Successful Data Quality: 5 Best Practices
Data quality is an essential element in your data governance strategy. This means taking the time to develop quality rules to use optimal data that teams will trust. Here are our top 5 tips for creating standard rules for reliable data.
Ensuring data quality: What rules to follow?
Data quality rules are requirements that every organization must implement to ensure data quality and integrity. These requirements aim to meet two interdependent objectives:
- To define the format to which the data must conform and the relationships that must exist between the data;
- To provide a reference for the company to verify and measure its data quality against these requirements.
Improving data quality and ensuring its long-term sustainability is essential for an organization to optimize its decision-making process. Data quality rules aid in the continuous improvement of the data quality level. Data quality management allows you to ensure that you have quality data and to improve the management of this data over the long term to enable teams to have confidence in its use. An information system cannot function with erroneous data! These rules will allow the definition of objectives, according to the nature of the data and the business needs, to set up data quality hygiene and to be able to have faith in its reference data.
#1 Involve the managers of the different departments of the company
The different departments and services of a company have priorities that are not necessarily the same. To develop effective data quality rules, it is essential to define them with the requirements of the managers of each department, in agreement with the various data people.
#2 Have a reasonable number of rules
To move towards better data management, quality rules are a great help, but it is crucial to create a reasonable number of them. Searching for a data item with ten rules is not the same as searching for one with 100 rules; the solution should not become a problem! Therefore, you must find the right balance between consistent data quality control and a certain measure of rule implementation.
#3 Encourage a step-by-step approach
It is not necessary to create rules covering all data right away. Similarly, an organization that is just starting to implement its data governance strategy should not solve the quality rules issue with a snap of the fingers.
For good quality management, it is best to identify critical data that needs immediate attention.
#4 Create rules taking into account each type of data
Many data quality characteristics will allow you to establish rules according to the domain to which a given data belongs.
Data quality examples
The “employee’s full name” data is critical, indispensable information, whereas the “employee’s contact number” data is not necessarily as important. These two data will not have to meet the same quality requirements. While the first data will have to meet the requirements of completeness, uniqueness, and accuracy, the second data will have to be accurate and orderly. These requirements will have to be reflected in the quality rules, for example:
- the employee’s full name must not be N / Y (for completeness) ;
- only one “Employee’s full name” must correspond to one “Social Security Number” (to ensure uniqueness);
- the employee’s full name must have at least one space, and contain only letters, no numbers or other permitted characters (to ensure accuracy and completeness);
- the employee’s phone number must be numeric only (to ensure accuracy and sequence).
#5 Choose between centralized or independent storage of quality rules
This tip is especially for large, multi-directional organizations. You should choose between centralized storage of quality rules, allowing you to have a single rule regardless of which department or branch is collecting the data. In the case of “independent” storage, each department will be able to set its own data rules, allowing it to have its own requirements according to its business.
The right data quality rules can make all the difference in the world. If you are wondering how to get started, don’t worry: there’s no need to reinvent the wheel. If a good set of rules already exists within your organization, find out who’s in charge of maintaining it and ask them for advice. If you don’t have a formalized set of rules yet, that’s okay too. Your company’s data is unique and probably warrants an approach tailored to its needs. Find out what makes the most sense for your organization and start implementing it today!
Don’t rely on poor-quality data. Say no to bad decisions: implement data quality rules with your Data Catalog!