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Fintech 1,000 Landscape Report: XBRL Analytics

This is the first Landscape Report from the Fintech 1,000 database.

This Landscape Report demonstrates the difficulty of using a rigid taxonomy. The names given to a “space” (VC speak) or a “segment” (analyst speak) evolve over time and the most successful ventures emerge before anybody has given a name to what they do. Entrepreneurs don’t enter a space/segment; they solve problems and, if those are big problems that a lot of people have, somebody then gives a name to what they are doing and that then becomes a space/segment. Ideally you control the messaging enough that you invent the name that defines what you created. I describe this approach – define the market that you want to lead – in my book Mindshare to Marketshare.

The first phase of XBRL was all about the people, process and technology needed to produce XBRL content.

The next phase of XBRL is where it gets interesting. This is when we see the tools to consume XBRL content. This is what I call XBRL Analytics.

Where does XBRL Analytics fit within a Fintech taxonomy? Here are some choices:

  • Low Cost Active Alpha (within a broader Personal Financial Management space). This is where I tend to put XBRL Analytics, but I understand that it is a quirky name and probably won’t catch on.
  • Analytics or Big Data. This has become so broad and hype-driven as to be meaningless today. It begs the question “what do you want to analyze and why?”
  • Equities (within a broader Capital Markets space). This fits because most XBRL is used to analyze publicly listed stocks. However XBRL has applicability to Fixed Income and Private Equity as well.

That is why I use tagging as well as taxonomy to do custom searches on the Fintech 1,000 database. I never assume I will have a perfect taxonomy or a complete database. Fintech 1,000 is always a pragmatic work in progress tool.

XBRL Analytics could be used by any of the following:

  • Individual Investor.
  • Hedge Fund analyst.
  • Private Equity analyst who is looking for take private opportunities.
  • Investment Banker who is looking for M&A or IPO comparables.

Where it gets interesting and disruptive is when that humble Individual Investor creates something like a Syndicate or a Motif or a Wikifolio. Now the lines between Individual/Retail and Institutional/Professional start to get blurred. Entrepreneurs love blurry intersections because they signal opportunities that are overlooked by established players.

XBRL is disruptive because:

“XBRL democratizes fundamental stock analysis in the same way that PCs democratized computing and social media democratized HTML. When you can compare the cash holdings across 100 companies with a single entry in your spreadsheet, or see quarter to quarter revenue growth for every company in the index that you devised, do you need mega brokers and their me too reports churned out by freshly minted MBA graduates in an outsourcing center somewhere?”


That is why XBRL Analytics is important.

I am seeing 5 different types of companies moving into XBRL Analytics:

  1. Use XBRL Analytics “under the hood” to deliver actionable information to investors such as:

Capital Cube The interview with Larry King is a good intro.

Market Realist. Their tag line is “democratizing investment management”.

StocksForTheWeek. They focus on discovery of undervalued small cap stocks, which is the natural use case for XBRL, because it is a way for the Small Cap to escape the valuation trap known as “small cap hell” and for the small investor to find the bargains overlooked by the big guys.

  1. BI (Business Intelligence) vendors using XBRL. An interesting one to watch is Tableau because they are innovators with scale in BI (successful IPO in 2013, market cap over $6 billion). IBM is the behemoth in BI and they seem to be adding XBRL capability to a lot of their products. SAS are doing the same. When companies like Oracle and IBM embed XBRL into accounting systems, analysts can merge internal and external data.
  1. XBRL vendors that started in XBRL creation:

Rivet There are a lot of “devil in the details” of XBRL creation, so understanding these should make one better at the analytics part.

  1. Pure Play XBRL Analytics tools.



Thinknum. They have an interesting “model sharing” model (share your model in the free version, keep it private in the paid version; a bit like GitHub).


Silqe. They are still in Beta, so I am not sure if positioning is correct.

  1. New data technology. Data that is semantic and real time may need something other than RDBMS. I only found one company in this category, which is 28IO. You can see an example of their output at

The best expert in the XBRL Analytics space is Jim Truscott who writes a good blog here.

It is interesting to see a stack emerging. At a low level we have companies like 28io with new database technology to power real time analysis of large semantic data sets. At a user level we have companies like Market Realist and StocksForTheWeek that abstact away the XBRL layer to deliver actionable information. For financial analysts who want more control there are services such as Thinknum, Contexxia and FinDynamics.

I hope this is useful. Please tell me in comments if I have missed any innovators.

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