally, timeliness reflects how recent the data is. Newer data is more timely and more likely to be accurate and higher quality. Older data is often less relevant and less useful.
Why is Data Quality Important?
Data quality dictates the usability of the data. Higher-quality data more accurately reflects a given reality, while lower-quality data is less reliable and can lead to poorer decisions. Without high-quality data, an organization is likely to make misguided decisions and waste money doing so.
Benefits of High-Quality Data
Organizations of all types use high-quality data to manage their day-to-day operations and provide input for long-term planning. The more high-quality data a company collects, the more it can be used in various aspects of the business.
According to FinancesOnline, the four most important benefits companies realize from the use of high-quality data are:
- Faster innovation cycles (25% of those responding)
- Improved business efficiencies (17%)
- More effective R&D (13%)
- Better products/services (12%)
Consequences of Low-Quality Data
Conversely, bad data can negatively impact a company’s operations and planning. The consequences of bad data quality are many, and include:
- Poor decisions based on inaccurate data
- Wasted marketing funds based on incomplete customer data
- Erroneous invoicing based on outdated customer data
- Incorrect analysis based on duplicated data
- Inability to conclude based on nonstandard data entry
These and other consequences of low-quality data can cost your company financially. Gartner estimates that 20% of all data is bad, and IBM says that bad data costs the U.S. economy $3.1 trillion per year.
What is Data Quality Management?
You can improve data quality by employing data quality management (DQM). DQM is a set of practices designed to maintain data at a high quality, free of errors, duplications, and other issues.
DQM involves examining existing data, identifying issues such as missing fields or duplicate records, and then “cleaning” the data. You can do this by removing inaccurate records, completing incomplete records, and checking data contents against known data points. Monitoring and cleaning incoming data in real time helps ensure constant high data quality.
While you can manage data quality manually, it’s a thankless chore – especially with large data sets generated from many different sources. A more practical solution is to employ an automated DQM solution. These solutions, such as FirstEigen’s DataBuck, employ artificial intelligence (AI) and machine language (ML) technology to autonomously monitor and clean big data sets without tedious and costly human interaction.
4 DQM Best Practices
An automated DQM solution should be a part of an overall focus on data quality in your organization. To improve the data quality throughout your organization, you need to encourage a quality mindset among all your employees. Here are some of the best practices you should employ.
1. Make Data Quality a Company-Wide Priority
Train all employees, from upper management to data entry, in data quality management. They need to know why data quality is important and how to ensure high-quality data.
2. Implement the DQM Process
The DQM process starts with measuring the quality of your firm’s data and includes designing and implementing processes to improve data quality. It also involves cleaning any bad data and constantly monitoring results.
3. Automate Data Entry
One impactful way to improve data quality is to move from manual data entry to automated data entry. This reduces the risk of human error and ensures that all relevant data is always entered.
4. Monitor Both Master Data and Metadata
Focusing on the master data is essential but so is ensuring the accuracy of accompanying metadata. This includes information such as data entered, data modified, author, file size, and the like that can affect the interpretation and use of master data.
(The following video discusses data quality management best practices.)
Let DataBuck Automate Your Organization’s Data Quality Management
When you want to improve the quality of data in your organization, turn to DataBuck from FirstEigen. DataBuck is an autonomous data DQM solution powered by AI/ML technology that automates more than 70% of the data monitoring process. It can automatically validate thousands of data sets in just a few clicks.
Contact FirstEigen today to learn how DataBuck can automate your company’s data quality management!
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)