Unlocking the potential of regulatory data management

Regulators are becoming increasingly focused on addressing their data collection systems to meet a huge increase in the quantities of data that regulators and institutions need to store, access and manage. 

The 2008 crash led to an explosion in the volume of data regulators need. Regulators are battling to ensure they are not overwhelmed by the increased volumes of data and their capacity to process, analyse and understand the data is not diminished.  

There is a requirement to address the amount of time spent on activities that are not pure supervision, including wrangling data volume or data complexity. The portion of time spent on those activities is on the rise, which means the time spent on real supervisory activities is decreasing. Regulators need to look for solutions to that problem. Traditional approaches aren’t sufficient to meet today’s data collection challenges, but technology can help.  

Why regulatory data management is important 

Regulatory data management is the field of designing and developing, publishing and implementing regulatory data collection. It includes everything from the processes, standards, best practices, governance, skills, resourcing, and technology. 

Why is it important? Effective regulatory data management leads to better data outcomes, such as a clearer understanding of reporting requirements. Poor regulatory data management leads to poor outcomes, such as an increased burden on financial institutions or a lack of flexibility with the data. 

Regulators need effective data modelling to gain a clear view of all the data and the relationship between different datasets. This enables them to analyse the data and supervise while encouraging compliance and input from the industry. There are a number of components that need to be addressed. 

A common language: A data dictionary with one definition for each data point ensures there is no ambiguity and zero duplication of data collected.  

Coherent data collections: Optimised data collections that are easily understood, consumed and consistent are more efficient and easier to manage. Users continue using the tools they are familiar with, mainly Excel, but find it easier to get real value from the data. 

Financial Institutions and RegTech enablement: Producing good data models and publishing them in well-defined, machine-readable specifications, helps reporting entities and industry to adapt faster and accelerate the adoption of regulatory technology. 

Format agnostic: Data models need to be format agnostic to allow flexibility to add or replace existing formats, but also to design consistently independent of them.  

This approach to regulatory data management optimises every aspect of the data collection life cycle from collection to analysis.  

APRA shows the way for regulatory data management 

The Australian Prudential Regulatory Authority (APRA) supervises institutions across banking, insurance and superannuation. In 2019, it embarked on a project with us to transform the way it collected, stored, analysed and published data.  

APRA’s existing D2A collection system was over 20 years old and limited from a technical and business perspective. It was dependent on desktop installs at the regulated entities and the platform did not allow for wider and more granular data sets to be analysed for insights. 

APRA was keen to replace D2A with a system that would be more flexible for the regulator and the entities it regulated, be easier to use, have less ongoing maintenance and be adaptable to future needs.  

One objective was to reduce the data collection burden on the industry by enabling self-service updates to the information, especially corporate information, as well as a clearer and easier to use interface to the system. APRA also wanted greater analysis and insight to deliver more understanding of what was happening in the entities, perform more analysis and provide data for internal and external publication.  

Ensuring greater clarity around data requirements would reduce the potential for misinterpretation by both sides when the data was provided and when it was being analysed.  

The APRA Connect external test environment was made available in April 2021 and for production in September 2021. Due to Covid-19, the project pivoted to deliver new 2021 collections, with legacy collections due to be addressed in a later phase. 

A digital transformation project for superannuation running in parallel with APRA Connect involved a major restructure of collections from the superannuation industry over several years. Our Regulatory Data Management (RDM) artefacts are consumable by people and machines using Excel, making them accessible for use in design, build, test, integration and publication processes by APRA and by filers and RegTechs. The transition from APRA’s consultation artefacts to VRDM artefacts was easy and the artefacts are used consistently throughout the whole process. This has resulted in a more accurate build with fewer defects. Any defects are easily traceable back to design artefacts as the build process is more highly automated. 

The benefits of regulatory data management 

There are a range of benefits of regulatory data management, many of which are being implemented in APRA Connect. They include: 

• Optimal industry adoption of data collections,  
• Reduction of the industry burden of regulatory reporting, 
• Quicker and easier to get real value from the data, 
• Easier to manage and analyse data collections, 
• Next-generation supervision (advanced analytics, granular data, machine-to-machine reporting).  

We recently spoke to Chief Information Officer at APRA, Doug Jenkins at the RegTech Convention. He agreed on the many benefits of regulatory data management coming into effect for APRA Connect. It certainly gives “us more flexibility than in regards to how we are analyzing the data and we are using the data. So hopefully look that reduces the burden for the entities in terms of providing the data to us. But as I say, it also gives us those benefits of being able to understand more clearly.” 

Our tried and tested regulatory data management process is established and proven over 20 years of practice specific to the regulatory industry. It includes the processes, tools and training to empower regulators with self-sufficiency and reduce the production time of data collection from the first conception to production. At the same time, it ensures the data collections they produce are best practice and standardised, bringing all the benefits of good data modelling and data management.