The Dimensions of Data Quality: Currency and Consistency

Posted on by

4th of a 4-part series

Previous posts have provided an overview of data quality drivers and their associated dimensions. To recap, the dimensions covered include:

  • Integrity
  • Accuracy
  • Completeness
  • Duplication
  • Currency
  • Consistency

This post presents more detail on what is meant by data currency and consistency.

Data currency

Data currency is not a financial reference, it’s a temporal reference: “the degree to which the data is current with the world it models,” as the Data Management Body of Knowledge (DMBOK) suggests. You may have once had the right information about a server or other IT asset in your database, but then it was renamed, moved or re-purposed. The data is not current and must be updated.

Updates can be manual or automatic. They can take place on an as-needed basis or they can be scheduled periodically, it all depends on your business requirements. The business rules that define your approach to this are called “data currency” rules.

Data currency is a familiar issue for IT asset repositories and configuration management databases (CMDBs). Too often, such projects are funded as a one-time capital project, which means it’s a challenge to obtain support for ongoing operations. Without steady-state operational processes in place to maintain the data, they will inevitably decay, and the entire capital investment is then at risk as the repository loses credibility.

Guarding against this requires strong executive support for, and a continuous improvement approach to, data quality issues. The value of keeping the repository up to date should be assessed, and conversely the costs and risks of data inaccuracy must also be factored for long-term success. 100% data accuracy and currency is neither affordable nor required – but what is an acceptable level? 95%? 98%? Only you can answer this question for your organization, in terms of such factors as:

  • Prolonged system outages
  • Redundant researching of the same IT data
  • Increased security and financial risk due to lack of awareness
  • Other risks, many of which may not be obvious

Data consistency

Data consistency is related to both data integrity and data currency. It applies whenever data is maintained in two places; DMBOK summarizes it as “ensuring that data values in one data set are consistent with values in another data set.” If you designate one system, such as the CMDB/ITAM (IT Asset Management) repository as the system of record for servers, and another system (for example, a monitoring system) as a downstream replica, then you should run periodic consistency checks. Are the servers in the monitoring system still recorded in the CMDB? Are there disposed servers in the CMDB that the monitoring system still records? Are there servers in the monitoring system that have never been entered into the CMDB?

Data can be assessed for consistency in terms of its relation to data in other systems, or even within the same overall system. For example, a high-performance system that may cache frequently accessed data also runs the risk of potential consistency issues.

All such issues, whether between or within systems, should be flagged through exception reporting and investigated, then fixed. The process gaps that caused them to occur must also be investigated and fixed; perhaps discovery needs to be run more frequently, or different approaches to identifying exceptions are needed. Finding and fixing such problems may require a few rounds of experimentation, but these are baseline requirements for any efficiently run IT infrastructure.

Conclusion

Data quality is not a mysterious art, but rather a defined set of practices that are well established in the data management professional community. This set of articles has looked at the six dimensions of data quality:

  • Integrity
  • Accuracy
  • Completeness
  • Duplication
  • Currency
  • Consistency

By understanding their definitions, and developing clear methods for measuring and improving them, you can add significant value to your CMDB and IT Asset repositories, the IT service management processes they support, and ultimately your business, with its increasing dependence on digital technology. Companies that learn this important lesson and stay one step ahead of their IT requirements are those that thrive. At its core, this is dependent on the quality of the underlying data.