Improving Data Quality with Data Governance

Understanding the quality of data appears to be easy enough; data is either right or it is wrong. But is it really as easy as that? I’m sure that you must have come across many examples of ambiguous or erroneous data and their unintended consequences in the popular press.

Here’s a few examples:

  • Incorrect medical procedure: Data needs to be accurate (Fig.1).
  • Who made the commercial flight to space first, Richard Branson or Jeff Bezos? Where the Earth’s atmosphere ends and space begins is apparently ambiguous and open to interpretation: definitions of data measurements matter (Fig.2).
  • Aircraft flies to wrong location: Data needs to be timely (Fig.3)

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So, what causes poor data quality? There are many ways that erroneous data can enter your system, but they all fall into two major categories: human error and IT systems challenges.

These can be categorised through the following data quality attributes:

Duplicated: Avoid data duplication, there should be only one source for the data.

Accuracy: The values contained in each field of the database record should be correct and accurate and in accordance with their metadata definition.

Completeness: The data should contain all the necessary and expected information, and the scope of the data element should be understood by the user and in accordance with their metadata definitions.

Consistency: Recorded data should be the same throughout the enterprise and across all IT systems.

Conformity: Data should conform to certain standards of type, size, format, etc. and in accordance with their metadata definition.

Integrity: The data should be valid across applicable relationships.

Timeliness: The data should be available and valid in accordance with the user’s expectations when it’s required.

Data quality: is measured in terms of these attributes. Enterprises need high quality data that they can trust to make decisions. Any lack of trust hinders the enterprise to use their data to make effective decisions. The cost of poor data quality can be measured in terms of financial, productivity, reputation, and impact on the well-being of life costs. At best the consequences of bad data can be annoying but easily remedied, at worst they can be catastrophic. To prevent poor data quality, you want to move away from fixing bad data to preventing it from occurring in the first place, this is where effective data governance becomes crucial.

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Data governance is the collection of practices and processes to standardize and automate the management and use of data within an enterprise. It is a system of authority and control over the management of enterprise data assets. Data governance provides a framework for collaboration through an agreed and shared set of governance processes, responsibilities, and assets.

Data quality and data governance can be considered as two sides of the same coin; you can’t have one without the other as there is a mutual interdependence. Between them, the practical application of tender loving care ensures that data that is valued will flourish.

However, it is often common for organisations to commence data quality initiatives without implementing a data governance practice and framework to support them. Sadly, this leaves many data quality initiatives as merely tactical solutions which only deliver short-lived improvements.

Enterprises are often daunted by the task of Implementing a data governance practice and framework as it involves a set of complex interactions and systems that need to work together effectively. But what if you could draw upon the expertise, best practices, and knowledge of others that have followed this path before and accelerate your implementation of a data governance practice?

This is where Sandhill’s COMPASS Data Governance Management System solution can be used to manage the complexities of implementing data governance.

Watch our COMPASS Webinar to see what COMPASS is all about and how it can help you here:

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Figure 1 – 2019. Man circumcised by MISTAKE in hospital blunder after medical notes mix-up. [Online] [Accessed 21 October 2021]. Available from:

Figure 2 – Helmenstine, A. 2021. Where Does Space Begin? The Kármán Line. [Online] [Accessed 21 October 2021]. Available from:

Figure 3 – Hope, K. 2019. BA flight lands in Edinburgh instead of Düsseldorf by mistake. [Online] [Accessed 21 October 2021]. Available from: