Standard Business Reporting: Open Data to Cut Compliance Costs
This blog post is adapted from an excerpt, published with permission, from the Data Foundation’s forthcoming research paper on Standard Business Reporting (SBR), which applies open data to regulation to reduce compliance costs. The research paper will be co-published by the Data Foundation and PwC in early 2017. The Data Coalition’s final breakfast panel of the year, presented by PwC on December 7, 2016, explored the potential of Standard Business Reporting for U.S. regulation.
UPDATE: The Data Foundation and PwC co-published the research paper on March 13, 2017. The research paper is available here.
Regulatory compliance imposes heavy costs on the private sector. A 2014 study commissioned by the National Association of Manufacturers, for example, estimated that U.S. federal regulations cost businesses $2.028 trillion annually. A survey of manufacturers indicated (page 22) that full-time staff and consultants devoted to regulatory compliance represented the majority of these costs.
Regulatory compliance imposes heavy costs on government, too. In fiscal year 2016, the U.S. Securities and Exchange Commission estimated (in its Congressional budget justification, page 16) it would spend over half its budget to “foster and enforce compliance,” the Federal Reserve Board of Governors’ banking supervision and regulation division was its most expensive (see table 3 of its budget), the Internal Revenue Service planned to invest over one-third of its budget on enforcement (see page 4 of its justification), and the Census Bureau’s most expensive program, aside from the five- and ten-year economic and population censuses themselves, was the one charged with maintaining its Business Register, with information on over 31 million U.S. business establishments (General Economic Statistics program, see page 24 of budget justification).
For the private sector, regulatory compliance involves compiling information and reporting it, at periodic intervals or when triggering events occur, to government agencies. For government, regulatory compliance involves receiving, reviewing, and acting on that information. For the private sector and government alike, these tasks involve a great deal of manual labor.
In all developed countries, including the United States, regulatory compliance is fragmented by industry and purpose. Government agencies specialize in tax, securities, banking, statistics, workforce, environmental, and many other matters. Each agency, separately, has legal authority impose restrictions on, and collect information from, regulated entities.
Agencies’ reporting requirements overlap. For example, a 2011 study found that a large U.S. company was obliged to report the same information, packaged differently, to the Securities and Exchange Commission, Federal Reserve, Census Bureau, and Bureau of Economic Analysis (see page 10).
Around the world, governments are choosing to transform their information from disconnected documents into open data. The term open data refers to information that is both standardized, using consistent fields and formats, and also published and freely available to its users.
Open data promises to cut companies’ regulatory compliance costs in two ways. First, if government agencies standardize data fields and formats for the information they collect, rather than expressing that information as unstructured documents, businesses’ software can automatically compile and report it, reducing manual labor. Second, if multiple agencies align their fields and formats with one another, by adopting universal standards for overlapping information collections, software can automatically comply with multiple reporting requirements at once, eliminating the duplicated effort of overlapping reporting requirements.
Meanwhile, open data promises to cut governments’ regulatory costs by allowing agencies to apply data analytics technologies to regulatory information more cheaply. In the United States, for example, simple data matching could have revealed Bernard Madoff’s fraudulent activities before his financial firm collapsed, allowed agencies to quickly gauge the financial industry’s exposure to Lehman Brothers while deciding whether to initiate a bailout, and indicated that the fuel cell manufacturer Solyndra was the riskiest recipient of a federal loan guarantee well before its 2011 bankruptcy – if the relevant information had been available as standardized data. But because Madoff’s securities reports, Lehman’s financial filings, and Solyndra’s energy and securities disclosures were available only as disconnected documents, these insights would have required expensive, time-consuming, and purpose-built analytics projects.
Most countries, including the United States, have not yet begun to apply open data to regulatory reporting in this way. But there are two prominent exceptions. The Netherlands and Australia have embraced an approach known as Standard Business Reporting (SBR). SBR brings multiple government agencies together to define consistent data standards across their compliance requirements. In both the Netherlands and Australia, SBR reduces the manual labor of compliance, eliminates duplicated efforts of overlapping reporting requirements, and allows agencies to apply analytics. A Deloitte study found that the Australian SBR program saves Australian companies more than AUD $1 billion annually – even before being made mandatory for most agencies. The Dutch government’s goal for its SBR program is to facilitate “business reporting in zero clicks.”
SBR was mostly unknown in the United States until October 2016, when the Center for Open Data Enterprise highlighted it in its recommendations for open data actions for the next presidential administration (see Recommendation 24).
SBR can reduce compliance costs while avoiding political battles over the substance of what companies are required to report to regulatory agencies. The 115th Congress and the new Trump Administration should take notice.