What is Data Quality Management?

In the age of analytics, data quality plays a critical role in helping organisations achieve better and more sustainable results. However, as it is a complex issue often with distributed ownership, many organisations fail to tackle the issue of data quality holistically. Siloed business units and individuals can have different points of view of what data quality is and where data quality issues may exist. Unconnected projects can pop up that may not tackle root causes and may not work together towards a common goal. Because data is such an intrinsic part of the way we do business today, we see data quality as a foundational element of organisational effectiveness.

What is Data Quality?
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Data Quality is basically the shape that all your information is in.  Is your company's data fit for purpose? Is it complete, accurate and reliable?

Our clients rely on data driven insights, whether it is to develop key strategic initiatives or to help improve relationships with clients via marketing and servicing. The quality of data will determine your ability or inability to solve business problems and will greatly influence your ability to make sound and accurate decisions. In short, the impacts data quality cannot be underestimated.

Defining Data Quality Dimensions

Data quality management  is multifaceted, so when defining data quality in your organisation, it is important to create a common language and understanding of​ the dimensions of data quality.

Here's an example of  6 dimensions of data quality that are useful in defining data quality.
 
1. Consistency - Is there only one version of the truth? Can you compare data across data sets reliably?
2.  Completeness – Do you have all the information you need? Are your key data attributes populated for your data set?
3. Accuracy – Is your information correct? Have you a process to manage errors?
4. Uniqueness – Does the information you have uniquely describe each individual? Can you identify a unique individual across data sets?
5. Timeliness – Is the information fresh? Do you have real time access to the data?
6. Validity - Is your information in the correct format?  Make sure that the data you have is user friendly and aligned to business rules.

Data quality can be a very complex and challenging business problem to solve, but breaking down the problem and reaching a common understanding of what the problem is, can be a helpful first step to commencing a well designed Data Quality Management Strategy.
 
Collagis is committed to helping businesses like yours to optimise workforce and organisational effectiveness.
We'd love to  share with you how we can help address data quality in your organisation.

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