Pulling together accurate reports that meet regulators changing demands is a clarion call for bodies and resources to “unwind data” and aggregate its metadata. Failure to do so could mean penalties, fines and loss of trust.
But more resources and endless ETL can actually create more data uncertainties and inaccuracies. In this 60 min webinar ModelDR’s Greg Soulsby and Simon Roberts joins MarkLogic colleagues Chris Atkinson, Rupert Brown and Diane Burley to discuss a new and sustainable data aggregation process.
Designed for data architects and managers, the webinar will equip you for further discussion with interest groups in your organization. You will learn key terms like:
- data point modeling
- congruent panoply
We hope you can join us for this show dont tell event.
Dodd Frank Record Keeping Rules – a lot of data for banks to untangle!
Bank systems have thousands of data attributes, delivered by hundreds of internal and external sources, all stored in dozens of unconnected databases. This fragmentation results in a continual challenge of mapping, cross-referencing and manual reconciliation, further exacerbated by the problem of common terms that have different meanings, common meanings that use different terms and vague definitions that are not captured in recognised financial taxonomies.
The Dodd Frank record keeping rules are directed towards full ‘end to end ‘ transaction monitoring and require an unambiguous data management strategy with full data ownership and accountability.
Dodd Frank record keeping requirements can only be met by data architecture that untangles all firms silo based data systems and harmonizes the data aggregation process. Systems must be ‘wired up’ to data architecture, designed in data point model format that has been updated with the latest regulatory taxonomies.
The Data Point Model ‘wiring up’ process is depicted in a static example form below.
ModelDR untangles the data, imports the regulatory taxonomies and builds a new design architecture. It creates a clear and complete view of ‘in house’ data and allows data quality testing consistent with current regulatory standards.
Dodd Frank Data Point Model