Reducing the Cost of Regulations by Using Data StandardsPosted 2012-08-21 Print
XBRL data standards have a wide variety of uses across various industries and can reduce the cost of surveillance, increase efficiency in data reporting and help organizations use this information to clearly analyze the impact of regulations.
By Greg Carter
I am addicted to Sunday morning news programs, and my wife and I spend most Sundays reading various papers and listening to the topics du jour. Regulation, and more important, the cost of regulation, is a frequent topic of discussion. On a recent Sunday, there was an especially interesting discussion centered on advances in process management and technology that could reduce the cost of regulation for businesses and governments. The problem being discussed involved the level of administrative bureaucracy instituted by both regulators and businesses. The cost for businesses and agencies to pull together compliance data and normalize it is significant. However, by using data standards rather than technology standards, these organizations could dramatically reduce costs while increasing efficiencies.
In fact, since the Internal Revenue Service (IRS) implemented a data standard over the last 10 years, and as a result it has dramatically improved efficiency, reduced costs and provided better customer service. These benefits are in large part due to moving from conducting a close inspection of every return to using a more sophisticated surveillance and audit model, in which returns are automatically inspected and accepted. Returns are submitted in a standard, machine-readable format that allows broad surveillance activities that can eventually lead to an actual audit. The key to the IRS’ ability to both improve surveillance and streamline processing is the agency’s standard data set—its taxonomy—and a standard exchange format for information submitted as returns. This standardization allows automation and a dramatic reduction in manual intervention; the savings are realized both by the agency and the agency’s customers.
This type of data standard—versus a technology standard—has wide applicability. For example, today the U.S. Environmental Protection Agency (EPA) regulates a variety of pollutants under the Clean Air and Clean Water acts. As part of the regulation that keeps our water and air clean, the EPA issues permits for various pollutants and classes of pollutants. Organizations report on the output of these dangerous pollutants on a regular basis. Large companies can report on some pollutants using a semiautomated data feed, but the majority of companies use electronic forms that are filled in by people. The reported information is digested into a proprietary database, and the data is made available as raw text extracts for analysis and surveillance. Anyone who uses this data needs to transform it into a usable format and manually map it to a data dictionary before the data becomes information, which is a time-consuming, laborious task.
By using a data standard such as eXtensible Business Reporting Language (XBRL) instead of its current system, the EPA could work with states, other countries and industries to develop a pollutant taxonomy. This taxonomy could include not only what to report by industry and pollutant, but also information and rules regarding the frequency of measurement, units and how the data should be displayed for review.
One key point here is that the taxonomy defines not just a machine-readable format but the actual data-specific rules that help ensure that the data will be valid, meaningful and comparable. If this data is compiled directly from existing systems, which is likely, accuracy is even further improved. Having the rules encoded in the taxonomy also allows businesses to proactively monitor their compliance and avoid punitive action by regulators. Using highly consistent information, the EPA could conduct surveillance using automated processes that are backed up by statistically driven auditing techniques. This will lead to a new era of value-based decision making in regulatory action, in addition to the benefit of lower compliance costs.
Industry would then be able to produce this information readily from disclosure tools that leverage common applications such as Word and Excel and could publish a disclosure in multiple formats, including XBRL. The EPA could easily digest the XBRL and allow submitters to syntactically and semantically validate submissions in advance. (XBRL formulae included in XBRL taxonomies allow an automated validation by the producer of information before submission.) Once available as XBRL, the EPA may easily use the data for surveillance in a format that fosters simple comparative analytics.
The information in XBRL also offers low-cost portability, which allows the information to be more easily used by independent surveillance organizations and by the very industries being regulated. An oil reprocessor, for example, will be able to look at its emissions of sulfur dioxide, mercury and nitrogen oxide relative to its peers. If the firm is compliant but lagging behind other firms, this could indicate that other firms are reaping far better returns on reprocessing efficiencies and that an investment in research and development is needed.
The XBRL data standards have a wide variety of uses across various industries and can reduce the cost of surveillance, increase efficiency in data reporting and help organizations use this information to clearly analyze the impact of regulations.
Greg Carter serves as chief technology officer at EDGAR Online, and he has more than 20 years of experience in software design, development and implementation.
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