Ashu Bhatnagar is CEO of Good Morning Research, a Softpark mpany that specializes in building Semantic XBRL technology. The GoodMorningResearch.m machine automates XBRL tagging of Excel data in RDF format with one-click Save As XBRL functionality. Mr. Bhatnagar moderates the Semantic XBRL group on LinkedIn.

Is anyone interested in the transparency of financial data?

That’s obviously a silly question: institutional investors, market regulators, analysts, financial advisors, and even some sophisticated individual investors are very interested in greater transparency of financial data than what is currently available. This interest in transparency is driven not only by the need for keener insight but also by an interest in managing risks — particularly when such data is used to drive significant investment and trading decisions that affect trillions of dollars in wealth on global capital markets.

However, trust often ends up used as a proxy for transparency, replacing it either by choice (e.g., to protect mpany secrets) or because of practical nsiderations (like limited resources of time, money, or reporting skills). In addition, mpared to transparency, trust is simple, nvenient, and less work!

Trust can be broken, however, in cases of fraud like Bernie Madoff or inadequate financial models like those used to value and rate mplex instruments, such as credit default swaps and mortgage-backed securities before the real estate bubble burst. That’s when we are reminded of Ronald Reagan’s old maxim: trust but verify.

While Semantic Web and XBRL technology cannot provide a cure-all solution, they go a long way to integrating verifiable data into every step of financial reporting and anizing it for analysis and insight. Verifying financial data requires exposing transparency at every stage of the information supply chain as the data moves through it. The requirements are twofold:

(a) Make source data transparent in its raw format; and

(b) Make every step of any value-add process that normalizes and modifies this data transparent as well.

Examples of such value-add processes include normalizing and tagging raw data with metadata and taxonomy labels for mparability, applying relevant acunting adjustments, normalizing currencies and reporting periods, and adding other business rules and assumptions.

The first part, achieving transparency in publicly available raw financial data, is mparatively easy to attain. The send part, however — achieving transparency across value-add processes — may not be so. This is because value-add processes may be highly prized, and business rules and calculation models may be proprietary and closely guarded by data providers. In such cases, trust generally substitutes for transparency, but this requires an architecture that affords appropriate verifiability. The Semantic Web is designed to meet this challenge.

In my post last week, I discussed how Tim Berners-Lee ined the term Semantic Web in a roadmap for future Web design. A quick look at his Semantic Web architecture diagram, popularly known as “layer cake,” clearly demonstrates that the issue of trust — and, by extension, transparency — is addressed at a fairly high level in the Semantic Web’s stack. This architecture diagram has undergone several refinements; the most recent version (shown below) is on the W3C web

To crystallize for our purposes: while building trusted systems, it’s necessary to go back not only to the source, i.e., raw data, but also to metadata about the source. The single most important point to derive is that the Semantic Web architecture explicitly addresses the issue of trust (and therefore transparency) at a much higher level than XBRL alone.

In the Semantic XBRL worldview, transparency extends not only to data and its associated taxonomy, but also to its logic, business rules, portability, and security, as well. This affords greater opportunity for verifying data when stakes are high and trust matters.

Raw data is in big demand though for reasons beyond transparency. Let’s examine two users of financial data who demand a data structure that includes raw data: buy-side institutional investors, and market regulators who represent taxpayers.

Buy-Side Institutional Investors

At O’Reilly’s Money:Tech 2008 nference in New York, a session asked, What Do Hedge Fund Managers Really Want? Here, some hedge fund managers explained that they wanted “Raw Data Now” with fully transparent source data before it was modified by sell-side analysts’ proprietary secret sauces or data aggregators’ value-add processes.

Why? Some simply disagreed with the value-added adjustments and forecast assumptions. Others wanted to be able to apply their own adjustments to raw data before building their own valuation, forecast, and risk models. I found this to be the case in my own personal experience at a major sell-side firm, where buy-side research analyst clients wanted to -mingle data from multiple sources and needed to apply their own taxonomy tags and adjustments to the raw data.

Market Regulators

In testimony to the Domestic Policy Submmittee of the Oversight and ernment Reform mittee, Mark Bolgiano, President and CEO of XBRL US, observed:

Taxpayers want to know how their money is being used to fund the financial bailout. XBRL is a standard that promotes transparency and acuntability and can be used by regulators to perform oversight functions more effectively and efficiently.

On the subject of XBRL’s impact on the transparency of financial transactions, specifically Mortgage Backed Securities (MBS), Bolgiano noted that:

The lack of reporting standards has made it difficult to understand the simple fundamental value of the mortgages in these loan pools. Information llected about borrowers, loans, ongoing surveillance, settlement and clearance information is reported in differing data and reporting formats. The identity of individual loans is lost when the pool is securitized and value bemes based on a rating and essentially what the market will bear.

With an agreed-upon data standard and XBRL, issuers, investors, rating agencies and regulators uld forecast actual disunted cash flows of the individual loans, making it significantly easier to value each security – effectively “normalizing” the data so that the security can be valued using a regnized valuation method.

In the parallel universe of Semantic Web, Tim Berners-Lee is championing a grassroots movement to call for “Raw Data Now!” where raw data is stored in the RDF-based Linked Data Format to greatly increase data transparency. Speaking at this year’s TED nference, he said:

Often you find that the people are used to database hugging. You don’t let it go before you have made a beautiful web…. Before making a beautiful web, first give us the unadulterated data. We want the data. We want the unadulterated data.

Berners-Lee went so far as to enurage the audience to join him in a chant for “Raw Data Now” and noted:

Practice that. It’s important because you have no idea the number of excuses people me up with to hang on to their data even though you paid for it as a tax payer. And its not just America but it’s all over the world, and that’s not only the ernments but the enterprises as well.

Over the next several weeks I intend to explore more in the areas of Semantic wiki-tagging and Semantic XBRL data quality.