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:
The risk of attempting a data quality review without a framework in place include:
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:
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.
Contact us today
*Adapted from The Practitioner’s Guide to Data Quality Improvement. By David Loshin
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