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A-Team Insight Blogs

ModelDrivers Outlines the Potential of Data Point Modelling to Meet Regulatory Demands

ModelDrivers is piloting its ModelDR data point modelling solution at a large US headquartered investment bank that initially intends to use the software to support MiFID II reporting. To date, the European Banking Authority (EBA) is the only major financial services organisation that has adopted data point modelling – the authority’s Common Reporting and Financial Reporting requirements are based on a data point model – but ModelDrivers expects further adoption as banks struggle to achieve regulatory compliance using legacy, and often siloed, data management systems.

The concept of data point modelling is not new, as well as EBA’s adoption, it underlies the XBRL (eXtensible Business Reporting Language) standard format for reporting, but banks are only now beginning to explore its potential.

Greg Soulsby, co-founder of ModelDrivers and former chair of a working group at XBRL International, says the company’s pilot project should come to fruition in the near future and that it is talking to half a dozen additional banks about data point modelling.

He explains: “Data point modelling is a design level practice that deals with the logic of data rather than physical data. It can dice data in existing systems such as databases and data warehouses into small blocks that can be wired together on the fly as new business demands emerge. It can also be used to model regulations and turn them into data, which means systems data and regulatory data can be in one place, in a congruent format, and can be wired together as necessary.”

As well as providing a flexible data management model that can be used to create solutions for compliance with numerous regulations, data point modelling could play into increasing regulatory demand for ‘cubes’ of multi-dimensional data rather than traditional reports, and support the requirement of Basel’s BCBS 239 regulation for banks to have a logical data model. There is also potential for reduced cost and improved efficiency as speed and agility can be gained without the need to implement new systems.

ModelDrivers’ ModelDR product reverse engineers data schemas from existing databases, spreadsheets and reports; designs data solutions in standardised financial taxonomies; creates data architecture and dictionaries necessary for generating new queries and reports; and provides access to metadata for those who need it.

By providing data congruence across disparate legacy systems, ModelDR integrates siloed data and removes the cost and time of building data warehouses. It also enables the implementation of a standardised and universal financial language, and provides tooling to ensure data elements and attributes are precisely defined, aligned to meaning, described as metadata and managed across the data lifecycle.

As a small company set up just over a year ago, ModelDriver works with partners to take ModelDR to market. Among them are consultancies and technology specialists such as MarkLogic, which can integrate and implement its semantic technology with ModelDR, bringing meaning to data and making it easier for banks to generate solutions and answer questions. Soulsby concludes: “Banks can’t go on using complex and siloed systems in today’s regulatory environment. Data point modelling and semantics are not a ‘nice to have’ solution, but powerful tools for next generation data architecture.”

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