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 strategy bets one level up on the cloud stack with data-as-a-service offerings, NASDAQ Data-On-Demand and CME DataCloud 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.

Every day automated trading creates mountains of historical stock data that traders must store and manage for trade execution, compliance, and modeling trading strategies. You’d think it would all be on the cloud already, but it isn’t. While most of the financial services sector ponders the future impact of the cloud, NASDAQ OMX is exploring ways the cloud can make life easier for stock traders today.

The Automated Trading Fire Hose

While many people have heard of automated stock trading and some of its more spectacular consequences like the 2010 Flash Crash, what most people don’t know is that there is a financial information technology arms race going on that has produced radical changes in stock trading over the last ten years. With every progression in industry standards, increase in processor speed and decrease data storage costs, automated traders up the ante by developing faster, more computationally intensive algorithms to outwit the market and outsmart each other. The result is a feedback loop that demands ever increasing data processing power and data management efficiency. One can debate whether this is a good thing or a bad thing, but what is not up for debate is that financial services firms that want to stay competitive must constantly look to new technologies to avoid being crushed under the relentless deluge of stock trade data.

To put the growth in automated trade data in perspective,
this graph shows the multiple orders of magnitude growth in quote and trade volume
in US options over the last ten years.
Source: Presentation by CBOE at the Financial Information Forum, 10/2010

The Big Historical Stock Data Dilemma

Historical stock data in particular is a real problem, because it just gets bigger and bigger as the trades pile up over time. Data management costs increase in lockstep with the amount of historical stock data stored, but value declines as the immediate usefulness of any given historical trade is not always apparent. For example, you can’t unleash an untested trading algorithm on real money, so every automated trading algorithm requires extensive back-testing with historical stock data. That’s a big data management problem when you don’t know today what automated trading algorithm you will be testing tomorrow. You don’t know what historical stock data you will need until you need it, so you must store it all just in case.

NASDAQ Data-On-Demand | Tick Data on the Cloud

Last week NASDAQ OMX released a new enterprise version of NASDAQ Data-On-Demand, a cloud-based historical stock data-as-a-service with 3 years of historical stock trade and quote data, including every tick for every symbol traded on NASDAQ, NYSE, and US OTC markets. That translates into mountains of data that customers of the service don’t have to manage internally. Users can request custom data sets as needed by selecting historical stock data for specific ticker symbols, data fields, and time frames. Depending on the size of the output, the data can be accessed either through a Web service API that can be plugged directly into applications or through a batch request interface that provides various file formats for FTP download.

In addition to straightforward slice-and-dice access to historical stock data, NASDAQ Data-On-Demand also provides an historical analytics service overlaid on top of the raw data to sweeten the pot. For example, users can calculate VWAP and TWAP (volume/time weighted average price) for a given stock over a given period on the fly by picking the specific time range of trades down to the sub-second level with the result typically returned in under one second.

Just the Beginning?

This is surely the first of many big data migrations to the cloud in the financial services sector. Financial market data in particular is a natural fit for cloud storage and data-as-a-service delivery. It’s big, it’s in demand, it’s valuable, and unlike corporate and consumer data, it isn’t private. Moreover, automated trading is systematically making inroads into every global market and every asset class from equities to forex. It’s just too much data for most firms to manage in-house. That new giant sucking sound we’ll soon be hearing will be the whirlwind of all those petabytes of historical trade and reference data rising up to the cloud.

Financial services IT departments are experiencing quantum leaps in data volume and application complexity that require commensurate quantum leaps in underlying financial services technology. The cloud in all its flavors: SaaS, PaaS and IaaS, public and private, offers the most promising refuge.

The Financial Market Data Explosion

Eric Schmidt, CEO of Google, recently claimed that “every two days we create as much information as we did up to 2003″.  While his comment was largely directed at consumer media and the creation of user-generated content in applications like Facebook and Twitter, it is equally if not more relevant to financial market information.  Like consumer media, financial markets generate reams of unstructured data from earnings call transcripts to analysts reports to news to blog posts.  However, financial markets are also generating exponentially increasing amounts of market data due to high frequency trading, venue fragmentation, and an ever expanding pool of financial instruments.

High frequency trading, market fragmentation and instrument proliferation
are driving exponential increases in the volume and variability
of real-time and historical financial market data

Financial services IT shops are struggling to keep pace with this explosive growth in market data. Moreover, the financial market data explosion isn’t limited to real-time trade data, because every tick flows into gargantuan historical databases used for modeling and analysis. The volumes are simply staggering and the resulting pressures on financial services IT infrastructure predictable. Fully two-thirds of financial services firms fear their analytics programs and infrastructures will not be able to handle increasing analytical complexity and data volume, according to a just-released research report, featuring a survey of financial services IT professionals conducted by Wall Street & Technology in conjunction with Platform Computing, SAS and The TABB Group.

Attention Economics and The App Revolution

Translating these exponentially increasing amounts of data into useful information is no easy task.   Just like consumers, business users are constantly making trade-offs in how much time and attention they are willing to spend digging through irrelevant data to get to the relevant information they need to do their jobs.   Business users need more targeted access and specialized applications. This requires financial applications to shoulder an ever increasing burden of scrubbing, filtering, crunching, and presenting the data in bite-sized chunks of useful information that the average human being can digest and use to take positive action.  However, the simpler it gets for the user; the more complex it gets for IT.

The everything-in-a-box Bloomberg terminal model is breaking down, because it places too much of this increasing data burden on the user and not enough of it on the application. Financial services IT departments are no longer willing to pay for irrelevant data just because it comes “in-the-box,” and are increasingly opting for specialized and mobile apps that provide users and downstream applications with just the market data they need, where they need it, when they need it.

Competitive Advantage and the Need for Speed

Only an order-of-magnitude increase in agility will enable financial services IT departments to handle these order-of-magnitude increases in demand for data and applications. High frequency trading isn’t the only area of financial services IT where speed is critical. Sustainable competitive advantage is built on the ability to deploy and evolve critical IT services and infrastructure faster than the competition, not on some unique algorithm or application. Inflexible architectural and deployment models that rely on the false assumptions of stable user requirements and a stable industry environment are feeling the crush between the exponential demand for data capacity on one end and the increasing demand for specialized applications on the other end. There just isn’t enough time to keep up.

While financial services IT professionals will no doubt prove to be the most cautious adopters of cloud technologies, they are perhaps in the most need of them. The cloud in all its flavors: SaaS, PaaS and IaaS, public and private, offers the most promising refuge from this crisis. The reason is straightforward: on-demand. To quote Wikipedia: Cloud computing…resources, software, and information are provided to computers and other devices “on demand, like the electricity grid“.” From on-demand deployment of private cloud server instances to handle real-time risk management processing spikes to instant upgrade of a SaaS portfolio management application, cloud solutions shrink deployment and maintenance time by an order-of-magnitude. The speed gains offer the flexibility financial services IT departments need to adapt to the increasing and variable demand for data and applications, while the time savings can free up overburdened IT resources to focus on projects that deliver the most competitive advantage.

Like any disruptive technology, cloud computing promises to help businesses do things better, faster and cheaper.  However, I am convinced that when presented the choice of doing things better and faster on the same IT budget versus doing things the same with a reduced IT budget, competitive growing businesses consistently choose time over money.

The representation of lower TCO as a cost savings is misleading,
because virtually every cloud business case built on lower TCO
is really a time savings masquerading as a cost savings.

This opinion is based entirely on my own experiences, but recent surveys by Sandhill.com and Banking and Technology depict a very consistent, fact-based picture of when and why IT professionals choose the cloud. Let’s face it. CIO’s don’t win awards for saving money.  CIO’s win the praise of their companies, their colleagues and their user communities when they deliver better capabilities faster than the competition.  It is time savings, not cost savings that is driving cloud adoption.

Speed Fuels Competitive Advantage

Technology has become essential to virtually every business operation.  Entering new markets, launching new products and offering new services invariably result in new IT requirements.  And, it is often the case that these new requirements sit squarely on the the critical path to getting things done.   The classic SaaS CRM adoption tale is the new VP of Sales with an unforgiving quota who signs up for a SaaS CRM even though it goes against the corporate IT standard, because it is the quickest and surest route to business success.  Fast moving IT departments are competitive assets to their companies.  Slow moving IT departments become liabilities.

On-Demand Increases ROI

The on-demand cloud combination of rapid, elastic deployment and pay-as-you-go pricing can add up to superior ROI, even when total out of pocket expenditure of a cloud service significantly exceeds that of an on-premise software alternative.  Time to value shrinks and CAPEX becomes OPEX as big up-front investments are spread out over time into modest renewal payments.  Time leads; money follows.

Flexibility Drives Efficiency

The cloud versus on-premise software decision is not unlike that of buying versus renting an automobile.  If the vehicle is not critical to your business, then cost effectiveness will be your primary concern and your best bet is purchase combined with a minimum amount of maintenance.  If the vehicle is a race car, again your best bet is purchase combined with heavy customization and dedicated pit crew. But if the vehicle is a critical, everyday workhorse to your business, then reliability becomes your chief concern. You will want to keep it in like-new condition to ensure smooth operations.  In this scenario, rental becomes a compelling alternative to purchase. The rental company keeps the car in top condition.  You can easily upgrade to the latest model. You can adjust your motor pool to match demand. And, you don’t have to hire a mechanic.

More Time Means More Opportunity

Economists like to use the term “opportunity cost” to define what could have been had we used our time more wisely, revenue and savings missed.  But the real culprit behind opportunity cost is time, or rather not enough of it.  Every IT department has a seemingly inexhaustible backlog of valuable projects.  The challenge is never doing less with less, but doing more with less.  Offloading IT workloads to the cloud increases the leverage of IT workers by freeing up time to tackle valuable projects that would otherwise never make it off the backlog.

Saving Money is Really Saving Time Too

SaaS and cloud computing vendors claim their solutions offer dramatically lower total cost of ownership (TCO) when compared to on-premise software.  However, the representation of lower TCO as a cost savings is misleading, because virtually every cloud business case built on lower TCO is really a time savings masquerading as a cost savings.  The majority of the TCO savings in every cloud vs. on-premise analysis comes from the reduced IT staff required for deployment, training, administration, maintenance, and upgrades.  However, it is the rare cloud IT project that actually realizes these cost savings by following up the launch party with a layoff.   Staff is universally reassigned to the next project on the backlog, thus revealing the true business motivation to be the need to update the technology and free up staff:  doing more better and faster, not less cheaper.

Given all the hype about cloud computing, it refreshing to see some real data. So, if you are a banking IT professional or you work for a cloud computing or SaaS company that is targeting the banking industry, you might want to check out the recent survey conducted by Banking Systems and Technology in partnership with Information Week.

While some of the survey results reinforce what should be common sense to anyone involved in financial services technology, such as security and compliance trumping cost as the primary concerns when purchasing a cloud solution in banking. I did find one data point a little surprising. According to the survey, the number one reason for considering cloud technology in the first place wasn’t to save money or to manage capacity. It was speed. Not low latency, but project delivery: “The ability to meet user demands quickly and achieve scale.”

Just these two data points alone provide strong guidance to would-be cloud vendors to the banking industry. First, make sure you have security and compliance nailed before you even bother to approach a prospect. And second, focus on helping the IT department delivery more, faster to its user community.

For the complete survey results and a more in depth analysis, see Penny Crosman’s original article in Banking Systems & Technology.

Cloud computing has graduated from technical curiosity to technology trendAccording to a recent Gartner survey, cloud computing and virtualization sit squarely at the top of the list of 2010 CIO priorities. However, most financial services IT professionals are still taking a cautious approach to cloud computing. This is quite understandable given the high performance, security and compliance requirements of financial applications. Unlike Twitter, you can’t just post the fail whale when a financial application has an issue, because real money is at stake not just tweets. So, what financial applications are good candidates for cloud computing and why? To cloud or not to cloud? That is the question.

This is the first post in an series entitled Thinking Out Cloud with the aim of helping financial services IT and market data professionals charged with developing cloud computing strategies separate the cloud buzz from the cloud reality.

Focus on Competitive Advantage

Realistically, somewhere between 75% and 95% of the software used by most businesses today is a commodity. That is, it offers no competitive advantage because the competition has access to the exact same capabilities from a plethora of software vendors, SaaS vendors, open-source projects and home-grown systems. This means that if your financial services IT department does everything in-house, pretty much 75%-95% of your IT department’s hard work and budgetary expense offers a competitive advantage of exactly zero. Only the 5%-25% spent on systems that help your business stand out from the competition make a real difference to top line revenue. Yet, I’ve never seen a financial services IT department that had extra time on its hands. Therefore, the first consideration in any analysis of cloud computing should be to weigh the competitive importance of the various systems under IT management, and to look for opportunities to free up resources by pushing systems that do not offer competitive advantage to the cloud.

Leverage Cloud Computing to Lower Costs

The primary financial benefit of cloud computing is lower total cost of ownership created through the economies-of-scale of shared computing resources. It makes no difference if it is infrastructure from Amzaon AWS, CRM from Salesforce.com, or market data from Xignite, the underlying common thread is that these vendors can do it significantly cheaper by the dozen than customers can do it in-house one at a time. This cost savings is then passed onto the customer in the form of an ongoing usage-based subscription. Conveniently, this prospect of lower overall costs fits nicely with the idea of outsourcing commodity systems. Therefore, the next consideration in an analysis of the benefits of cloud computing for a particular system should be the relative costs savings. It is critical at this point to compare the total cost of ownership of each internal system to a cloud vendor’s subscription pricing, not just the cost of the software and hardware. More often than not, the largest costs are hidden in the IT staff time that the systems absorb in the form of ongoing maintenance, upgrade projects, support and bug fixing, etc. Since these activities are taken on by the cloud vendor, they should be included to ensure an apples-to-apples comparison in the on-premise vs. cloud decision.

Reduce Operational Risk or at Least Stay Risk Neutral

Risk is easily the biggest barrier to cloud adoption for financial services applications. Financial risk, regulatory risk, security risk, performance risk…risk, risk, risk. The risk implications of cloud computing often appear so daunting that some financial services IT professionals would prefer to avoid the cloud entirely—which is a shame. To be sure, putting customer investment account information on the cloud is a non-starter. And, even something as simple as company email entails significant compliance risks. However, the one mistake most IT departments make when evaluating the cloud is to equate risk reduction with internal control. It is often the case that the focused expertise and scale of a cloud computing vendor provides significantly lower risk than an in-house system. This gut reaction can be compared to fear of flying: just because you’d feel safer if you were flying the plane, doesn’t mean you’d actually be safer. Therefore, it is important to evaluate the risks of cloud computing as objectively as possible. Does the vendor publish an SLA and public performance statistics? Who are its current customers and what do they say about the vendor? What are the vendor’s security and disaster recovery policies? What kind of support is available? All these questions should be asked of a cloud vendor. And, all of these questions should be asked of the comparable internal IT operation. If you’re not a pilot, you might just be better off taking a commercial flight.

Market Data: To Cloud or Not to Cloud?

There are two important aspects of market data systems to consider when evaluating the potential for moving them to the cloud. The first is to recognize that it is not the data that is being outsourced with cloud computing, but the data management infrastructure. In most cases, the data is already outsourced in the form of a traditional data feed, so the on-premise vs. cloud comparison actually centers around the internal software, hardware, databases, development and administrative IT staff required to maintain the internal data management infrastructure that shuffles the data from a feed to a final application. The second aspect is that unlike investment account data, market data is not about the company or its customers, and thereby sidesteps the number one barrier to cloud computing adoption in financial services: data security. But, it doesn’t make it a slam dunk. It is still important to carry out a full evaluation along the dimensions of competitive advantage focus, cost reduction and risk reduction. Does the data management system in question create competitive advantage or simply maintain competitive parity? Does the cloud offer significant cost reduction over the total cost of internal data management systems? And finally, does the cloud offer neutral to lower risk given the nature of the market data and the relative sophistication of the cloud vendor’s platform vis-a-vis on-premise operations.

As most members of the financial technology community ponder their cloud strategies, NASDAQ OMX, CME Group and BG Cantor are taking the lead with bold on-demand market data cloud initiatives.

NASDAQ OMX says it will provide on-demand Web services access to detailed historical Level 1 trade and quote data for all U.S.-listed securities. Tick data is increasingly used in quantitative environments for back testing of algorithmic trading strategies, but collecting and managing it can be onerous.

According to Randall Hopkins, NASDAQ OMX’s Senior Vice President of Global Data Products. “Today our customers spend a large amount on technology infrastructure, not the market data itself…we want to drastically cut data management costs by running the technology infrastructure on the cloud for our clients and delivering to them the data they need, when they need it, and how they need it.”

NASDAQ discusses it’s market data cloud initiative with Max Bowie
of Inside Market Data at the 2010 SIFMA Financial Services Technology Expo

CME Group plans to offer on-demand access to end-of-day OTC settlement, volume and open interest data to support markets available through CME ClearPort®, a set of OTC clearing services that provides neutral settlement prices across asset classes. CME ClearPort currently clears more than 500,000 contracts daily, including Credit Default Swaps (CDS), energy, metals, agricultural commodities and foreign currencies.

“With the ongoing development of our multiple OTC product and service offerings, and the need to deliver this data to customers with continued market transparency, the Xignite platform will be a quick and cost-effective way to get OTC pricing and reference data to our global market participants,” said Brian McElligott, Managing Director of Information Products at CME Group.

Pete Harris of the A-Team Group interviews CME Group, BG Cantor and Xignite.
The A-Team Group gave the cloud prime coverage in their recent Q2 issue,
and Pete is launching a new “Market Data Cloud” channel, so stay tuned to that.

BGCantor Market Data plans to use BGCantor On-Demand to provide an affordable solution to middle office asset managers and portfolio managers who require timely market data but do not require a full data feed. “Our customers depend on us to deliver the data they need, when they need it and using an innovative technology like web services we can provide even more flexibility,” said Bernie Weinstein, Executive Managing Director and head of BGCantor Market Data.

NASDAQ OMX, CME Group and BG Cantor have all partnered with Xignite to get their market data cloud initiatives off the ground (sorry, couldn’t resist).

Market data providers and IT professionals have tough jobs. Every day financial markets spew out huge fountains of data that must be captured, routed, scrubbed, reconciled, stored and redistributed with dizzying speed and accuracy. The diversity of data is staggering, from low-latency pricing data for algorithmic trading to intermittent corporate actions such as stock splits, and from globally dispersed real-time currency exchange rates to aggregated end-of-day VWAP and NAV calculations. Optimizing and tuning the market data systems that keep this crucial information flowing smoothly and cost effectively is no easy task. What, if anything, can cloud computing offer to ease the challenge?

This is the second post in a series 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 types of market data that naturally lend themselves to cloud computing (and those that do not) in order to identify the market data sweet spot for cloud computing.

First, it’s important to recognize that it is not the market data that is being outsourced to the cloud, but the on-premise market data management infrastructure. Therefore, the market data sweet spot for cloud computing is the intersection of data management systems that offer little competitive advantage, but are costly and difficult to maintain in-house. Both relative competitive advantage and relative level of difficulty will vary from firm to firm by business focus and IT capability respectively; however, there are aspects of the market data itself that can contribute significantly to the cost and complexity of maintaining an in-house data management system.

Hard to Use Market Data

If the market data comes in original feed formats that are not well suited to the particular use of the data in the final application, then considerable effort must be expended to make the market data application-ready. For example, a real-time streaming exchange feed is great for creating a stock ticker, but not so great when the goal is to analyze historical tick data or simply get an ad-hoc real-time quote for a single symbol, then there can be lots of programming involved to parse the feed, store the data, continuously refresh the database and create a data access layer that applications can easily utilize. Cloud computing is built on Web services that allow for multiple interfaces to the market data, so it is especially good at tailoring the data format to the specific needs of the application on the fly. For example, a Web service request can be for a single price, multiple prices, or simply for symbol validation against master data.

Hard to Maintain Market Data

If the market data in question is stored and refreshed often due to daily activity, such as historical time series and tick-by-tick data, then it can entail a complex update process that must be maintained and monitored. Quality testing must be put in place to ensure data quality and alerts to ensure that update processes run successfully to completion. As market data accumulates, regular backups, purges and capacity upgrades must be carried out to ensure efficient operation. Also, market data that is particularly complex, such as corporate actions, can consume significant resources scrubbing, mapping and updating the data even if the volume is not as heavy as price data.

On the other hand, market data that is infrequently updated in large batches that replace the entire data set may be easier to receive and maintain internally as a simple flat file. Similarly, market data that is streamed for continuous presentation and immediately discarded should be relatively easy to handle in-house.

Hard to Access Market Data

Technical and geographic barriers can conspire to make certain kinds of market data extremely difficult to access, let alone store and use. Getting access to market data that is not in high demand in your geographic location can be very difficult when local data providers do not support it, such as low volume market niches and new products, or market data that is created on the other side of the world. If data needs to be made available throughout a dispersed global organization, it can be quite costly to receive it centrally and build the necessary network and services infrastructure to distribute it globally to consuming applications. Cloud computing provides direct application access to market data over the Internet, so both the geographic and technical barriers to access are significantly reduced. Web services standards ensure that the technical hurdle is very low, so as long as sufficient Internet bandwidth is available, access is no more difficult than pointing a Web browser to a Web page.

The Cloud Computing Sour Spot for Market Data

If hard to access, hard to maintain, and hard to use define the market data cloud computing sweet spot, then what is the sour spot? When boiled down to its essence, all of the “hard to’s” above are made “easy to’s” by the Internet. So, the sour spot is found when the Internet has a significant negative impact on market data delivery. The three biggest limitations, in most likely order of importance are…

Too Risky

The market data is unique and proprietary with significant competitive, security or privacy concerns that preclude storing or distributing it using a public network, even with encryption.

Too Fast

The latency requirements of the data are very strict and have a low tolerance for variation. Typically less than about 50ms on average which is about the best that can be consistently expected from a high performance Internet connection. In addition, Internet latency can vary significantly depending on the number of network hops between the cloud provider and the consuming application. This number improves every year (back in 2000 we were talking seconds). But it is safe to say that latency requirements measured in microseconds or nanoseconds won’t be a good fit for the cloud.

Too Fat

The market data volume involved implies a network transfer time that exceeds the requirements of the consuming application given the best expected performance over the Internet. This may be alleviated by getting a direct network link to the cloud provider if the cost of the additional bandwidth is justified by the overall cost savings from outsourcing the relevant data management infrastructure. In some cases, this can also be resolved by moving the application itself to the same cloud computing infrastructure. However, an in-house application that requires frequent updates with large volumes of data can quickly exceed the best performance currently available over the Internet.

You can be sure cloud computing is making real inroads into the enterprise when Gartner weighs in. This week the Gartner Global IT Council, which boasts CIOs from some prominent financial services names like Dow Jones, American Life Insurance and International Finance Corporation as well as those from MIT and Anheuser-Busch, took on the challenge of industry standards for cloud services performance by publishing it’s list of Rights and Responsibilities for Cloud Computing Services.

While most major cloud providers do a very good job of implementing these tenets already, relying on the cloud provider’s good intentions is simply not an option for financial technology. Therefore, developing common practice and industry standards is essential for long term success of cloud computing in the financial services industry. This is a good first step.

Rights and Responsibilities for Cloud Computing Services

• The right to retain ownership, use and control one’s own data
• The right to service-level agreements that address liabilities, remediation and business outcomes
• The right to notification and choice about changes that affect the service consumers’ business processes
• The right to understand the technical limitations or requirements of the service up front
• The right to understand the legal requirements of jurisdictions in which the provider operates
• The right to know what security processes the provider follows
• The responsibility to understand and adhere to software license requirements

I must say though, I’m a little taken aback that there are six rights and only one responsibility on the list, and that one being a little strange. Ostensibly the responsibility requires both cloud consumers and providers to stay honest with respect to licenses for the third party software that they “put on the cloud.” This is clearly important in managing liability and risk of audit and copyright violation, but it is somewhat tangential to the core cloud consumer-provider relationship that the list addresses. Some more appropriate responsibilities might have been to adhere to the terms of signed service agreements and avoid the most common service violations, e.g., not exceeding usage levels by a factor of 100 during testing and whacking your cloud provider and not sharing user credentials under the table to siphon off unlicensed services.

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 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 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!”