Data Quality Management in Business Intelligence

A good business plan, creative marketing, and excellent customer service are some of the components of running a successful business. You can add many other qualities to the list, but it is always incomplete without comprehensive data management. Many organizations are making important decisions based on their data, but the results of those decisions are dependent on the quality of the data.

Data quality management is a good way to help your business intelligence tools give you a better business plan. In this blog post, I am going to share some insight into the following topics:

• What is data quality management?
• Why do you need data quality management?
• Major causes of poor data quality.
• Essential factors for clean data.
• How to maintain data quality.
• Roles that ensure data quality.
• Impact of data quality on data analysis.

What is data quality management?

Data quality management consists of the processes and practices of constantly maintaining a high quality of information. Data quality management includes the process of identifying poor-quality data, cleaning it, and making it usable with your business intelligence platforms. With the proper data quality management system, you can gain insight into your business from the data and use that insight to improve the growth of your organization.

Why do you need data quality management?

Data enters an organization in various ways, so not all the data is accurate and perfect. It may be outdated, duplicated, or inconsistent. If it is not accurate and consistent, you cannot use it to make important decisions.

Making business decisions based on incorrect and unreliable data could cost you a fortune. Data quality management helps you find the poor-quality data and detect how it is coming into your database. Then you can clean that data and prevent more from entering your database.

Major causes of poor data quality

Many organizations are trying to establish systems to clean poor data. But preventing the poor data from entering the database in the first place is as important as cleaning it. By identifying the causes of poor quality data, you can prevent it. Here are some major causes of poor data quality:

Manual entry

Organizations record data manually into their systems, even though there are many automated ways to do it instead. The process of entering data manually in a database involves many employees. There are many chances for wrong data to be typed into the system.

Acquisition and mergers

The acquisition and merging of a company with your company involves not only the financial or business aspects, but also loads of data. The data entering your company’s database is formatted according to their company’s policies, not yours. There can be invalid formats, duplications, and just wrong data, which are going to affect your analysis quality.

Real-time updates

Gathering data and gaining knowledge in real-time is a fast-paced process. To help you make efficient and up-to-date decisions, data is updated in real time automatically in the data servers. Because of the time constraint, though, there are chances of acquiring incomplete or manipulated data.

Indiscriminate data collection

Many organizations collect all the data they come across, but not all that data is useful to an organization. You should identify and keep important data and avoid collecting unimportant data. Collecting large amounts of unwanted data will become time-consuming to sort through and your storage will be needlessly occupied.

System upgrades

Frequently upgrading your systems’ software or hardware is also a cause of poor data. While upgrading systems, there is a significant chance that data gets deleted or corrupted.

Essential factors for clean data

Having the data alone is not enough to help you make business decisions. The quality of the data is as important as having the data. With quality data, you can do wonders in your business. To ensure that you are using quality data, you should check if the data complies with the following factors.

Completeness

Completeness is having all the data needed for your intended purpose. For example, if a customer’s name and contact number is needed and their address is not needed, then you can consider your data on the customer complete even without their address.

Accuracy

Accuracy is whether the data is compiled without any manipulated or duplicated data. When you have a larger amount of data, then there is often a larger amount of inaccurate data in it. You always need to ensure the data is accurate and reliable. This will increase the quality of the data.

Consistency

When two data values from different data sets are cross-examined, they should be the same. If they are not the same, then you have unreliable data, so you cannot make any decisions based on that data.

Timeliness

The availability of the data when needed is known as timeliness. If the data is not available when it is needed, then there is no use for the data. It should be constantly updated in real time if it changes.

Integrity

The data in your database should have a valid real-world connection. There are chances that you have duplicate or invalid data in your server. For example, if you have a product sales database with customer information. Then, if a customer name is missing for a specific product sold, then it is invalid data and there is no use for the data.

How to maintain data quality

Maintaining data quality is as important as cleaning and processing the data. By following these procedures in your organization, you can ensure the quality of your data.

Collecting data

With the proper data collection plan, you can ensure the quality of the data entering your databases. Categorizing data by the needs of your business lets you avoid confusion among departments and ensures they collect only the data they needed.

Ensure standards

By creating data quality standards, you can categorize wanted and unwanted data. This will improve the efficiency of your data analysis and presentation.

Correcting data

Providing your team with proper guidelines and rules for correcting data gives them a better understanding of the data they need to process and ensures the quality of the data.

Roles that ensures data quality

Since data is an essential factor for many businesses, it is important to make sure that you are using reliable information to make strategic decisions. By providing data quality guidelines and assigning the following roles to individuals or a team, you can ensure data quality in your organization.

Data owner

The data owner is solely responsible for the data within the organization. They have the ability to edit, modify, create, share, and restrict access to the data.

Data steward

The data steward is responsible for the data governance process and managing the data sources. They help find the problems in the data sets and recommend ways to mitigate the risks and ensure the data quality.

Data manager

A data manager is responsible for effective data management and establishing the rules and procedures to handle large amounts of data and oversee the data from the entry point in an organization.

Data users

A data user is the one whose daily tasks include entering data. By following the rules of the organization, they improve the quality of data entry and ensure that little human error occurs.

Impact of data quality on data analysis

When your data collection complies with the qualities listed in this post and passes through these management processes, then it is ready to feed into your business intelligence platforms. High-quality data gives you high-quality analysis, leading to many benefits:

• With a data management system, your data will be more consistent, and the quality of your data will be more reliable.
• Quality data gives you confidence in the decisions that you are making and presents you with desired results.
• With good-quality data, you can improve the quality of your marketing campaigns by identifying the target audience.
• Good-quality data informs you of the needs of customers and helps you improve the quality of your products and services.
• High-quality data gives you an advantage over your competitors who don’t have it.
• You can predict the purchasing patterns of customers and increase the growth of your company by suggesting more related products to them.
• By integrating your quality data with business intelligence tools, you can visualize emerging trends in your market and make key decisions according to them.
• Maintaining quality data enables you to reduce the costs of managing poor-quality data.
• A data quality management system helps you monitor the growth of your organization, find weaknesses, and correct them.

Conclusion

Integrating your business data with Bold BI’s solution dashboards helps you to visualize the data in real-time and monitor your organization’s important KPIs using a variety of widgets. I hope you now have a better understanding of data quality management, the causes of poor data quality, the essential factors and important roles that ensure data quality, and the benefits of data quality management.

If you have any questions on this blog, please feel free to post them in the following comment section. To get started with Bold BI, please request a free 30-minute demo with our experts to discuss creating dashboards and any other features you would like to learn more about. You can also contact us by submitting your questions through the Bold BI website or, if you already have an account, you can log in to submit your support question.

Originally published at https://www.boldbi.com on December 4, 2020.