Changes are coming to the way federal agencies collect and analyze financial data.<\/p>\n\n\n\n
Of course, at a high level this is no surprise. As part of the implementation of the Digital Accountability and Transparency Act, the Department of Treasury and the Office of Management and Budget established a non-proprietary data standard governing reporting of federal spending by various agencies. Structured financial data reporting provides transparency over how our tax dollars are spent.<\/p>\n\n\n\n
Additional legislation \u2013 the Financial Transparency Act \u2013 would require major U.S. financial regulatory agencies to use consistent data standards when collecting data from federal contractors and grantees. By collecting electronic information about spending in a structured data format rather than a haphazard mix of paper, PDFs and plain-text HTML documents and using a non-proprietary identifier, regulators can streamline the disclosure process. That would make the data more useful not only for government agencies\u00a0but also for investors, markets and taxpayers.<\/p>\n\n\n\n
With these clear benefits on the table, broader implementation of standards-based data reporting is no longer a question of if, or even when. The question that regulators must now tackle is how.<\/p>\n\n\n\n
At the Data Coalition’s Second Annual Financial Data Summit held March 28, more than 300 policymakers, regulators and industry leaders gathered to discuss the current formats and processes that financial regulatory agencies use to collect information.<\/p>\n\n\n\n
As the U.S. financial regulatory agencies move from collecting information in plain-text documents to collecting that same information in an open, searchable data format, there were three major takeaways from the event that the regulators need to consider:<\/p>\n\n\n\n
1.\u00a0<\/strong>Identify and agree on standards in advance.<\/strong><\/p>\n\n\n\n It’s a structure we all know on a gut level, but it bears repeating: do the legwork of aligning key stakeholders around a set of data standards before beginning the implementation process. Making those decisions in advance can preempt a huge number of problems related to misunderstandings and mismatched priorities.<\/p>\n\n\n\n 2.\u00a0<\/strong>Don’t reinvent the wheel.<\/strong><\/p>\n\n\n\n The process of collecting and analyzing structured data is no longer a new one. There are multiple examples of what works and what doesn’t.<\/p>\n\n\n\n In particular, outside the U.S., a number of countries have adopted comprehensive structured data programs across government agencies. In Australia and the Netherlands for instance, the government collects financial data from companies in a central repository that all agencies can access. This reduces the regulatory burden on companies, who only need to file once, ensures that agencies are working from consistent data, and makes it easy for regulators to pull out exactly the information they need.<\/p>\n\n\n\n Even in the U.S., we have successes to draw on. For instance, at the Summit, Alan Deaton, acting associate director in the statistics branch of the FDIC, spoke about the success they’ve had both collecting and using structured data. The FDIC has not only created a process that works, driving measurable improvements such as the ability to review more call reports with fewer examiners and ensure that companies correct errors more quickly. The FDIC has also saved hundreds of thousands of dollars on an annual basis by requiring that banks report financial information using structured data format.<\/p>\n\n\n\n