Data has become an organization’s most valuable asset nowadays. Not that all data is valuable, the only data that can be trusted is. Working with untrustworthy data can easily lead to incorrect insights, skewed analysis, and poor decisions.
The terms data quality and data integrity are used to describe the state of data. The two terms are frequently used interchangeably but are very distinct. A company that needs to maximize the consistency, accuracy, and context of its data to draw insights and make better decisions needs to understand the difference between data integrity and data quality. We begin by defining them before describing how to ensure them in your organization.
What is Data Quality?
Data quality can be defined as a data’s ability to serve its intended purpose. It is referring to the reliability of data. Quality data is defined as being complete, unique, valid, timely, and consistent.
What is Data Integrity?
Data integrity is defined as the dependability and trustworthiness of data throughout its lifecycle. Data integrity can be defined as the state of your data or the process of ensuring data accuracy and validity. Checking for compliance with regulatory standards such as GDPR is one method of ensuring data integrity.
Understood the difference between how do we ensure data quality and integrity? We accomplish this by outlining some steps.
Accurate data gathering requirements:
Its aim is to meet the requirements and deliver the data to clients and users for the purpose for which the data is intended. The data requirements should include the current state of the data, as well as all data conditions and scenarios. Proper requirement documentation, as well as easy access and sharing, must be enforced. Finally, an impact analysis is performed to ensure that the data produced meets all of the expected requirements.
Monitoring and cleansing data
Monitoring and cleansing data entails comparing data to standard statistical measures. It entails validating data against predefined descriptions and identifying relationships within the data. This step also verifies the uniqueness of the data and assesses its reusability.
Access control
Access control goes hand-in-hand with audit trails. People within an organization who do not have proper access may have malicious intent and cause serious harm to vital data. Systems should ensure that audit trails are clear and tamper-proof. These are not only a precautionary measure, but they also aid in the detection of problems when they occur.
Validate data input
A good system should require input validation from all known and unknown sources of data. Users, other applications, and external sources could be data sources. All data should be verified and validated to enhance accuracy.
Remove duplicate info
Sensitive data from an organization’s repository can end up in a document, spreadsheet, email, or shared folder, where users without proper access can tamper with it and introduce duplicates. Data quality and integrity are ensured by cleaning up stray data and removing duplicates.
Back up
As important as removing duplicates to ensure data security is, backing up the data is also an important part of ensuring integrity. Backing up is critical and goes a long way toward preventing permanent data loss. Data backups should be performed as frequently as possible. To ensure maximum security, encrypt your data. Backups are useful when there is a security breach, such as an attack.
Good data quality control teams
There are two types of teams that play a critical role in ensuring high data quality the Quality Assurance and the Business Analysts teams. The quality assurance team inspects the quality of software and programs installed at the beginning or during the data lifecycle. It is the team that change management to ensure data quality in an organization undergoing fast transformations as well as changes with applications that are data-intensive. The business analysts team, on the other hand, is well-versed in the company’s rules and requirements. It is the team’s responsibility to detect data anomalies, outliers, broken trends, or unusual events that occur during data production.
Summary
In all modern organizations and enterprises, data quality and integrity are critical for the accuracy and efficiency of all business processes and decision-making. Data integrity and quality are also a central focus of most data security programs. These two goals are met through a variety of standards and methods, such as accurate data requirements gathering, access control, validating data input, removing duplicate data, and frequent backups.
Check out data quality platforms like DQLabs, which can help you with the entire data lifecycle for your organization or business.
