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
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 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

customer data protection
Here Are The Most Important Ways To Ensure Customer Data Protection
Critical Cloud Security Tech You Need to Understand in 2018
Freakonomics and Your Data
Department of State’s Consular Systems and Technology: A Track Record of Innovation
Dirty Data: Embarrassing, Expensive, Avoidable

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

payment methods
How Data Analytics Is Transforming eCommerce Payments
Business Intelligence
cybersecurity essentials
Cybersecurity Essentials For Customer-Facing Platforms
Exclusive Infographic IT Security
ai for making lyric videos
How AI Is Revolutionizing Lyric Video Creation
Artificial Intelligence Exclusive
intersection of data and patient care
How Healthcare Careers Are Expanding at the Intersection of Data and Patient Care
Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

How to Share Bad Project News

5 Min Read
data quality and quantity in artificial intelligence
Artificial IntelligenceBig DataData QualityExclusiveMachine Learning

What To Know About The Impact of Data Quality and Quantity In AI

8 Min Read

Big Data Without Integration Is Broken

7 Min Read
Image
Data Quality

Why Data Should Be a Business Asset: The 1-10-100 Rule

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 in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

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

Sign in to your account

Username or Email Address
Password

Lost your password?