Data Quality Rules That Every Organization Should Live By

The data collected by organizations is only as valuable as its quality. Poor data quality leads to bad decision-making, inefficiencies, and wasted resources. To ensure that your data is high-quality, you must follow certain rules. Keep reading to learn more about data quality rules and how they benefit your organization.

What is data quality?

Before we explain what the data quality rules are, let’s first define data quality. Data quality is the accuracy, completeness, and timeliness of data, and it is a critical factor in making sound business decisions. Data quality starts with the data collection process. There are several factors to consider when designing a data collection process, including the type of data to be collected, the target population, the sampling method, and the data collection instrument. Businesses use the results from the collected data to make informed decisions about products, services, and strategies.

What are the data quality rules?

The first rule is to have a clear understanding of the business goals and objectives. This means that everyone in the organization, from top management to individual contributors, understands what is expected of them concerning data. Without this shared understanding, it will be challenging to ensure that data is accurate and meets the needs of the business. The second rule is to establish data governance processes and practices. These processes and procedures help ensure that data is collected, processed, and used consistently across the organization.

They also identify and manage any risks associated with using or relying on data. The third rule is to use good information architecture. This includes designing databases and schemas that are easy to understand and use, as well as creating meaningful labels for fields and columns. Well-designed information architecture makes it easier to ensure the accuracy and completeness of data. The fourth rule is for effective ETL (extract, transform, load) processes.

These processes move data from one system or format into another controlled manner. Good ETL processes help reduce errors in data transmission and improve the quality of data overall. The fifth rule is to perform regular quality checks on all aspects of data management operations. This includes verifying that input data is correct, checking database indexes for accuracy, and more. By performing these checks regularly, organizations can catch problems early on before they cause significant damage downstream.

What are the signs of bad data quality?

There are many signs that data may be of poor quality. Some of the most common include inconsistent data, invalid data, out-of-date data, duplicate data, and incorrect data. Inconsistent data happens when data is entered inconsistently into different systems or updated manually without following a consistent process. For example, a customer’s name might be spelled differently in other systems, or the same customer’s age might be entered as both 27 and 28.

Invalid data is a result of incorrect input or data that has been corrupted in some way. For example, a product’s price might be entered as $1,000,000. Out-of-date data is data that has not been updated regularly or has been archived and is no longer accurate. For example, a customer’s contact information might be accurate as of last month but not accurate anymore. Duplicate data is data that has been copied and pasted multiple times or data that has been entered more than once. For example, a customer’s name might be entered into the system twice, with two different addresses.

Incorrect data is caused by data that has been entered inaccurately or by data that has been mistranslated. For example, a customer’s name might be entered as “John” when it is actually spelled “Johnathan.” Incorrect data can also be caused by data entered in the wrong field. For example, a customer’s phone number might be entered into the address field. Or, a customer’s purchase might be recorded as a sale instead of a return.