Can the Bloomberg Terminal be “Toppled”?

In the eye of some entrepreneurs and venture capitalists, the Bloomberg terminal is a bit of an anomaly, perhaps even an anachronism.  In the era of free information on the Internet and open source Big Data tools, here’s a business that makes billions every year charging its users to access data that it generally obtains from third parties, as well as the tools to analyze it.  You’ll hear the occasional jab at its interface as reminiscent of the 1980s.  And at a time of accelerating “unbundling” across many industries, including financial services, the Bloomberg terminal is the ultimate “bundling” play: one product, one price, which means that that the average user uses only a small percentage of the terminal’s 30,000+ functions.  Yet, 320,000 people around the world pay about $20,000 a year to use it.

If you think that this sounds like a perfect opportunity for disruption or “unbundling” at the hand of nimble, aggressive startups, you’re not alone.  I spent four years at Bloomberg Ventures, and this was a topic that I heard debated countless times before, during and after my tenure there. Most recent example: a well written article in Institutional Investor a few weeks ago declared the start of “The Race to Topple Bloomberg“, with a separate article highlighting my friends at Estimize and Kensho as startups that “Take Aim at Bloomberg“.


Yet, over the years, the terminal has seen its fair share of would be disruptors come and go. Every now and then, a new wave of financial data startups seems to be appearing, attempting to build businesses that, overtly or not, compete with some parts of the Bloomberg terminal.  Soon enough, however, those companies seem to disappear, through failure, pivot or acquisition.


What gives? And where are the opportunities for financial data startups?


Frontal assault: good luck


To start, Bloomberg is not exactly your run-of-the-mill, lazy incumbent. Perhaps I drank too much of the Kool-Aid while I was there, but I left the company very impressed.  Bloomberg, which was itself a startup not that long ago, comes armed with a powerful brand, deep pockets, a fiercely competitive culture, a product that results from billions of dollars of R&D investment over the years, and a technology platform that basically never goes down or even slows down, supported by generally excellent customer service.


But great incumbents have been disrupted before.  So there is perhaps another set of less immediately apparent reasons why the terminal has so far been very resilient to disruption by startups:


1.  It is protected by strong network effects.  One surprisingly misunderstood reason to the long term success of the Bloomberg terminal is that, beyond the data and analytics, it is fundamentally a network.  In fact, it was probably the first ever social network, long before the term was coined. Although some believe that its cachet as a status symbol is starting to erode, “the Bloomberg” (as it is often called) has been for decades the way you communicate with other finance professionals (for legitimate or not so legitimate reasons).  In its relevant target market, everyone is on it and uses it all day to communicate with colleagues, clients and partners. Web based services (Facebook, Dropbox, Gmail), often banned in financial services companies, haven’t made much of a dent in that, at least for desktop communication.


2.  It is an aggregation of niche products.  In the world of financial data, there is enough specificity to each asset class (and subsegment thereof) that you need to build a substantially different product for each, which requires deep expertise, as well as a huge amount of effort and money, to address a comparatively small user base (sometimes just a few tens of thousands of people around the world).  Bloomberg started with fixed income data and over many years, used its considerable cash flow to gradually conquer other classes (still a work in progress, to this day).  So disrupting the Bloomberg is not as “easy” as coming up with a great one-size-fits-all product.  It would take immense amounts of venture capital money to build a direct competitor across all those niches.


3.  It’s not “just” a technology play.  Providing financial data at scale is not a pure technology play, so it is not a matter of coming up with radically better technology to aggregate and display data, either.  At this stage at least, there is a whole web of human processes, relationships and contracts with underlying data providers that has been put on place over many years.


4.  It’s a mission critical product. This is a key point.  In the financial world, data is used to make gigantic bets, so total accuracy and reliability is an absolute must – which makes people cautious when experimenting with new products, particularly built by a startup.


The Bloomberg terminal business may face macro headwinds, as described in the Institutional Investor piece (dwindling of the number of relevant jobs on Wall Street and a global shift from desktop data to data feeds).  However, as a result of the above, I don’t see the Bloomberg terminal being entirely “toppled” by any one given startup anytime soon, and I think even competing directly with any of its key functionalities (unbundling) is a tall order for startups, even with access to large amount of VC money.  Not that it can’t be done – I just think there are lower hanging fruits out there and some real benefit to position away from the Bloomberg.


Where are the opportunities in financial data?


While I don’t see much opportunity for startups to build a Bloomberg terminal replacement (or a a replacement to Thomson Reuters or Factset, to be clear), I think there are fertile grounds “around” and “below” the terminal – meaning in areas where the company is unlikely to want to go.


Specifically, I believe there are going to be ongoing opportunities to apply some of the quintessential internet concepts and processes (networks, crowdsourcing, etc) as well as new-ish technology (Big Data)  to the world of financial data, including:


1.  Finance networks/communities.  Like the Bloomberg terminal did, some of the more interesting “adjacent” plays opportunities will marry data, tools and community.  Historically, capital markets haven’t seen much of a sharing culture (lots of nuances here, I know), which is in part due to the nature of finance investing itself – however, it’s going to be interesting to see how, at least in certain areas, that culture will evolve as digital natives rise in the ranks of their organizations.  Beyond early entrants Stocktwits and Covestor (which generally target a more casual audience), examples of such professional communities include SumZero, initially for Buy Side analysts but now wider, and more recently Quantopian, an algorithmic trading community where scientifically educated people and other quant types share strategies and algorithms.  Early stage startup ThinkNum thinks financial models should be shared and wants to the “Github” for financial models.  What else can be shared?


2.  App stores. The app store model is an interesting way of leveraging the expertise of a “crowd” of specialized third party developers (Bloomberg launched its own a couple of years ago). OpenFin, for example, provides infrastructure to enable the deployment of in-house app stores, addressing the necessary compliance, security and inter-operability requirements (having data flow from one tool to the other). A combination of an in-house app store infrastructure with some best of breed applications (say, a ChartIQ, which provides HTML5 financial charts, including technical analysis tools) is an interesting approach to target the portion of the market “below” the terminal, as  companies that cannot afford a full on terminal infrastructure could pick and choose the apps they need and have them work in their environment.


3.  Crowdsourced data.  From Estimize (which crowdsources analyst estimates) to Premise (which crowdsources macroeconomic data through an army of people around the world equipped with mobile phones), a whole new way of capturing financial data has emerged. Quandl, a financial data search engine, has aggregated over 8 million financial and economic datasets through both web crawling and crowdsourced, community contributions.  Once such a data platform has been built, could third party developers add analytic and visualization tools on top, essentially resulting in a crowdsourced “terminal” of sorts that would be reliable enough, at least for non mission critical, non real time use cases?

4.  Big Data “insights”: Extracting signal from data is obviously the end game here, and interesting startups are heavily focused on those opportunities, from Dataminr (social data analytics for Wall Street) to Kensho (which is working on “bringing the intelligent assistant revolution to finance”). In terms of market positioning, it is unclear to which extent those technologies compete with the Bloomberg terminal (which, for example, has been very active on the social data front), or potentially complete it.


The big question facing entrepreneurs and VCs alike is how to scale those businesses and turn them into billion dollar companies in a context where solidly entrenched platforms have a stronghold on arguably the juiciest part of the market. But overall I believe that we’re only going to see more startups going after financial data opportunities, with potential for some serious wins – I’m excited to see how it all evolves.

71 thoughts on “Can the Bloomberg Terminal be “Toppled”?”

  1. Plenty of pragmatic advice for aspiring Bloomberg conquerors in this article, the most important being that a “full frontal” attack would be perfectly futile. If Bloomberg is going to be toppled, as Matt correctly implies, it will be a slow decay as various companies chip away at their dominance. Full disclosure: I’m doing a bit of chipping myself. Well written Matt.

  2. Great article, Matt. Thanks very much for the mention of [Quandl]( 🙂

    You write:
    > Once such a data platform has been built, could third party developers add
    > analytic and visualization tools on top, essentially resulting in a
    > crowdsourced “terminal” of sorts that would be reliable enough, at least
    > for non mission critical, non real time use cases?

    In fact, this is happening already. One of the really neat things about this next generation of startups is how they all interact nicely with each other in an emerging “data ecosystem”.

    Here’s an example. A data producer like [PsychSignal]( publishes its social media sentiment index on [Quandl]( A user exports that data from Quandl to a high-end visualization service like [Plotly]( or [DataHero](; extracts subtle insights using an analytics tool like [BigML](; backtests an investment strategy using [Quantopian](, and then actually trades on that data using Quantopian again.

    All of these transitions are basically just a few clicks of the mouse: the integrations already exist. That’s a marked difference from the “walled gardens” that previously dominated this industry.

    1. Really interesting… you guys seem to be making rapid progress. Is the next step (or a part of the long term plan) some level of cross-app integration? (which is one of the key features of a terminal, where all functions speak to one another). The related issue is standardization of data, particularly at the security level (symbology, etc.).

      1. I can see cross-app integration happening, and sooner rather than later. But to be honest, we’re not too keen on doing it ourselves. Data acquisition is what we’re good at and what we’ll stick to. There’s just so much data out there, and so much of it is messy and hard to access; that’s enough of a challenge to keep Quandl occupied for a long long time. If we just do our part (acquiring, harmonizing, storing and delivering data quickly and efficiently), I have no doubt that others will build all sorts of amazing apps and integrations on top of our API, and we can all share the benefits.

        1. Actually I hadn’t come across Big Terminal – let me take a look. One of the great benefits of writing a post like this is to learn from the conversation. Thank you!

      2. I founded/run bigterminal. Nothing raised yet and just a team of basically me right now up in weather stricken Toronto. Got ~30 million series being covered in the system and lots of interaction. Would love to bring this project of mine much further though, got tons of road map ideas hope to execute on. Nice to see organic mentions. Thanks!

        fwiw, email contact is at the footer of the site in case of feedback or q’s

    2. @MattTurck, this is an excellent article.
      [OpenF2] ( is another technology in the mix, that tries to make the integration parts of all of this happen as well. Cross-app communication, multi-party apps within a terminal/desktop/container, etc. Very much in the same vein as @qaternary’s ‘walled garden’ comment.

  3. Good read – appreciate the inside look at Bloomberg. My question is this: Won’t anyone with enough hustle to get it done in this space be much more likely decide to try to create alpha themselves and run a small fund with more limited dilution and more upside than they would likely see in a venture-backed scenario? I am thinking the nature of the vertical will significantly dilute the talent pool with the needed domain knowledge to go after opportunities in the space. What do you think?

    1. I agree. It’s basically the first question that comes to mind whenever someone comes in to raise venture money with a capital markets venture that tries to extract unique insights from data or provide some other kind of informational edge – if it’s so great, why don’t you trade on it yourself? In most cases, it’s because the technology is too early. In some cases, however, people genuinely want to build a technology company rather than a hedge fund. And, to your talent question, you see more and more former hedge fund people who’d rather do something more fun. Leigh at Estimize is a former hedge fund guy; also see Matthew Granade, the former co-head of Research at Bridgewater who is now building a data science platform (Domino Data Lab).

      1. >”If it’s so great, why don’t you trade on it yourself?”

        I hear this often and the answer is because specialization matters now and alpha doesn’t equal a successful quant hedge fund.

        Startups today aren’t creating simple sets of data or tools. We’re not peddling models that we say work so you should buy them too. Those are the get rich schemes from Wall St hucksters of the past. We’re building specialized tools & data sets that take focus and that frankly a hedge fund can’t or doesn’t want to do themselves.

        Levi Strauss was a hell of a pant maker and I’m sure his invention improved the lives of gold miners everywhere but I bet he couldn’t mine gold for shit.

        I would tell you the same thing. I was a professional trader for 10 years and so naturally I see the value (crowd psychology rules the markets) in social media derived sentiment data being applied as ONE ingredient amongst many inputs into an algorithm. I built a startup to provide such a service but I’m not about to start a quant fund. Combining those data inputs is a whole new skill set and if there is one thing startups need to do early it’s to focus and do one small thing very very well.

  4. Good read – appreciate the inside look at Bloomberg. My question is this: Won’t anyone with enough hustle to get it done in this space be much more likely decide to try to create alpha themselves and run a small fund with more limited dilution and more upside than they would likely see in a venture-backed scenario? I am thinking the nature of the vertical will significantly dilute the talent pool with the needed domain knowledge to go after opportunities in the space. What do you think?

  5. That’s a strange way to phrase it, huh? The answer is almost certainly “yes”. The only questions left are “when, how, and by whom”.

    How many companies have maintained utter dominance in their field for 10 years? A decent number. 50 years? A few. 100 years? None that I can think of offhand.

    1. You certainly have history on your side. But my point here is, why is there no startup that seems anywhere near being able to do it at this stage? The topic fascinates me.

  6. This is a super interesting space – a big part of why it’s so hard to topple the terminal is because of Bloomberg’s relationships with different institutions out there. Bloomberg has a direct line of contact with the stock exchanges, and any feed issues on the terminal can be fixed in one simple phone call. No startup out there will have that kind of credibility

      1. That direct line and the numerous third party involvements that have to be integrated into one platform are also equally filled with time, bureaucracy, formality, patience, policy, etc….generally areas in which startup hacker mentality isn’t typically appreciative of when fighting a new battle. Definitely no shortage of areas ‘adjacent’ to the Terminal that startups might have an easier time diving into first.

        Some of this decoupling of services may ultimately need to be driven by the companies themselves. The individuals and the teams involved will always have the best window into the services each of their groups needs and what is and isn’t vital to their success. And the accountability and expectation for such internal driven teams may be higher and more focused if done right and rewarded properly.

  7. Also, News.

    This is less known, but Bloomberg News is the largest employers of reporters in the world. And while the news is available for free, it flashes instantly on your BB terminal, while it appears on the website with a delay.

    If Bloomberg breaks a single news item a year, which causes a stock you own to drop by over $20000 year in the interval that it takes for the news to appear on your BB and other sources of (passive, usually, unlike BB) the terminal pays for itself.

    BB is a tough but to crack. And its much derided interface is wicked fast. Competitors will have a really hard time both replacing with something faster, and getting users to learn another set of command line functions (because that is what the primary BB interface is, a command line).

  8. Bigger question is, what does the profitability of the business look like after you topple Bloomberg? If one could “de-bundle” the business, maybe the ~300k customers end up paying $5k each instead of $20k. Revenue drops to $1.5 bill from $6 bill. Assuming BB is worth ~$40 billion, multiple of say 15 times post tax (fair?) they are producing ballpark $3 billion per year. Maybe $4 billion pretax. So costs are about $2 billion per year? Maybe they can be cut in half. Then you are making $500 million per year pre tax v $4 billion. One would have to include the possibility that if you completely topple BB you likely have eaten some of Reuters lunch too so maybe revenue is higher without much increase in cost base.

    Many of the above numbers are out of thin air and could be debated, but they do highlight the fact that “toppling” bloomberg isn’t likely to result in creating a $40 billion value business. It is likely to create something worth a small fraction of that.

    1. Great analysis. The caveat is that creating a business that would be worth a small fraction of the value of Bloomberg (say, $1 billion) would be a huge home run for the founders and their VCs – one of reasons why startups are dangerous to established leaders is that they have nothing to lose and have different success metrics.

  9. Seems that you’ve hit all the points on why Defeating Bloomberg is likely a fool’s game – I do believe the Bloomberg social network angle is slowly eroding with open standards, particularly the efforts by Markit/TR and others, and the evolving buy side which is growing less and less dependent on Bloomberg (and even humans in general) for communications, let alone data/analytics.

    While I completely agree the data side can be chipped away at, the social angle is something Bloomberg will need to focus on to keep its dominance IMO.

    1. Thanks Ben. Yes, it’s interesting that Bloomberg had such an early start on the social angle, way ahead of its time, and then let the world catch up – in large part, I think it’s because the average Bloomberg user didn’t express much interest for “more” social functionality built into the terminal, and Bloomberg pays considerable attention to what its users say – rightly so obviously. But in the meantime, the world has changed around those users and Wall Street in general, and social functionality that seemed frivolous not so long ago has now become solidly entrenched in everyone’s professional life.

  10. Interesting conversation guys. I think part of the answer is that it’s been shown that trying to go head to head with Bloomberg as a startup is not the best or only path to financial data success. There have been a few companies emerge and take large chunks of the industry in the last 10-15 years. CapIQ has grown from nothing to $1.2bln, MarkIt has also grown to over $1bln in a somewhat overlapping space. CapIQ did it by dominating the ‘light terminal’ market and MarkIt did it by filling in data niches and aggressively acquiring companies.

    In a slower moving b2b space, startups don’t move in overnight, they grow over 10-15 years at which point we view them as ‘competitors’ and not start-ups.

    1. Yes, definitely takes time. Markit is an interesting example – shows the magnitude of the effort required to pull this off, including in their case securing the backing of many key financial institutions, and indeed pursuing a very capital-intensive roll up strategy. But is there a next Markit on the horizon? I’d love to know. In the same vein (although in a very tangential way to the Bloomberg terminal), the latest startup founded by Neal Goldman (one of the co-founders of CapIQ), RelSci, is a very interesting venture – same general approach of throwing a massive amount of effort and money to a big problem (I think they have close to 1,000 employees and were founded two years ago or something like that.).

      1. It’s essential to remember that nearly all the successful companies (Markit, clearly one) that have grown in and around this space over the last few decades have been built with significant upfront support from the big banks and institutions, including Bloomberg (Merrill Lynch) and firms like First Call (4-5 institutions).

        Given the state of venture capital today, I find it hard to believe that a seed/A round entirely built around strategics would be looked at favorably by traditional VCs, but unfortunately that may be the only path to real success in this space…

      2. I think you both have tapped into a huge part of the challenge — it takes more time to pull off a shift in workflow tools, particularly in the investment space, than most investors and entrepreneurs realize. I have often advised people that in fintech it is “evolution not revolution” — there are simply no Snapchats or Instagrams in this industry. The client base is among the most conservative there is with the adoption of new tools, especially post 2008. You need to have a good solid solution to a known problem, a long runway, laser focus, and tight execution of a plan to turn the idea into a real profitable business. While as we have seen it can make for sexy PR and headlines, I think the biggest operational mistake startups make in this industry is that they state that they are out to mimic or unseat one of the big guys. The landscape is littered with upstarts who had great ideas on a slide deck, raised a ton of money, got some PR buzz, and then underestimated how much time, effort, additional money and luck it takes. While I do believe this industry is ripe for some disruption and change (and I am excited to see it), I think we are going to see it in small bites and not in big gulps.

  11. One of the things people often forget is that there is a data department in Bloomberg that is almost equal in size to the r&d department. These are thousands of people that clean up and digitize everything from bond issues to regulatory reports, data that feeds the terminal. It’s this wealth of historical information that’s the hardest to beat in my opinion.
    In addition the company has a pretty strong entrepreneurial culture internally. In the last 5 years I have been part of 2 ‘strategic plans’, that are a given a carefully monitored set of resources and time to start generating profit.

    1. The data department is huge, and hugely important, no question. Fascinating that, even with access to all the latest technology tools, so much of the effort needs to be done manually, in order to guarantee 100% accuracy. And yes on entrepreneurial culture as well… I certainly wrote a few of those plans myself when I was there.

      1. @someoneWhoWorksThere: You’ve nailed it. Data is the key advantage that Bloomberg has. Without accurate, comprehensive, timely data, none of the other value-adds (analytics, news, social features, integrations) would be viable. Or at least, they wouldn’t add up to the all-encompassing super-product that Bloomberg is.

        The massive data department is a clear edge for Bloomberg right now, but also their greatest vulnerability going forward, because it’s such a gigantic cost centre. And these costs are fundamentally irreducible, because the process of data curation is so complex and human-dependent. As Matt says, “even with access to all the latest technology tools, so much of the effort needs to be done manually, in order to guarantee 100% accuracy.”

        Anyone who can replicate Bloomberg’s data acquisition pipeline at a significantly lower cost (via better technology, crowd-sourcing, a marketplace for data, or some other as-yet-unknown process/model) will be sitting on a gold mine.

      2. Great post. As somebody who has seen first-hand what is required to collect high-quality financial data on a Global universe, I’ve quickly learned that the mental model most people have is skewed heavily towards the data available and sourced from first world economies (the US in particular). Data – even basic company financial data – is not available “electronically” (structured data feeds) in many markets: you’re looking at getting your hands on PDFs and trying to figure out ways to automate/assist human collectors, including dealing with a ton of technical financial language presented in something other than English. Additionally, for some but not all of the financial data sets, the data standardization problem gets more difficult the more markets you must consider. The requirement to offer up a truly global product is what makes it the giant cost center it is for the established players like BB, TRI, FDS.

        Focusing on the data problem, the opportunity that some of the companies identified in the article are going after is either in cheaply aggregating what is electronically available or – better – in figuring out ways to make new kinds of data available. Given the size of the gap between these smaller data offerings and a full solution, the “new data” companies (e.g. Premise) probably make more sense as partners/acquisitions for the big guys than as companies eroding the hold BB and others have in the space. It’s obviously still true that anyone who can “replicate Bloomberg’s data acquisition pipeline at a significantly lower cost” would have a huge competitive advantage, but that is going to be a lot more difficult than it might initially seem.

    1. Yes, a roll up strategy of some sort, including getting started by acquiring something like this, is probably one of the best ways of building something in the space. Goes to my point about how incredibly capital intensive it all is.

  12. This is a really interesting discussion, something I can relate to.

    We ( are trying to do something similar to SNL, which is a sort of Bloomberg for banks. SNL has the same network effect, the same brand recognition and the same business model as Bloomberg. Their secret sauce is that they offer news as well. Bankers subscribe to SNL to get information on banks, but also to read news. They have their own newsroom, and they are the first place anyone calls with banking news.

    Instead of a frontal assault we’re taking a different track. We’ve focused on doing one thing extremely well, providing incredible search and comparison capabilities. We also have a really easy to use UI, which doesn’t hurt either. There’s a pain point that we’re trying to address, banks need to compare themselves to similar banks for regulatory compliance. Getting the data and comparing is hard, as is finding comparable banks. We dramatically simplify that process and make it easy. From here we have a million other plans, but it’s perfecting one thing at a time.

    It’s also worth noting that both Bloomberg and SNL grew up at the same time. They came of age in a finance environment where gathering data was hard if not impossible, and they solved that. Now that data is available new tools are needed to identify insights and move finance to the next level. I find it ironic that both Bloomberg and SNL have clung tightly to their 90s-esque interfaces.

    1. Interesting, thank you. As to the interface, as per some of the other comments, there are real reasons why Bloomberg has kept it (power users love it, it’s very fast, etc.), but I hear your point.

  13. By genius or accident Bloomberg have created a great defensive market position as highlighted in this article by the difficulties to disrupt them. Their position is really a dream scenario for any VC that would have backed them early.

    And now when I hear a VC/Investor ask me how will we maintain a competitive advantage as we grow our start-up I will have Bloomberg in the back of my mind as an example of the ‘holy grail’.

    1. @AlanMeaney it’s worth remembering that Bloomberg disrupted a business largely dominated by Reuters at the time, and while in hindsight it seemed like an obvious improvement on their platform, at the time it was not so easy to foresee.
      The Reuters strategy at the time (and surprisingly continued to this day) was to have separate products for each disparate customer base, which caused the problems Matt’s referred to here – each niche market on its own was not necessarily worth the effort, but as a whole package, it made economic sense.
      In addition, Reuters strategy was heavily focused on real-time data, but not on archiving and leveraging historical data for analysis. This was in direct contrast to the Bloomberg model, even though it was far more costly to execute. Reuters just didn’t see where its customers needs were evolving.
      Finally, Bloomberg courted the buy-side early which involved giving them steep discounts in the early going, in contrast to Reuters who had been the champion of the sell-side. Given the emergence of communications within the terminal, the buy-side dominance sealed Bloomberg’s fate as the platform of choice.
      Its the combination of Bloomberg’s strategy and technical capacity that enabled it to beat a large incumbent, and I agree with Matt that smart targeted startups can do the same to (at least parts of) Bloomberg.

  14. I think some of it was really “genius”. Quite amazing what those guys built in the early days – had to build the hardware, the software, the data center, digitize everything, built an entire news organization… Bloomberg was basically the first cloud/SaaS company, the first social network, the first Big Data company… quite amazing when you think about it.

    As to the VC discussion, it’s actually interesting to think about Bloomberg as a VC investment – would VCs have funded it at the time? Would it have turned out to be a good venture investment (in terms of growth, liquidity, etc.). Obviously a purely academic exercise (as Mike put in his $10M of severance pay in the business and then raised strategic money from Merrill Lynch). Perhaps a topic for another day.

    1. Not to mention the first secure instant messenger, the first network to offer live status bars for users, the first modern news reader, heck, even the first app to deliver, to a mass audience, the generic concept of typing in specific resource identifiers and having specific pages appear on your screen. All of this, years before the world wide web was invented.

      Incredibly impressive. “Genius” is not too strong a word.

  15. Hi, Matt — You touch it a bit about what is below ad around financial data. From my perspective, the opportuniy seems not to be in financial informatio but more so the data that people and machines are creating for the purposes of development. Financial information is the output of the data that the technologies create to give the markets real-time analysis. Consequently It seems like too much attention is placed on what results and not what is created. Bloomberg prospers by providng financial information through its terminals. That market is bound pretty tight. But look another level deeper and there is a vast ocean yet to be explored.

  16. Excellent post. Accuracy is certainly an important component but even when imperfect, network effects come to the rescue.
    10 years ago I worked as an analyst at a top Investment Bank. Bloomberg provided incorrect number of shares for an obscure emerging markets Telco, as the company had just repurchased 1/3 of its shares. Our pitch deck was incorrect, and as a summer, I was mortified. I told my Associate and he said “you should always triangulate all sources but in this case all other sources were also off, probably because they feed from Bloomberg”. I later got an offer…

  17. We just released an interesting Free web app with Hedge Fund Tested Analytics. Covers Stocks, Bonds, FRN’s, MM, Options (multiple models), Callable Bonds, Portfolio Analysis, News from hundreds of RSS Feeds, User Forecasting, Patent-Pending. Runs on any device with a browser with HTML5, cloud computing, SQL Data Base. Much more to come.

  18. Matt et al,

    This is an interesting and important discussion. There is one over-arching consideration which “unbundling” cannot address: all financial enterprises need a unified platform for communication, analytics, trading, and inventory management. The compliance/regulatory/legal economies of scale offered by Bloomberg make this consolidation imperative. Risk management, communication review, trade surveillance, and a host of other compliance functions become more expensive with each information system layered into the process.

    To be fair, vendors such as Charles River can handle this, but again, it becomes a layered-on cost.

  19. The words ‘platform’, ‘standard’ and ‘bundle’ are worthy of mention… a lot of firms build their businesses around Bloomberg. For instance, try using a third-party order-management system without Bloomberg data. Bloomberg is, incredibly, the only provider with complete market coverage and a consistent API. Since everything integrates with Bloomberg, it’s hard to go with anything else (a platform effect that’s a different type of network effect). Then, once you buy service for one purpose, everything is bundled… so often there’s little incentive to go to a third party solution… kind of how Microsoft killed Lotus with the Office bundle, and of course Windows integration… similar to the way any mobile device will eventually be an iPhone feature (music player, camera, GPS, fitness/body tracking), any financial software or data service will eventually be a Bloomberg feature if it isn’t already.

  20. With all that new Technology offers the user today, I don’t think anyone in the industry should be focused on building a BB killer. Instead the focus should be on building, and delivering, a cutting edge financial application for Free. This app should run on any device, and give the same financial analytics/models, market data, Global News and messaging, as any high end, fee based, software product that currently exists. Unless we are forced to, we don’t want to be tied to a terminal, or PC, in order to use a robust platform, we don’t want to have to pay a high fee, and we want the flexability to be highly Mobile. has started down that path with its current web app. It currently supports Stocks, Bonds, FRN, Callable Bonds, MM and Options. The user can create as many positons as they wish, and the models will Calculate Price, P&L, Sensitivity and Risk on Positions and the Portfolio. The ability for users to create as many Portfolios as they wish is currently being added, along with a Cross Asset Dashboard. The Dash will allow users to load up any positions/portfolios and see live P&L, Risk, Sensitivity and will be able to do What-If analysis. Other Models that will be added later in 2014, Swaps, CDS, CB, Swaptions, Caps/Floors, Futures and FX.

    After being in the Financial Industry for almost 30 years, I believe this business model, for Financial Software/Analytics, is the future.

  21. Reblogged this on Justin Badlam and commented:
    Matt Turck offers a fascinating take on the competitive marketplace for business data. Bloomberg has been the gold-standard for financial data and, obviously, deserves all the praise that is heaped upon it. As many people speak of the decreasing marginal costs of data and information, Bloomberg is still a thriving business and will likely continue to be one for the foreseeable future. As Turck notes, the network effects are indeed one of Bloomberg’s greatest strengths. The legions of financial analysts are trained to use the product and until something comes along that improves efficiency and ease of us, Bloomberg’s preeminence as a resource will continue. Financial companies are unlikely to cancel their subscriptions until a proven product is available. As Turck notes, extracting the signal from the noise is probably the most possible technological advancement that could cause some headaches for Bloomberg. Of course, these companies are still confounding the reality of dealing with big data and how to properly translate their analytical assessments into positive returns. Meanwhile, Bloomberg’s infrastructure is already positioned to adapt to those changing trends. The future of data is indeed an exciting one.

  22. I tend to agree with @DavidAFrankel. Companies in Fintech are well advised to take a long-term view on all aspects of their businesses. Companies need their short & long-term strategy, investors and management team to all be aligned. The bar is very high in Fintech.

    Fintech has unique challenges because you need to check the box in so many different areas to make any progress:

    1) Tech
    2) Internet DNA
    3) Finance/Investment Knowledge
    4) Assets (Company Balance Sheet, Data, Relationships)
    5) Long-term commitment

    The long-term commitment is one of the harder facets IMHO. The market is big enough for a lot of players. If you can continue to grow each year then you end up with a really large business over time.

    There is a fair amount of “technical debt” with incumbent companies that new companies can take advantage of. However, as someoneWhoWorksThere mentioned, data collection and quality is a major barrier to disrupting in Fintech.

  23. Really great article, and balanced as well. I also agree that while it sounds easy to disrupt an offering such as Bloomberg’s, I don’t see it happening for many of the reasons you highlight. I have worked with them for many years on a variety of issues, and while they may have been the ‘enemy’ at times, you had to respect not only what they had built, but the thoughfulness they brought to all of their actions.

  24. While the fintech market may not be disrupted by newcomers any time soon, what about the competition and cannibalization taking place between incumbents? Notably arch rivals Thomson Reuters vs. Bloomberg. TRI had a disaster roll out of its Bloomberg terminal killer: Eikon. How does Eikon stack up against Bloomberg in terms of data, speed, network effect, customer service, etc… Anyone betting on the Eikon?

    1. That’s a whole different topic but yes, the competition between incumbents is indeed fierce. Check out the Institutional Investor piece I refer to in the post, it has some good thoughts on the matter.

  25. One of the biggest issues with crowdsourcing was not mentioned (and it’s the problem that plages most of the aforementioned startups): content based on reciprocity. Numerous startups’ success lies on the fact that the information that they crowdsource is of same/similar quality than what you currently have in the marketplace (i.e. bloomberg). That is not yet proven and there are many reasons why data input is not incentivized to produce the best quality data. Unless someone finds a way to properly incentivize data originators many startups run the risk of garbage-in/garbage-out.

  26. One of the biggest issues with crowdsourcing was not mentioned (and it’s the problem that plages most of the aforementioned startups): content based on reciprocity. Numerous startups’ success lies on the fact that the information that they crowdsource is of same/similar quality than what you currently have in the marketplace (i.e. bloomberg). That is not yet proven and there are many reasons why data input is not incentivized to produce the best quality data. Unless someone finds a way to properly incentivize data originators many startups run the risk of garbage-in/garbage-out.

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