Data quality dimensions: Consistency
In this latest post, we look at one of the DAMA six dimensions of data quality – consistency.
The absence of difference, when comparing two or more representations of a thing against a definition.
Analysis of pattern and/or value frequency.
Essentially we’re wanting to ensure that where this entity is recorded across two different systems or data sets, that the entity is recorded consistently.
Inconsistent data can be the source of many problems in your business. For example, consider a property asset recorded as being gas heated in a housing management system, but electric heat is defined in the asset management system. These types of errors are common where system integration/data feeds takes place and the respective applications are used to independently maintain the data. Imagine having a property fuelled by gas, but the same property not being present on the gas service register because the feed of data came from the asset management system and not the housing management system! For landlords this type of error is a criminal offence with the CEO ultimately culpable. However, the consequences to the tenant could be a lot worse, a potentially serious safety matter. NB: At time of writing (May 2021) there have been at least two explosions caused by gas at houses in the UK in recent weeks.
Consistency of data may however not always be across systems, it is sometimes an issue within a record. For example, you have have an attribute that states a person’s preferred communication method is email, yet the email address field is empty or invalid.
Infoboss enables the establishment of rules for cross-checking of data across system data sets and even data attributes within the same record. The ability to identify consistency issues especially across systems and processes is a major benefit of the infoboss solution.
To discover more about how infoboss can help support your data quality and data protection initiatives, please get in touch.