In 2011 the cloud finally went from an unproven curiosity to an accepted mainstream technology solution. In 2012 we will witness the deepening penetration of the cloud into the consciousness of multiple industries.  The market data industry is an interesting case in point because the major industry trends are all pointing to the rapid adoption of an on-demand cloud-based market data solution.

Let’s review each of these trends individually to understand how important the cloud will be for the market data industry in 2012:

1. Market Data Supplier Economics

The suppliers of market data are in a state of flux. On one hand, the cost of business is soaring with the new technology resources required to support sky-rocketing message rates, microsecond execution, and new regulation. On the other hand, the exchanges are experiencing sluggish revenue growth. Traditionally exchanges had four distinct sources of revenue: 1.) execution; 2.) listings; 3.) clearing; and 4.) market data. Of these, only market data is growing, while the others have either completely dried up or are not likely to be a significant source of revenue in the future.  The exchanges have responded to these unfavorable economics with a wave of consolidation in an attempt to reduce costs but the health of the industry depends on growing the revenue side of the equation. The exchanges understand that their best revenue strategy is to distribute their most valuable asset, market data, direct to consumers. This strategy has already seen success with the exchanges offering direct feeds, co-location, and other services to their low-latency clients.

In 2012 we will see more exchanges begin to focus on the largely untapped segment of consumers who need market data, such as historical trade and quote data, but are not latency sensitive. This is potentially a huge revenue source for the exchanges and is ideally suited to the on-demand market data cloud. Forward-thinking exchanges such as CME DataCloud, Direct Edge EdgeBook Cloud, and NASDAQ Data-On-Demand, have already moved in this direction, but 2012 will be the year that many more exchanges embrace the market data cloud to sell directly to consumers.

(Read more about how the exchanges are embracing the cloud in our recent blog post – Cloud Strategy for Exchanges and Financial Markets.)

2. Market Data Consumer Economics

As with the suppliers of market data, consumers are also facing an uncertain future. For many consumers, particularly in the financial services industry, the whole process of data management has become overwhelming. The old model of bringing all market data in-house, so that it can be accessed quickly, is under considerable pressure. There is now simply too much data to do this cost-effectively. Another related issue is time to implementation. With intense competition, many investment firms require immediate access to global and multi-asset class market data. Unfortunately, the traditional method of having a vendor add a feed can be a very slow process. Investment firms require a much more nimble solution that allows them to quickly access discrete data sets.

In 2012 we’ll see more firms conclude that not all data should be brought in-house. This change in mindset will lead firms to become much more discerning about what data should be stored locally, and what data should be retrieved on an ad-hoc basis from a market data cloud.

3. Proliferation of Mobile Devices

It is clear that we are in the midst of a technological sea change as the world moves from an Internet that is tethered to desktop PCs, to one that can be accessed from anywhere, through a wide array of always-on smartphones and tablets. Many experts predict that by 2014 mobile Internet usage will overtake desktop Internet usage. Already in late 2011 we saw mobile-based local search overtake PC-based search. The mobile Internet revolution will affect the market data industry greatly as apps and websites designed specifically for these devices demand more and more market data. An on-demand market data cloud is ideally suited for this mobile revolution because mobile apps typically require only discrete on-demand data sets and app developers are not inclined to build their own data management infrastructures. In 2012 the market data industry will experience a huge uptick in demand from theses mobile devices.

(Read more about the proliferation of mobile devices as it relates to the buy-side The 3 Phase Evolution of Buys-Side Mobile Apps.)

4. Regulation

Regulation is always a factor in any discussion on trends in the market data industry. 2012 will be no exception, particularly because many of the regulations that were proposed in direct response to the financial crisis of 2008, will either be implemented, or will be at a point in the approval process where we will have a better sense of their likely impact. The main thrust of new regulations such as Dodd-Frank, the Volcker Rule, and Basel III, is to manage systemic risk and to force transparency across the financial services industry. All of the important industry players will be significantly impacted including the exchanges, the buy-side and the sell-side.  Complying with these new regulations will involve onerous and costly reporting requirements. These regulatory requests for data will serve to accelerate the adoption of on-demand cloud-based market data solutions because many of the players simply do not have the resources necessary to comply with these and future regulations. It is also likely that regulators themselves will favor cloud-based market data because it will promote industry best practices and transparency.

2012 will certainly be a year to watch for the market data industry and the adoption of the on-demand market data cloud model.  We can expect to see the 4 major trends outlined above to all converge to make this the year of the market data cloud.

Technology has led a dynamic transformation of the exchange industry, and cloud strategies are an important factor in unlocking the revenue potential of exchange data.  The following article about how to monetize exchange data via the cloud was recently published in “How To Build An Exchange,” a fascinating must-read collection from our friends at Mondo Visione.  Request access to the full collection from Mondo Visione here. 

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Monetizing Market Data in an Evolving Global Marketplace

The global environment for exchanges and data originators is rapidly evolving with exploding data volumes, increasing and unpredictable regulation, expanding variety of financial instruments, fragmenting market, competitive encroachment and the need for new sources of revenue. Beyond these challenges, the race to microsecond latency will stretch the infrastructure capacities and budgets at major financial services firms.

Exchanges and data originators are struggling to service the opportunities of the future with the products and solutions of the past. A paradigm shift is rapidly approaching as old monolithic systems designed for universal applicability are being replaced by new custom tailored micro and mobile apps designed to satisfy the needs of a specific knowledge worker or user type.

The emergence of new mobile technologies and use-specific applications is altering the usage patterns of market data and changing the ways market data is purchased and consumed. If an emerging exchange is to succeed, it must tackle these issues in new ways.

A few years from now the market data application mix will boil down to two distinct flavors: low latency and everything else. How will exchanges capture new market data revenue profitably and manage the delivery of these two distinct flavors when their target applications, usage patterns and infrastructure requirements are so dramatically different?

Given its mission-critical impact on trading efficiency, low latency infrastructure has historically received the bulk of attention and investment. Meanwhile, legacy data distribution systems are crumbling under the weight of the increasing volumes of data produced by low-latency trading. The cost and complexity of servicing everything else is skyrocketing. Enter cloud computing. Once thought to be the domain of small and medium-sized businesses, cloud strategies are now seeing investment from exchanges and data originators to gain economies-of-scale, simplify the access and delivery of market data, decrease time-to-market for new products and add new revenue streams from new and underserviced segments.

In August of 2011, The Melbourne Group laid out the following cloud action plan for existing and emerging exchanges:

Make Core Market Data Assets Available from the Cloud

By creating an Internet-based cloud offering using standard cloud APIs, exchanges can offer both institutional and retail customers a means to access their core market data assets on-demand while driving down both the costs of access for the customer and the costs of service and maintenance for all.

Create Cross-Selling Opportunities with Direct Feed Customers

Direct feeds designed to service automated trading applications are poor vehicles for usercentric and mobile applications. A cloud offering coupled with an exchange’s direct feeds allows buy and sell-side firms to synchronize front and back-office operations seamlessly.

Monetize Exchange Data Assets by Targeting Unmet Business Needs

Without the cloud, the sheer volume of stale data sets make broad delivery cost-prohibitive to anyone but the largest clients. With the cloud, exchanges can move away from the one-size-fits-all approach and offer custom tailored products that allow customers to select and consume only the data required for their specific applications, modeling or back-testing.

Expand and Simplify Global Access and Delivery

Cloud-based delivery can be complemented with an e-commerce purchase and provisioning system to access new geographic regions and new customers through a 24×7 web presence which allows customers to select, purchase and use an exchange’s data products without a local sales organization, or any sales department intervention at all.

Increase Revenue by Expanding Retail Channels

A scalable, low-cost cloud platform allows exchanges to distribute exchange data directly to retail websites and investors. By accessing these channels directly, exchanges open revenue streams currently outside their reach and strengthen their brand within new and existing markets.

Deliver New Products Faster to Meet Emerging Customer Needs

New trading products create new information needs for financial services firms and market participants. The cloud allows exchanges to provide immediate global access to new market data products, removing constraints imposed by traditional market data vendors and eliminating infrastructure investments by customers.

Increase Market Transparency for OTC Derivatives and Complex Products

New exchange-traded products and new reporting requirements brought forth by Dodd-Frank, RegNMS and MiFid create new information needs for financial services firms and market participants. A cloud solution can provide both market participants and regulators easy global access to activity in these markets, providing accurate and auditable information that helps financial institutions manage risk and meet regulatory and internal compliance requirements.

The last few years have been undeniably tough for the once brash hedge fund industry. Recent headlines do not suggest any improvement with August being the worst month for hedge funds since October 2008, and marquee firms like Paulson & Company firm down 34% year-to-date.  Prior to the crisis of 2008, the industry appeared to be on a steady upward trajectory, evolving from a small, scrappy upstart, that catered to high net worth investors, to a more formalized $2 trillion industry, that serviced the largest pension funds in the world. Since the crisis, however, the industry seems to have lost its way. What exactly happened and how can what we term the “Hedge Fund Cloud” return the industry to its former glory?

Institutionalize or Die

Pre-crisis, managers believed that the measure of success was not only returns but assets under management. In their race to acquire new assets, managers were motivated to “institutionalize” their infrastructure so that they could go after the really big allocations from large pension funds and endowments. For many firms this institutionalization meant leaving the relative simplicity of their single prime relationship to the much more complex world of building out their own multi-prime infrastructure. Almost overnight managers found themselves running complex and unwieldy businesses. Seemingly simple operations like adding a strategy, that required a new asset-class, or producing a new report, became long and involved IT projects.  Any thought of outsourcing any of this burden was dismissed because of perceived privacy and control concerns.

Prisoners of their own Hedge fund Infrastructure

The actual crisis further exposed the inflexibility of hedge funds’ infrastructures. Managers struggled to view their true exposure across asset classes and multi-prime relationships. Just when managers most needed their former agility they discovered that they had become prisoners of their own expensive infrastructures.

Fast forward to today. We are still experiencing the after effects of the crisis. A strong regulatory backlash response has been unavoidable. There is still tremendous uncertainly about the true impact of these new regulations, but what is certain, is that the business of running a hedge fund will become even more complex and costly.

How can the industry remove itself from this funk and prepare itself for the next crisis? The answer is that the industry needs to return to basics by once again making alpha generation its sole focus. The industry needs to regain its former investment agility. In short, managers need to get out of the running-a-hedge-fund business and get back to the investment business.

The Hedge Fund Cloud to the Rescue

Fortunately, the Hedge Fund Cloud offers managers the opportunity to get back to basics.  The Hedge Fund Cloud allows firms to focus on alpha generation by moving all non-core infrastructure to the cloud. The Hedge Fund Cloud is made up of the fast-maturing ecosystem of cloud-based hedge fund service providers who have now begun to offer institutional-grade infrastructure.

The three main components of the Hedge Fund Cloud are as follows:

1 – Hedge Fund Cloud – Software-as-a-Service (SaaS)

SaaS means that software can now be accessed through a thin client or browser, from anywhere, as a service. SaaS includes upgrades, disaster recovery, and is paid for on a subscription model. Many of the important hedge fund systems including execution management, order management, risk management, portfolio management are now available from the leading providers on a Software-as-a-Service basis.

2 – Hedge Fund Cloud – Data-as-a-Service (DaaS)

DaaS delivers financial market and reference data via web services or API’s rather than the traditional data feeds and flat files.  This approach allows firms the ability to query and use just specific subsets of data rather than downloading and storing masses of data. It also means that firms do not have to build out data management infrastructures.

3 – Hedge Fund Cloud – Infrastructure-as-a-Service (IaaS)

IaaS involves the outsourcing of physical hardware such as telephony, storage, servers, and networking components.

To take advantage of this new model, managers must now take an impartial eye to their existing hedge fund infrastructures and ask themselves what really is vital to alpha generation. Everything else should be stripped away, moved to the cloud, and essentially turned into a utility. This return to basics offers the industry the best chance to get back to the pre-crisis glory days and to restore its mojo.

Perhaps nowhere more in the diverse world of hedge fund strategies is the prospect of alpha decay more unsettling than at high-frequency trading firms. In many ways high-frequency trading firms are now facing a reality that other hedge funds with more esoteric strategies may one day face – too much money chasing a finite amount of alpha.

The End of the Hardware Arms Race

The latest figures indicate that high-frequency trading now accounts for somewhere between 60-70% of trading volume in the US. This is up from around 35% just five short years ago. High-frequency trading firms, that in the past have been among the most profitable on Wall Street, are now seeing that increased competition has crowded out many of their traditional strategies. High-frequency trading firms have responded by co-locating their black boxes and by throwing ever more expensive hardware at the problem. This approach has worked for some of the larger, better-funded firms, but only postpones the inevitable.

What will happen when this hardware arms race meets the laws of physics? The answer is that many of these firms will need to develop high-frequency trading strategies where alpha is still relatively abundant and competition less fierce.

New High-Frequency Trading Alpha Opportunities

The two most common opportunities that high-frequency trading are now exploring are as follows:

1. Alpha outside the US – The US was the first to develop electronic trading, other regions are still in the process of building out their electronic trading infrastructure. A recent Credit Suisse study estimated that high-frequency trading activity accounts for 35% in Europe and only 10% in Asia (excluding Japan). Many high-frequency trading firms are now seeking to port the strategies that worked so well in the US over to these less developed markets.
2. Multi-Dimensional Strategies – The traditional strategies of high-frequency trading have typically been one-dimensional involving the high-frequency trading in and out of a single large, liquid name. The newer high-frequency trading strategies are much more complex and multi-dimensional in nature searching for arbitrage opportunities across asset-classes, geographies etc.

The New Arms Race and Cloud-based Market Data

The common thread that runs through these new strategies is the need for access to more diverse and dispersed market data sets. Market data has always been the lifeblood of high-frequency trading firms. It has also been one of the real pain points of this industry with the requirement to build out large infrastructures to manage and query terabytes of information. This new world of alpha discovery means that high-frequency trading must now potentially analyze vastly greater amounts of data to discover opportunities. Furthermore, the competition to discover these new opportunities will be intense. In this new world alpha will also be more fleeting as firms quickly clone successful strategies. The new high-frequency trading arms race will be all about alpha discovery and time to implementation.

As the high-frequency trading industry’s search for alpha switches from a hardware focus, to a discovery/speed of implementation focus, it is clear that firms with access to large and diverse data sets, that are easy to query and work with, will be at a distinct competitive advantage. The traditional method of downloading vast data sets to a firm’s own data center will not work because of the cost and time delays involved. The only solution that is flexible enough to assist in such rapid discovery and implementation is a Cloud-based approach, where small sub-sets of global multi-asset class market data can be quickly isolated and analyzed. Xignite is the only firm committed to this vision of  large, diverse sets of Cloud-based data, all made accessible by our easy-to-use web services, that require no infrastructure, and that can implemented in a matter of minutes.

As this new arms race intensifies and the search for alpha becomes increasingly more desperate we will see more and more high-frequency trading firms abandon their in-house data management infrastructures for the flexibility and breath of data that Xignite’s Cloud-based solution offers.

Each of the three largest US exchanges, NASDAQ, NYSE and CME Group has recently announced its cloud strategy, but exchanges and financial markets as a group have been slow to get on the cloud bandwagon. Too slow given the potential benefits to their customers and their own needs to increase revenue, market transparency and competitive advantage. That according to a recent white paper released by The Melbourne Group entitled The Winds of Change in Market Data : Winning Cloud Strategies for Exchanges and Trading Venues.

The new NYSE Technologies’ Capital Markets Community Platform is a significant and bold IaaS play that aspires to become the Amazon Web Services for financial markets by leveraging the tight community and unique technology needs within financial services that so often prevent generic offerings like AWS and Azure from competing on Wall Street. NASDAQ and CME Group on the other hand have placed their initial cloud bets one level up on the cloud stack with data-as-a-service offerings, NASDAQ Data-on-Demand and CME Data Cloud respectively.

Now that the big three have all made claims to the cloud, it seems like only a matter of time before each of the remaining hundreds of stock exchanges and trading venues around the world follows suit with its own cloud strategy. Right? The answer is not so clear, because every exchange is a little different. Different markets. Different regulations. Different customer needs. Very few have the capital and scale to tackle the IaaS market. In fact, it remains to be seen if NYSE can achieve anything near the automation, elasticity and scale of AWS within the limited scope of financial services. According to the Melbourne Group, the safer cloud strategy for most exchanges and trading venues is probably closer to the CME and NASDAQ approaches that use the cloud to reach new customers and monetize underutilized market data assets.

The business case centers around four trends that are creating significant market data technology challenges for financial market participants.

• Market data costs are skyrocketing due to the expansion of electronic trading
• New trading venues and technologies are driving mergers and consolidations
• Transparency requirements driven by regulator responses to the financial crisis
• Mobile technology enables new usage patterns for market data

Altogether, these trends add up to the the exchange version of the “too much data, too many apps crush” that is being felt throughout the financial services sector. In response, The Melbourne Group offers up the following cloud strategy for exchanges and trading venues.

There appears to be decent upside to adopting any one of these recommendations, while the downside seems minimal. Unlike IaaS, market data doesn’t come with the high security obstacles that are pervasive in the financial services sector. The primary challenge is cost-effective distribution of exponentially increasing amounts of market data and a good cloud strategy should open up many opportunities for exchanges and trading venues in both cost reduction of internal data management and revenue enhancement from expanded data delivery.

[Note: This article is cross-posted from Cloud Bulls]

Like many of us, you may have run into challenges when importing real-time market data in Excel, Visual Studio & SQL Server. With the recent addition of Xignite Financial Market Data to the Microsoft Azure Marketplace Datamarket, users can now pull live stock quotes directly into Microsoft Excel 2010, develop data rich applications using Visual Studio 2010 and seamlessly populate backend database with SQL Server 2010.

In late 2010, Xignite and Microsoft teamed up to make XigniteBATSLastSale, a real-time stock quote data service, available through the Azure Marketplace Datamarket, with the addition of other datasets scheduled to rollout in the months ahead. With this, Microsoft users can obtain instant online access to real-time stock quotes from the BATS Exchange. Now everyone from hardcore mobile app developers to garage based startups can obtain access to the same professional grade APIs to power their applications.

New to Xignite is the addition of OData, or Open Data Protocol, a method of querying and updating data which unlocks the data from the silos that exist in legacy applications. Today, OData is being used to expose and access information from a variety of sources including, but not limited to, relational databases, file systems, content management systems and traditional Web sites.

Wes Yanaga with Microsoft’s Channel 9 sat down with Shoshanna Budzianowski (SQL Azure) and Chas Cooper (Xignite) to get the low down on what this partnership is all about, and how it will benefit end user around the globe.

After the Channel 9 video was completed, Shoshanna Budzianowski sat down with Marc Bollinger, Web Service Engineer at Xignite, for some one-on-one developer talk and a bit of programming fun (CLICK MORE to see developer video)

Looking forward, Xignite has plans to put the vast majority its data onto the Azure DataMarket. “We have started with our most popular data sets, but will continue to add data until the market is satisfied with what Xignite is offering” said Chas Cooper, VP of Product management at Xignite.

Microsoft brings a very horizontal market to Xignite, with DataMarket providing broad coverage across different verticals. When Microsoft looked for companies to work with on Data Market, two things attracted Microsoft to Xignite; 1) the fact that Xignite understands how to deliver on-demand market data and financial web services” and 2) out of all the financial data providers evaluated for Azure Datamarket, Xignite has the most reasonable, rational terms of use across its data sets. Xignite also uses standards such as SOAP, REST & JSON to make integrate with Xignite’s web services simple and straight forward.

Developers should visit http://datamarket.azure.com, where they can type Xignite into the search bar there and see all of the services available. More information is available at www.xignite.com.

New to Xignite?  Curious about what we do and what were all about?  This interview with Xignite’s Joel York may provide the answers your looking for.  Visit TMCnet.com to view other videos from leading Silicon Valley companies.

Algorithmic trading firms were put on notice last Monday when Commissioner Bart Chilton of the Commodity Futures Trading Commission (CFTC) said that high frequency trading (HFT) and algorithmic trading firms should be made legally responsible for maintaining a minimum set of testing and monitoring standards to prevent future flash crashes.

The possibility that algorithmic trading firms may need to support new regulatory requirements highlights the rapidly growing importance of the market data cloud and new cloud services like NASDAQ Data-On-Demand for back testing purposes.

In a bid to resuscitate the heroes of 1980’s sitcoms, Mr. Chilton declared, “I want to be like MacGyver. Remember, he was always trying to prevent crime before it happened.” So if you’re a criminally culpable algorithmic trading firm, you’d best beware—especially if Mr. Chilton gets his hands on a paper clip and a stick of chewing gum. Household items aside, Mr. Chilton also plans to rely on tools such as a “kind of Good Housekeeping Seal of Approval.” He’d like algorithmic trading systems to be tested by either exchanges or regulators before going live. And after going live, he’d like algorithmic trading systems to be monitored on an on-going basis.

So, if you’re engaged in algorithmic trading, you’ll probably want to make sure you’ve got systems in place to test your algorithms so you’re prepared for any regulatory requirements that might come down the pike. That’s where the market data cloud comes in. If you want to future proof your algorithmic trading systems against any possible testing requirements, you’ll want to look into how you can use the market data cloud to back test your algorithms with minimal expense, time and effort put into building new technology infrastructure.

On-demand market data cloud services like NASDAQ Data-On-Demand offer a new way to hold down both the cost and time investments of getting historical tick data to back test algorithmic trading systems. You can get only the slice of data you need for your specific test conditions, and you only pay for what you use, not the entire universe of data, which dramatically reduces your data costs. On-demand cloud services also let you get highly customized data sets in a variety of common formats (XML, CSV, JSON and so on) which reduces your need to build new technology infrastructure just to load the data into your algorithmic trading systems.

So if Mr. Chilton knocks at your door with the latest issue of Good Housekeeping under one arm, never fear. Just check out NASDAQ Data-On-Demand and rest assured you can handle any algorithmic trading regulatory requirements for back testing that he might have in mind without breaking the bank on new technology infrastructure.

(For more details on Mr. Chilton’s public statements, see the recent Securities Industry News coverage.)

This week NASDAQ was the first major exchange to launch an on-demand service offering Level 1 historical stock tick data.  As the first truly on-demand service for historical stock tick data, NASDAQ Data-On-Demand is leading a revolution that’s democratizing access to market data and reducing costs by orders of magnitude.

NASDAQ Data-On-Demand has made it easy and affordable to get access to historical stock tick data for everyone from individuals to large institutions.  Individuals can analyze historical stock tick data in Excel, while large institutions can develop large-scale applications like algorithmic trading applications, using NASDAQ Data-On-Demand’s historical stock tick data for back-testing their algorithms.

Individual investors, professional traders, analysts and other individual users can get started for as little as $10 per month and gain instant access to start pulling historical stock tick data into Excel directly from the NASDAQ Data-On-Demand website.  Individuals get the same sophisticated analytics and detailed Level 1 historical stock tick data previously available only to large institutions.  And most importantly, traders can cherry-pick specific data with pinpoint accuracy, eliminating the need for custom-developed systems to manage and process the data for them.  Since pricing is usage based, individuals can use highly targeted queries to pay only for the data they need, making historical stock tick data affordable even for individual investors.

Application developers can power their applications with historical stock tick data accessed in real time through web service APIs.  Gone are the days of loading terabytes of historical stock tick data into a giant database, hiring a DBA to optimize the database and building a data access layer to run queries that retrieve the data your applications need.  Now your applications can call an API directly from NASDAQ Data-On-Demand web services, pass parameters defining a specific subset of historical stock tick data, and get back only the tick data your application needs right at that moment.  No need for databases, servers or DBAs.  (Sorry, DBAs.)  And with no need for technology overhead, the costs and timelines for pulling data into applications plummet.

With NASDAQ taking up the on-demand banner and leading the charge with historical stock tick data, it will be interesting to see how this new development affects the on-going movement toward an on-demand market data cloud.

Henry Ford once said: “Any customer can have a car painted any color that he wants, so long as it is black.” Then, in 1923 Alfred Sloan came along and cleaned his clock by offering a tremendous variety in colors and models. But, Sloan didn’t do it one customer at a time. GM redesigned its manufacturing line with the flexibility to produce a multitude of models and colors without compromising the inherent economies-of-scale of Ford’s assembly line innovation—a practice that today has evolved into the concepts of flexible manufacturing and mass customization.

What does any of this have to do with cloud computing? And for that matter, what does it have to do with financial market data? This is the third post in a series called “Thinking Out Cloud” with the aim of helping financial services and market data IT professionals charged with developing cloud computing strategies separate the cloud buzz from the cloud reality. This post explores the important idea of mass customization in the cloud and its relevance to market data management.

Mass Customization and Market Data

According to Wikipedia, mass customization is about providing “services to meet individual customer’s needs with near mass production efficiency” by offering a “tremendous increase in variety and customization without a corresponding increase in costs.” Or rather, have it your way at a cost you can afford. In the last installment in this series entitled Thinking Out Cloud – The Market Data Sweet Spot, I pointed out that the best way to identify market data that can benefit from cloud computing is to look for data that is hard-to-use, hard-to-manage, and hard-to-access. Mass customization on the cloud strikes at the core of the “hard-to-use” problem by replacing inflexible data feeds and files in static formats with financial Web services that allow applications to select and consume highly customized market data sets on the fly.

Mass Customization, Meta Data and Web Services

In software, mass customization is achieved by converting hard-coded application functions into meta-data, so that the functional behavior of the software can be controlled through data on an ad-hoc basis. For example, the only real difference between this Xignite WordPress blog and a million other WordPress blogs comes down to a few theme files and the store of data that comprises the brilliant blog posts within. The posts in turn get shuffled around the Internet using RSS (really simple syndication). The RSS standard lies at the heart of the Web 2.0 revolution, powering the distribution of widely varied content such as blogs, news, friends, tweets, music, video, etc., all because a few abstract meta-data elements like “title”, “description”, and “link” can be generalized to contain an enormous and disparate variety of data. Swapping out this data can literally change one blog to any other blog (or one friend to any other friend!).

In cloud computing, meta-data abstraction is realized through Web services (like RSS). Web services take the concept of configurability through meta-data abstraction to its natural limit, because every operation of a Web service is inherently abstracted to meta-data in the XML inputs and outputs of the API. For example, the Xignite delayed stock quote Web service allows applications to retrieve a single quote for a single symbol, multiple quotes for multiple symbols, intraday tick-by-tick prices, market volume and news, simply by varying the Web service operation and the input parameters, i.e., the meta data. Providing access to such a wide variety of information in an ad-hoc fashion from a statically formatted feed or file is simply not possible without lots of hardware, software and custom development–most likely to create a Web service.

Wrap it Up, I’ll Take It!

More plainly, market data Web services are built for mass customization, because they cleanly wrap the underlying market data and data management infrastructure in meta-data enabling highly customized output based on variable input. Therefore, applications that require highly varied, ad-hoc market data subsets will be better served by Web services over the static formats of data feeds and files. Or, as we like to say at Xignite: “There’s an op[eration] for that!”