Reliable data is foundational to good decision making. Data Integrity is an critical requirement, which is defined in many ways. The Technopedia.com definition of Data Integrity linked here focuses on three key attributes: completeness, accuracy and consistency.
Below are the three key attributes explained in an IT Service and Operations Management context:
Completeness: A data record, such as a description of an IT asset, must be complete to satisfy the needs of all its consumers. For example, IT Operations cares whether the asset is active, as well as its location, while Finance wants to manage attribution of software licenses. Gaps in the attribute data can impair an organization’s ability to manage the asset.
Accuracy: Wrong or misleading data helps no one. The cause of inaccuracy can be due to manual input errors, or mishandled conflicting data between sources, as well as from ineffective IT discovery tools that miss or double-count an asset.
Consistency: This is one of the harder data integrity issues to resolve. If you only have a single source of data, then it is likely to be consistent (although potentially consistently wrong). To truly verify the data, , it must be validated against multiple sources. Deciding which source is the most accurate is complicated and establishing automated precedence rules can be challenging without the right tool.
Achieving and maintaining data integrity can be done using various error-checking methods, such as normalization and validation procedures. Blazent’s Data Integrity platform was designed to make data management an integral part of the enterprise information flow through a scalable automated process for exception handling.
To learn more about the importance of good data integrity in an IT Service and Operations Management context, read Blazent’s white paper on Data Powered IT Service Management white paper here.