Too much of a good thing can be bad for you,and this is especially true when it es to panies,particularly banks,have a wealth of information at their fingertips,but until recently they lacked a basic method to harness this data and put it to better use.

The Securities and Exchange mission regnized this untapped potential and,in 2009,mandated that all publicly held panies disclose their financial information using eXtensible Business Reporting Language (XBRL)—a standardized method of data llection and reporting.XBRL uld facilitate more aurate parisons across panies to improve business performance,investment analysis,and decision-making.However,to this point,most panies see the XBRL mandate as a pliance headache rather than a value-added tool for analysis.An exception is the banking industry,which presents an excellent example of how XBRL can ease the big data headache through the creation of high quality,nsistent data.

The Federal Financial Institutions Examination uncil (FFIEC)started the Call Report Modernization project in 2005.This project requires banks to llect,validate,manage,and distribute structured data into a central data repository (CDR)that federal regulators and the public can aess.The FFIEC agencies were quick to adopt XBRL before many of the SEC industry mandates were enacted,and its members understood the value of inrporating the new standard data format at the introduction of the reporting process rather than waiting until the end and attempting to “fit”the data into XBRL.This decision not only streamlined the XBRL tagging process,it substantially increased the quality of the reported data and facilitated a real-time analysis of the data as it was processed.These anizations were also the first U.S.groups to build a CDR and introduce a large-scale solution based on the premise of structured XBRL data,better quality,and more timely reporting.It was,in part,the suess of the banking regulators that helped to push the SEC (and other regulators)toward the industry XBRL mandates we see today.

This wouldn’t have been possible if the FFIEC hadn’t had the foresight to cate banks,software vendors,and the related regulators on ways they uld improve efficiency,transparency,quality,and st saving through the use XBRL data.Because standardized data is universally parable,banks can more quickly express plex formulas and have that data immediately usable for analysis.

The FFIEC’s use of XBRL to assist with the management and ernance of the vast amounts of data being transferred between the operating entities,was directed toward their goals to avoid much of the manual entry and effort,to programmatically detect variations in data,and to identify substantial inefficiencies when nfronted with big data.Their suess can be demonstrated simply by reflecting on the state of data after the XBRL “Call Report”project,where it’s reported that 95%of bank data is nsidered clean before it is submitted by the banks.This suess was substantially attributed to the FDIC’s roles in the implementation of XBRL formulas,for use with the Call Reports,which allowed for the ‘hands free’ability for the identification of data anomalies.

Noteworthy is that during the early implementation meetings,banks expressed ncern as to how they’d learn XBRL.By including software vendors,who wrote the reporting software,at the start of the project,these anizations created an environment where the banks were oblivious to XBRL efforts;they would just realize the benefits.

Today,banks and their regulatory bodies are drowning in data,leading to issues with long processing times,error renciliation,re-work,speed to market,and data analysis,among others,but XBRL ntinues to prove its potential.By using a standardized reporting language and process,business leaders can base more decisions on meaningful data,which leads to improved risk management.It also saves time on data llation,allowing for quick action to be taken.Banks see faster recertifications,have fewer flawed data rerds,and have rced their processing times when creating and managing the information required for regulators.Regulators also take advantage of the more nsistent,aurate,and parable data by realizing a rction of their operating expenses and time spent on pre-XBRL rrective action.

The banking industry has been a sterling example of how XBRL can rce the time and money spent on internal operations,improve the decision-making and reporting processes and help manage overall risk.When it es to the big data headache,XBRL is the remedy you need.

中文新闻：http://www.xbrl-/2014/0113/98584.shtml