Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
    financial analytics
    Financial Analytics Shows The Hidden Cost Of Not Switching Systems
    4 Min Read
    warehouse accidents
    Data Analytics and the Future of Warehouse Safety
    10 Min Read
    stock investing and data analytics
    How Data Analytics Supports Smarter Stock Trading Strategies
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: XBRL: How to Save a Good Idea from a Bad Implementation
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Quality > XBRL: How to Save a Good Idea from a Bad Implementation
Data QualityPolicy and Governance

XBRL: How to Save a Good Idea from a Bad Implementation

tkorte
tkorte
4 Min Read
SHARE

If the problems with the U.S. Health Insurance Marketplace website have taught us anything, it is that implementation problems can overshadow the good ideas underlying government initiatives. The same problem is occurring right now with the Securities and Exchange Commission’s (SEC) adoption of XBRL, and critics must take care not to reject the idea of open financial reporting standards in light of a flawed implementation.

Publicly-traded companies must disclose financial statements to the SEC on a quarterly basis.

If the problems with the U.S. Health Insurance Marketplace website have taught us anything, it is that implementation problems can overshadow the good ideas underlying government initiatives. The same problem is occurring right now with the Securities and Exchange Commission’s (SEC) adoption of XBRL, and critics must take care not to reject the idea of open financial reporting standards in light of a flawed implementation.

Publicly-traded companies must disclose financial statements to the SEC on a quarterly basis.

More Read

Governance of the People? Of the Data? For the …
Top Ten Root Causes of Data Quality Problems: Part Three
Are You Reporting What You Can?…Or What You Should?
What Scales Best?
How Is Mobile Technology Impacting the Food and Beverage Supply Chain?

These disclosures include a wide range of variables, such as income, expenses, investments and cash flow. The SEC uses these reports to monitor activities and enforce U.S. securities laws against fraud, insider trading and other financial crimes.

In an effort to modernize these disclosures, the SEC mandated in 2009 that companies must submit their electronic filings in both plain-text as well as XBRL format. XBRL, which stands for eXtensible Business Reporting Language, would allow the SEC (along with investors, analysts and other government agencies) to conduct data-driven analysis of business filings, cutting transcription costs and enabling better fraud detection and smarter investments.

However, the SEC’s XBRL adoption has been marred by the fact that the XBRL filings are not audited like the plain-text filings. As a result, investors and analysts consider the XBRL data to be more error-prone and less reliable than plain-text filings and so they still rely on the ordinary filings. Moreover, some users, such as investors and analysts, are hesitant to switch to XBRL because they lack easy-to-use analysis tools for the data, and they do not want to incur the costs of developing ad-hoc technical solutions

The root of the problem is that the SEC does not consider the XBRL filing the authoritative filing by a company. Since the SEC was not penalizing companies for making errors in their XBRL filings, companies had no incentive to devote attention to the critically important machine-readable data releases.

These complaints can all be addressed through prudent policy revisions on the SEC’s part. First, the SEC should eliminate plain-text filings by 2015. The longer-term purpose of requiring machine-readable filings is to enable computer-aided analysis and searching, not simply a supplement to plain-text filings. This will only occur if XBRL filings are mandatory. Second, the SEC should begin immediately subjecting XBRL to the same level of auditing as plain-text files and require companies to correct XBRL errors as they are discovered. Third, the SEC should expand the machine-readable reporting requirement to include more types of filings, thereby expanding the range of data available and encouraging users to develop easy-to-use analytical tools that will in turn foster greater data usage.

Government agencies of all stripes should learn a lesson from the SEC’s XBRL difficulties: an exclusive focus on releasing data overlooks other important data policy issues such quality and adoption of standards. Otherwise, it is just “garbage in, garbage out” and the good ideas behind better use of data in government may end up going to waste.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in video game development
Machine Learning Is Changing iGaming Software Development
Exclusive Machine Learning News
media monitoring
Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
Analytics Exclusive Infographic
data=driven approach
Turning Dead Zones Into Data-Driven Opportunities In Retail Spaces
Big Data Exclusive Infographic
smarter manufacturing
Connecting the Factory Floor: Efficient Integration for Smarter Manufacturing
Infographic News

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Perils of Big Data: US Government Collecting Customer Data from Verizon

1 Min Read

Data Preparation: Know Your Records!

4 Min Read

Run IT as a responsive business, beat the cloud vendors at their own game

6 Min Read

Big Data Blasphemy: Why Sample?

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?