Assessing Your Data Quality

 Assessing Your Data Quality  

Assessing the maturity of data quality in your organisation can be difficult. Data Quality ownership may be distributed across individuals and business units, with those responsible holding varying points of view of issues and priorities. A data quality framework can help ensure that an assessment is complete and systematic, providing coverage across multiple inter-dependent data quality dimensions.

Benefits of using a Data Quality Framework

A Data Quality framework can help you assess data quality confidently and objectively. For example, they can be useful in exploring:

• What does the business need and expect in regards to data quality?​
• ​Does data quality mean the same thing to everyone in the organisation? ​
• Do we have the right tools and controls in place?​ 
• Who makes decisions about what information we need and how it should be used? 
• Do we know what our obligations are in terms of legislation and compliance wherever we operate? 

The risk of attempting a data quality review without a framework in place include:

• Multiple projects addressing the same data quality dimension, leading to duplication and wastage.
• Key aspects of data quality being missed, particularly root cause issues, e.g. data ingestion processes
• Resourcing  projects based on subjective measures, rather than prioritising based on business value.

Dimensions of a Data Quality Framework*

It is important to be mindful of the full breadth of data quality dimensions when assessing your data quality and formulating a framework.  The dimensions of your data quality framework should include aspects like:

• Governance – Management and oversight of data at the expected level of quality 
• Technology – The tools and platforms that support data quality protocols and processes 
• Policies – The data management policies that governs data collection and usage
• Procedures – The operational aspects of a system that implement, inspect, and validate your data management activities 
• Standards – Ensures that there is conformity with both the internal and external exchange of information.   
• Measurement – A performance management program is used to continuously manage data enterprise-wide. 

Tracking maturity over time

After defining your framework, you're ready to conduct a maturity assessment. This will help to provide a baseline of where your organisation is performing well versus areas for improvement. The maturity assessment and framework dimensions can now be used in tandem to track and prioritise projects that will  progress your organisation towards data quality maturity.

Outcomes of your data quality strategy can be measured through identifying a small number of data quality metrics. It is helpful to narrow down to the "metrics that matter", so as not to overwhelm with analysis paralysis, choosing the key leading and lagging indicators of data quality performance. Easy visualisation and frequent inspection can be enabled through the use of out-of-the-box CRM dashboards and simple governance practices to support ongoing improvement.
 
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 you address data quality in your organisation.
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​*Adapted from The Practitioner’s Guide to Data Quality Improvement. By David Loshin