Insights from dtsquared
Case Studies
More than tools and applications, we work side-by-side with our clients as advisors and long-term data partners. We utilise the Power of Data to demonstrate tangible value before, during and after every project for every client. Our team of experts have successfully implemented numerous solutions in a variety of sectors including investment banking, insurance, utilities, media and public sector organisations.
Innovating & Optimising in Financial Services
Case study
With investment in technology and infrastructure having been limited for over ten years, our client, a large, global financial services company, asked us to help them innovate and optimise their data. The client required us to create and implement a strategy and ongoing support that would guide their data activity and optimisation through multiple years. This involved establishing brand new departments across both the UK & the US and empowering their staff more widely, to make sure the organisation was equipped with the skills, knowledge and behaviours required in order to motivate their people and maximise the data strategy we implemented.
Building a brand new platform for a top global bank
Case study
Our Client, a top global bank, wanted to initiate a brand new Data Governance platform that aligned to their wider Data Strategy, replacing their current and limited system. This involved many aspects including undertaking retrospective data discovery, creating and implementing an enterprise-wide Data Dictionary and Operating Model alongside seamlessly migrating their data from existing platforms into a newly created solution that would help them manage their data in a fully optimised, compliant way.
Building robust data strategies for a Social Housing client
Case study
One of the UK's largest social housing providers recently engaged us to help them with their data strategy. Offering more than 28,000 homes, ranging from affordable social housing through to private rentals, they needed a robust framework which included configuring their Data Governance and Data Quality platforms, defining and monitoring their critical data elements and developing governance, controls and lineage.
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