By Campbell Pryde, President and CEO, XBRL US
The cost of government regulation is increasingly seen as a burden by many in Congress and the courts who look to limit the scope of regulatory agencies. At the same time, data collected by regulatory agencies is becoming more important for the functioning of a modern economy. With increases in geopolitical, environmental, market, liquidity, counterparty and political risk the need for timely and comprehensive information is essential to navigate these risks. The cost and burden of data collection must be reduced and the accessibility of data must be improved. Both can be achieved through standardization.
Regulators may be reluctant to embrace standardization as they believe it limits their flexibility to regulate as they see fit. This is a trap that must be avoided. Digital standardization enforces a disciplined and structured approach that results in a regulatory framework that is transparent, robust, and unbiased. As regulators consider implementation of the FDTA, it is important to keep in mind what constitutes success: better data, reduced cost, increased flexibility. We should not settle for anything less.
To that end, we urge the Agencies adopt a single semantic data model structure (XBRL) rather than the properties-based approach described in the rule proposal. The proposed approach will result in Agencies continuing to manage discrete, siloed datasets as they do today, that are not interoperable, and cannot be commingled, or automatically shared and inventoried.
Adopting a single semantic data model will help Agencies realize economies of scale, and will reduce costs for regulators and reporting entities, as well as users of the data: citizens, investors, policy setters, and researchers. Benefits of the single semantic data model structure include:
- Data produced by Agency collections will be machine-readable and machine-understandable, eliminating the need for manual data entry and vetting, establishing a common digital language for all stakeholders. Data will be interoperable, shareable and can be commingled and inventoried together. This is feasible because even though the Agency, the reporting entity, and the data reported is different, the structure of the data is the same. The structured, granular, consistent nature of reported data lends itself to artificial intelligence and machine learning applications, which is becoming more critical for regulators, businesses and researchers handling high-volume data.
- Data quality enhancements, because validation (business) rules can provide complex checking for accounting and business rules, completeness, reasonableness checks and more. Validation rule sets created for one data collection can often be easily repurposed for other data collections because of the highly structured nature of the data.
- Economies of scale will reduce regulatory cost to collect and analyze data because all nine Agencies can leverage the same tools and databases. They do not need to build custom applications and can “borrow” from other Agencies, while still maintaining the uniqueness of their own data collection requirements and analysis.
- Economies of scale will reduce costs for reporting entities and data consumers. Software providers with applications that support one XBRL-reporting program often leverage the same applications for other XBRL-reporting programs. Development costs can be shared across many reporting entities, resulting in lower costs for those reporting. The cost of maintaining three separate products for three reporting situations, will be higher than if a single application can be developed and costs shared across many. The same holds for analytical tools. Products used for multiple datasets will be less expensive than products that must be tailored for single datasets.
- Automates Agency collaboration. Following the same semantic data model structure automatically coordinates the work of the Agencies, without the need to establish bureaucratic steering committees to monitor work and ensure collaboration.
- Agencies will have the flexibility to choose from multiple data transmission formats to “transport” their data including CSV, JSON, XML, and XHTML. The standards program will also be set up to adapt to new data transmission formats that may be introduced in future years, because the transmission process is separate from the semantic data model and Agencies can choose the transmission that is the best fit for their data.
- Agencies will be able to update/revise reporting requirements more easily and inexpensively than the manual, paper-based process followed today; data preparers and data users will be able to adapt to updated requirements with minimal disruption. Time series data will remain intact even when reporting needs change.
- Reporting requirements will be kept current and have the flexibility to meet Agency and standard setter needs. Issuers will rely on the accounting standards they use today, for example, IFRS, GASB pronouncements, and FASB pronouncements, which will be kept current through taxonomies developed and maintained by the standards organization themselves. Agencies will be able to require Agency-specific reporting as well through taxonomies they develop that can be used seamlessly by issuers at the same time as taxonomies created by standards organizations.
We also recommend:
- Require a taxonomy/schema for all Agency collections. Even collecting “name” and “address” on an application has a schema with associated definitions, and relationships.
- Re-use existing taxonomies where possible such as the FASB GAAP Taxonomy used by public companies. Reporting entities such as banks and credit unions also adhere to FASB pronouncements. Recreating what is already available is costly, unnecessary and will result in data that is not interoperable.
- Establish a governance framework for the Agencies to facilitate continued sharing of information and standards development.
Furthermore, we urge the Agencies to eliminate potential off-ramps in the final rule that could derail the implementation such as the use of the term “to the extent practicable” which could be an easy exit when there is any pushback from stakeholders. Successful standards programs that yield the benefits outlined above require change and collaboration which is often difficult to embrace, for reporting entities, data users, and for the Agencies themselves. There are ways to ease the path for all stakeholders which could include compliance phase-ins or adopting creative pricing models.
The FDTA promises to provide substantial benefits for regulators, reporting entities, and users of data. We cannot afford to settle for less.
