Our Approach

How we work

Our team of consultants work closely, collaboratively, and in an agile manner through scoping, data-discovery, design and implementation. Our methodology in the preliminary stages involves phases and iterations of scoping and discovery workshops. Subsequent phases involve close collaborative development, ensuring functionalities and data are right for your requirements and goals. Throughout, we listen, understand, and identify your requirements and challenges – our ethos is to be our client’s partner, not merely a vendor. The ways we work are collaborative, focussed and thorough – adding value and insight early to provide a best basis for development or implementation.   
 
Our Data Register forms part of this fundamental methodology. The data register is an information asset inventory. It is built from engagement with data owners and key stake holders. Its purpose is to understand your data landscape in terms of content, purpose, quality, dependencies, volumes, interface interactions, dataflows, the mastering of data, age and retention of data (archive concerns), as well as security and compliance requirements. This is part of our consulting process, part of our implementation approach, and part of all data service projects. 
 
Broadly, we our working process follow three distinct stages, progressing through three distinct levels of consultation and analysis.  

Beyond these stages, after go-live, our consultancy teams provide ongoing added value with post po-live developments and integrations as required. Such changes can be done by our consultants or lead by our clients who can call upon our support as and when it is needed.  
 
We also serve as data partners in wider transformation projects and can assist with migration, cleansing, insight and analysis drives associated with the core service and/or product. We can offer Data Science skills to build on the integrated dataset our platform provides. Many of our clients have benefited from our Microsoft Azure Machine Learning capability for predictive profiling and big-data matching technology bolt-ons. These features enhance the quality of matching across huge datasets and provide a new dimension to the insight and analysis, for example in terms of risk and profiling vulnerability, customer behaviour, asset management, and fraud.