We defined and articulated a detailed Data Quality Management approach, prior to recommending and overseeing the implementation of Experian Pandora, a full-capability data quality platform.
Through a brief period of consultation, we identified the three areas of data governance to provide the biggest return on investment:
- Firstly, our client was dealing with data quality issues, but in a fragmented, departmental way. This led to siloed, inconsistent approaches and duplication of effort. Our data governance specialists worked with key business stakeholders to identify a suitable technology platform, and devised a data quality management approach which aligned with broader corporate objectives. Our approach was conceptually sound, but more importantly it was pragmatic enough to be adopted across all levels of the business. We mobilised Data Owners and Data Stewards to take ownership of the business data quality, leaving the BI team to focus on BI. The result is significantly increased awareness of data quality challenges, and a standard, defined approach to issue resolution. Data stewards are automatically notified of quality issues, leading to faster identification and resolution, and workflows ensure that DQ issues cannot go unnoticed.
- Secondly, despite being at the forefront of their industry, our client suffered from an all too common problem, namely leaving their business glossary languishing in Microsoft Office. Again, our data governance specialists were able to guide the client through the definition of their business glossary requirements, aligning with the broader data governance standards we’d previously defined. This facilitated a focused effort on documenting consistent and agreed business terms, with a practical governance structure and tools to enforce a suitable approval workflow. Business terms and definitions are now used consistently throughout the organisation, and provide previously hidden information about data context and usage. Furthermore, the cross-functional alignment of terminology has supported faster project delivery, as business analysts no longer need to spend time negotiating agreement on definitions.
- Finally, in an environment of increasing regulatory compliance commitments and toughening privacy legislation, our client needed to understand the criticality and sensitivity of the data they held. We developed a Data Classification policy to enable data owners to accurately assign sensitivity and criticality levels. These classifications allow the business to focus data quality efforts on those areas with the most significant business impact, while ensuring that risk and compliance are managed proactively. Critical data is now managed proactively in terms of data quality, risk and compliance, and has provided new perspectives on physical and digital security measures required to keep sensitive data safe.