The ability to capture that data and translate it into insight, can be just the edge a small business needs to go from fledgling startup to industry leader. With the right insights, small businesses can not just keep up with the competition, but surpass it. And with affordable and powerful business intelligence (BI) software, the playing field has never been more level.

Business intelligence tools can, for example, track products to see where they’re going, how they’re being used, and whether any inventory is slipping through the cracks. Metrics can be used to discover trends in the data, whether that’s noticing a shift in consumer demand before your competitors, or determine how even minor elements of website design increase or decrease conversions. The possibilities are virtually limitless.

What’s more, BI tools can both condense data and analyze multiple datasets from a wide range of angles, enabling small businesses to dig deeper and determine in which direction the causal arrows are flowing. Does, for instance, that regular delay in shipping stem from the shipping company itself, or is there a problem in the factory? This type of insight can be gleaned from big data via the right BI tool.

Upping the amount of data your organization tracks won’t be of any use if it’s impossible to understand the story that data tells, however – and it’s the surest way to get you confused. Business intelligence for small businesses can handle this problem by providing the kinds of customizable dashboards previously available only to companies with large IT staffs.

Dashboards help users condense only the data they are interested in, and to centralize it in one place for easy access and manipulation. What’s more, these types of software come with powerful reporting tools, allowing users to create visualizations that can help them not just better understand the data, but also to discuss that data in human terms when interacting across teams and with decision makers.

Getting Started

There are several aspects to consider to get the most bang for your buck from big data. Some of the things you should consider are:

Getting answers to these questions will help level-set your expectations and make rational decisions about your business.

With the right BI tools, big data is a great force for egalitarianism in the business world. Small businesses that learn how to get a better handle on big data and turn it into insight, will be the leanest, most agile and innovative businesses around.

Nat Venkataraman is a director of product management at LogiXML, a web-based business intelligence company. Nat.venkataraman@logixml.com or (703) 752-9700 x 7721.

Migration to the cloud is on the fast track. Enterprises are accelerating cloud initiatives to enable business innovation, realize long-term cost savings and drive increased profitability. Sixty-one percent of organiza­tions have at least one application or a portion of their computing infrastructure in the cloud, reports the CIO 2012 Cloud Computing Study. Within the next five years, almost one-third will have the majority of IT operations in the cloud.

Cloud computing represents a significant IT business shift from the traditional data-center business model. Now CIOs must manage a mix of multiple IT delivery models, including public, private and hybrid clouds as well as outsourced data centers.

With this shift come unprecedented management challenges. Building and deploying a cloud solution involves many complex variables—determining the best location for migrating applications and work­loads, integrating cloud solutions within your existing IT infrastructure and ensuring high-level security, all while accommodating the varying data center requirements of individual business units. There’s also the risk for increased complexity and higher costs when new cloud initiatives turn into isolated “cloud-in-a-corner” computing environments that are not integrated with existing systems.

The Business Challenge: Streamline Innovation

Business process innovation remains the focus of IT investment, according to the CIO 2012 Tech Poll. Findings show cloud computing is one of the top five technologies currently being implemented. Cloud-based service frees up resources so IT can focus on innovation rather than maintenance and addresses critical IT demands for additional bandwidth, greater automation, storage, flexibility and mobility.

As a strategic imperative, business priorities must drive data-center requirements. A government agency may need to understand its mission-critical applications and balance application workloads for greater efficiency and performance. Or an airline may seek to grow the business, reduce excess elastic data-center capacity requirements, improve services and reduce labor costs.

IT success hinges on responding to business initia­tives and priorities while delivering profitable IT growth. CIOs are expected to align IT with critical business objectives, maintain control, manage governance and compliance and reduce IT costs—to improve revenue-to-expense, reduce the service impact to the business and respond quickly to rapidly changing business requirements.

Imagine this scenario: Online banking services must be available to customers 24 x7 for transactions such as payments or account transfers. But security breaches or large amounts of traffic at particular times may create roadblocks, making online accounts inaccessible. To main­tain customer satisfaction, IT must access, monitor and manage a hybrid set of cloud and noncloud resources, wherever they are geographically located, so service can be restored immediately.

“CIOs are challenged to tie together the various cloud and noncloud delivery models with a single view so they can monitor system performance and proactively respond if service levels are out of compliance,” says Colin Lacey, vice president, Data Center Transformation Services & Solutions, Unisys.

The Hybrid Enterprise: A Strategic Roadmap

As the nature of the data center continues to evolve, it’s an ongoing struggle to manage traditional IT and private and public clouds, manage dynamic fluctuations of work­load capacity, and leverage existing services to create a multitenant services environment. Meanwhile, CIOs must maintain control, apply a consistent set of governance poli­cies and ensure transparency with financial and business reporting. It’s no easy feat to balance business priorities with capacity management and automation requirements.

“Looking forward, CIOs must see beyond the data center to their end-to-end delivery model and will need a consis­tent way to manage the total environment at the robust level that business units expect,” says Lacey.

What’s required is a unified, comprehensive view across both traditional data center and cloud delivery models to identify workloads; determine compute power, latency and memory requirements; and meet service-level agree­ment response time requirements. By using the right service delivery model, CIOs can determine the service impact to the business for improved capacity, throughput and efficiency—to optimize cost, compliance, security and performance while minimizing risk.

A hybrid enterprise represents a unique, integrated solu­tion to address CIO challenges and the complexities of multiple delivery models, as the foundation of a future-state data center environment. It strategically addresses the three core areas required to integrate and manage a future-state data center environment—applications, data centers and management, including:

The benefits: Greater business agility for faster time-to-market, reduced cap-ex with minimal up-front investment, and a single integrated management and application security environment.

“It is critically important that CIOs gain a deep perspective of end-to-end delivery model utilization and optimiza­tion over the next 12 to 48 months. Moving to this hybrid model is a key competitive advantage in the marketplace,” says Lacey.

The hybrid enterprise is a journey rather than a single product or technology. It requires a clear road map, begin­ning with an assessment of the organization’s business strategy, applications and infrastructure.

“Unisys examines the core technical aspects of a custom­er’s workload and the business attributes. We then blend them together with a blueprint for various model types,” says Lacey. “Our approach is to leverage existing customer investments so CIOs don’t need to rip and replace.”

Unisys can help CIOs achieve the agility and economic benefits the cloud promises despite all the challenges hybrid computing models pose. With the Unisys Hybrid Enterprise, organizations can build a strategy to reduce risk, increase efficiency and provide a future-state data-center environment that is borderless, virtual, automated, visible and secure. Unisys has a proven, enterprise-class experience in the data center and cloud solutions and can help customers build a hybrid enterprise that will seam­lessly integrate with existing IT management technologies, avoiding the cloud-in-the-corner approach.

To learn more, go to http://tools.en.idgresearch.com/cloudcomputing

CUSTOMER CASE STUDIES

FINANCIAL SERVICES: A large European bank needed to streamline, standardize and bridge the gap between meeting business goals and supporting IT resources. To improve customer service and market share, the IT challenge was to accelerate time-to-service while keeping IT management and server-provisioning costs low. By implementing a hybrid enterprise, the bank achieved greater agility and met compliance security requirements. New IT services were provisioned in hours instead of three to five weeks, while audit response was achieved in a few days instead of weeks.

TRANSPORTATION: An Asia-Pacific transportation firm needed to increase IT capacity to handle growing air travel transaction peaks and better support its customers. By implementing a hybrid enterprise, agility resources were matched to fluctuating business demands and transaction peaks created by large-scale events. Additional benefits included approximately $600k in cost savings and greater efficiency with 60 percent of server utilization and 80 percent of resource requests standardized.

Data Science is the practice of deriving insights from data to solve business problems. The current market wave is tied to the new world of big data, and Data Scientist is now touted as the sexiest job of the 21st century in the Harvard Business Review [1]. McKinsey has noted a 50-60% supply-demand gap for data scientists, with a shortage of more than 150,000 data scientists and 1.5 million managers with big data analytics understanding over the next 5 years [2].

The value of data science is undeniable, with core applications driving a wide array of business and customer intelligence programs including:

In addition, specialized data science programs drive industry vertical solutions, with industry-specific data sources that are expanding and evolving, e.g., to incorporate social networks and real-time data. In the Financial Services market, this plays out across all of the core functional areas including:

With the current excitement around big data and data science there are some solutions getting attention that are dangerous, e.g., promoting isolated, end-user, database analyses with all the attendant problems such as chasing noise, throwing out data that don’t match pre-conceptions, confusing leading and lagging indicators, interpreting correlations as causation, e.g., as described by Silver [3]. Some new companies are even making bold claims that they invented visual analytics, that data scientists will be dead in 18 months; or that we don’t need data scientists, just easier access to big data. This type of thinking introduces substantive risk – not only of heading in entirely the wrong direction, but with significant negative ramifications, like bringing down businesses, operations or financial systems. The end-user community can be readily enabled with self-service analytics (as outlined below), but there needs to be inbuilt guidance, and a framework of end-user data discovery, collaboration and enterprise readiness that promotes rigorous and real analysis on the business.

I outlined the basic Data Science 1.0 process in a 2012 Forbes article, “What is a Data Scientist” [4], and in notes to the European Banking Forum [5] and WSTA [6] communities. With recent technology innovations [7, 8], we have now jumped beyond this to what I believe to be a new Data Science 2.0 state. The expanded workflow in this new state includes:

This workflow is illustrated in Figure 1 below for a typical business insight use case in financial services.

Figure 1.

In the Financial Services market, our businesses are evolving rapidly and we are working hard to be nimble and intelligent with our information solutions. This combination of interactive, visual, descriptive and predictive analytics; with self-service guided and collaborative workflows for the masses, and in-line deployment in real-time event systems, is the future. This is Data Science 2.0.

References:

[1] Davenport, T. and Patil, D.J. (2012). Data Scientist: The Sexiest Job of the 21st Century, http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1.

[2] McKinsey Global Institute (2011). Big Data: The next frontier for innovation, competition and productivity.

http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation

[3] Silver, N. (2012). The signal and the noise (chapter 6 and throughout). Penguin Press.

[4] Woods, D. (2012). What is a Data Scientist?: Michael O’Connell of TIBCO Spotfire.

http://www.forbes.com/sites/danwoods/2012/01/25/what-is-a-data-scientist-michael-oconnell-of-tibco-spotfire/

[5] O’Connell, M. (2012). Big Data Analytics. European Banking Forum

http://www.spgmediadesign.com/arena/ebf/newsletters/2012campaign/2012-10/tibco.html

[6] O’Connell, M. (2012). Big Data Analytics: Scaling Up and Out in the Event-Enabled Enterprise. Wall Street Technology Association, Ticker 2012, Issue 3.

[7] TIBCO Spotfire (2012). Spotfire 5. http://spotfire.tibco.com/en/spotfire-5.aspx

[8] The Forrester WaveTM: Big data Predictive Analytics Solutions, Q1, 2013

http://www.forrester.com/The+Forrester+Wave+Big+Data+Predictive+Analytics+Solutions+Q1+2013/fulltext/-/E-RES85601

Michael O’Connell, PhD, Industry Analytics, TIBCO Spotfire (http://about.me.com/moconnell). Please visit http://spotfire.tibco.com for more information.

But for the financial-sector, international laws and regulations make life extremely challenging.

Behind the scenes, there are heavyweight struggles taking place that center around the “sovereignty” of data. If data are stored across international borders, how can your customers be sure that their sensitive personal information is safe? More importantly—at least from the lawyers’ perspective—who can be sued if it isn’t safe?

For organizations in the financial sector, the picture can be murky.

Safe Harbor and BCR Found Wanting?

The last disruption of financial service delivery on this scale took place in the 1990s, when the Internet became a tool for international business. In order to deal with the European Union’s stringent data protection laws, many U.S. businesses entered into Safe Harbor Agreements.

These gave the legal protection necessary when processing personal data belonging to EU citizens and are prerequisites when providing Web-based services to the European market.

However, these agreements were only available to businesses covered by the Federal Trade Commission or the Department of Transportation. Banks and other financial institutions don’t fall under these jurisdictions, so this mechanism was denied to them.

All that changed when the European Union introduced Binding Corporate Rules (BCRs). These agreements allow financial institutions to enter into contractual arrangements binding them to the safe processing of EU citizens’ data.

Frustratingly, these instruments were drawn up at a time when computing power was owned and managed by the corporation using it, so neither Safe Harbor nor BCR agreements will stand up to a fully distributed cloud. This leaves American businesses exposed to legal difficulties.

Kristen J. Matthews, head of the privacy and data security group at law firm Proskauer Rose, explains:

The use of Binding Corporate Rules...may be insufficient because, in cloud computing, personal data will be transferred outside of the [group] bound by the corporate rules. ...the very qualities of cloud computing that make it so intriguing and useful as an alternative to standard computing configurations are also the same aspects that raise...concerns. Given the enormous potential and benefits of...the cloud, it seems that, once again, the law needs to catch up to technology.

Lawyers and legislators are pressing to bring international law in line with the realities of cloud computing. For example, Microsoft’s General Counsel, Brad Smith, has for some time been lobbying the EU to harmonize data retention requirements and further extend the flexibility that allows for international processing of EU data. He’s also been lobbying Congress at home to bring privacy and trade laws into line with today’s technology. As he writes in a guest blog:

Congress can and should take thoughtful, constructive action to advance cloud computing. The country needs a ‘Cloud Computing Advancement Act’ to enhance privacy and security protections in the cloud computing era and foster the development of the cloud. ... Cloud computing offers many potential benefits, but those benefits will not be realized unless...questions are settled about how data is governed when it crosses national borders. Quite simply, we need responsible action now.

For those of us who remember the 1990s, this all has a familiar ring to it: First the engineers, then the lawyers—that’s the way technology advances. The financial sector needs to make sure its voice is heard while the law evolves, so that it too can compete in the cloud.

______ Dr. Emma Byrne writes for a wide range of publications in the fields of science, technology and current events, including the Financial Times, Forbes, and Global Business Magazine. She won a British Science Association Media Fellowship in 2008 and the British Computing Society's Machine Intelligence Competition in 2006.

For more about cloud computing, big data, and other aspects of “tomorrow’s business,” visit our blog on Forbes.com.

Numerous studies have shown that untethered employees are actually more connected to the enterprise than deskbound office workers, thus providing a strong incentive for the enterprise to integrate mobile devices into its infrastructure. However, this mobile connectivity comes with financial and security risks. An informed mobile strategy must take advantage of the convergence of the enterprise and consumer domains to enable a cost-effective and risk-managed mobile workforce.

A significant challenge for many firms is the cost and user-preference constraints of this highly consumerized trend. Because of such constraints, ”employee-liable” models – those that allow employees to use personal devices for corporate use – are increasingly being adopted to enable enterprises to deliver mobile services to a larger population at a reduced cost. To address this development, companies must build a smart strategy that takes into account the following:

“Bring your own device” (BYOD) is just the beginning of the “bring your own technology” (BYOT) trend. This is an expansion on the bring your own device model (e.g., laptop, smartphone and tablets), which includes apps that people download on their own to improve their productivity (e.g., contact management, PDF annotation, tablet word processing, tablet-specific presentation software like Prezi that are not specifically supplied by or supported by IT.

The path to successful adoption is hampered by a number of obstacles, and financial services companies should be mindful of the pitfalls when establishing their own smart mobile strategy.

Security and Regulatory Risks

“Ownership” is now a key dimension guiding policy making. As a result, there will be different sets of policies for security, privacy and application distribution of personal and corporate devices. Companies must keep security and regulatory compliance in mind when designing a BYOD strategy. According to Ernst & Young’s Global Information Security Survey [2], 52% of respondents have implemented policy adjustments and 40% have invested in awareness programs. But organizations recognize the need to do more. For instance, encryption techniques are used by fewer than half (40%) of the organizations. Greater deployment of encryption is an important strategic direction.

Devices connecting to the network must support the basic criteria set by IT to meet security requirements and policies. The organization must have some degree of control over the device in the event it is compromised. There are a number of technologies that work in concert to enable policies and reduce the risk associated with mobile computing. For example, mobile device and application management software helps organizations monitor devices connected to the enterprise and handle issues when they arise, such as patches, identification of “jailbroken” devices and remote wipe.

Compromised devices could allow for access to protected data, the transfer of malware into the enterprise environment and/or even the physical tracking of employees and executives. In May of 2012, hacked websites were used to spread malware on Android-based mobile devices. The threat was not deemed significant; however, it later came to light that a device infected with this malware could potentially be used to gain access to normally protected systems within the enterprise. [3]

Overall user experience and use-case design

Technology’s principal goals are to drive and deliver business value. While locking down mobile devices and prohibiting the use of personal devices may reduce security risks, policies that are too restrictive or too vague will drive down adoption of mobile devices or applications and could potentially hinder growth and innovation. In time, they may also drive employees to use unsafe channels and means to obtain the flexibility and access to which they have become accustomed. In such instances, neither the policy nor the program will be sustainable.

When it comes to mobile devices, one size does not fit all. Smart programs should be based upon an understanding of different user types and a clearly defined set of user segments. For example, multinational firms should consider the impact of device availability, usage habits and regional provider capabilities. A clearly articulated policy should drive the development of the user’s overall digital experience interacting with the mobile device or application, as a poor user experience will lead to failure. Ultimately, understanding the users and how the technology can enable their daily tasks will drive user satisfaction.

Technical strategy

The technical landscape for mobile devices is rapidly evolving. Buzzwords abound, and it’s hard to keep up with the latest trends, like mobile device management, mobile application management, dual identity smartphones and context aware applications. Those that lead in this space realize that mobile technology, while unique, is just another channel. Companies must be flexible when it comes to executing a strategy because the reality is that certain elements will change, probably more than once. They must also be mindful that in this quickly evolving space, some of the technology is unproven when it comes to scalability and reliability. Understanding the use case will help prevent the common pitfall of over-architecting solutions that users simply don’t want. Additionally, the inherent risk profile for mobile computing is high, so regulatory and risk-related issues must be covered by comprehensive policies.

The response to this trend will be as varied as mobile devices themselves. Some organizations are creating mobile teams to support the enterprise while others are moving to a mobile-first development methodology. Much of the tactical strategy depends on the solutions you are working to provide to your end users. The complexity of the solution and your user base are key factors as well. Managing a vast array of employee-owned devices can become burdensome if you have not designed a smart, policy-based solution. Many companies are now abandoning native development, at a high cost, after realizing the challenge of supporting multiple versions of various operating systems. There are a number of third-party mobile development solutions in the marketplace, now proven at scale, that address this issue.

Designing for the future

As technology advances at an exponential rate, the way we live, work, consume and communicate will continue to change, and rapidly. New devices are introduced to the market every three to six months, and there is no way to predict what will hold favor in even a year’s time. Building a smart, flexible and policy-based mobile strategy will allow financial services companies to explore innovative ways to empower their workforce and drive greater productivity, regardless of what’s in vogue.

Brad Wallace is an Executive Director and Bob Reinhold is a Principal in the Financial Services Office of Ernst & Young LLP. Brad can be reached at +1 704 338 0561 or brad.wallace@ey.com and Bob can be reached at +1 703 747 1967 or bob.reinhold@ey.com.

The views expressed herein are those of the author and do not necessarily reflect the views of Ernst & Young LLP.

[1] “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011-2016,” 14 February 2012, ©2012 Cisco.

[2] Fighting to close the gap: Ernst & Young’s 2012 Global Information Security Survey, Ernst & Young, 2012.

[3] http://gcn.com/Articles/2012/05/03/Android-malware-designed-for-network-access.aspx?Page=1

Today, there is a lack of industry-recognized benchmarks that demonstrate “high-performance” characteristics. To achieve performance, trade infrastructure implementation teams need to source the best available components from innovative specialist vendors, then integrate and tune them for optimal interoperability. This requires a combination of knowledge, skills, experience and deployment ability that is challenging for many firms. The process can be time consuming and exhaustive, not to mention expensive in terms of resource and capital expense implications.

In early 2012 a group of vendors tested FIX (Financial Information eXchange) engines as the start of an initiative to improve the availability of performance data on the characteristics of trading technologies. FIX, as the de-facto standard protocol for electronic communication across the trade lifecycle, was a natural starting point. FIX messaging can be a source of significant latency and jitter (a.k.a. inconsistent performance) which can adversely impact the success of trading strategies.

The testing program, undertaken by a community of hardware and software vendors, investigated the following assertions:

1. Using commercial FIX engines achieves lower latency and less jitter.

2. Using specialist low-latency network techniques has a significant impact on latency.

A collaborative approach by a community of IT vendors focused on the development of high performance infrastructure designs specifically for financial trading systems. A major benefit from this collaboration was the creation of a robust and representative engineering lab testing environment which was able to produce results simulating real-life conditions and their effect on the key function of FIX message transmission.

Three commercial FIX engines, from Rapid Addition, OnixS and B2BITS Epam, built on both C++ and Java architectures, were tested against an open source (QuickFIX) capability used as the null hypothesis. The commercial engines were 4 - 16 times faster (depending on load) than the open source equivalents, with an average latency of 11 microseconds across the measured leg, as opposed to 180 microseconds on open source options. This was even more evident when the match rate of the execution venue was increased. The stress that this exerted on the FIX engine drew out different performance characteristics in latency and jitter. The Opensource FIX engine failed when put under high volume stress - the 2 commercial engines showed different response characteristics, indicating that depending on the firm's trading strategy, this can affect the choice of functional components.

This lab based testing initiative has matured during 2012. The original group expanded into a community of collaborating vendors known today as the Finteligent Trading Technology Community (FTTC). Today, this community is focused on the expansion from straightforward FIX testing to functions across the trade lifecycle. The goal of this initiative is to establish a roadmap of intelligence that will assist in the procurement of solutions across the industry. For more information regarding the FTTC, visit: www.finteligent.net.

Jack Schwartz, Vice President of Sales, OnX Enterprise Solutions, 212-631-4700; email: jack.schwartz@onx.com; web: www.onx.com/High-Performance-Trading/

Nigel Woodward, Business Development Director, Financial Services, OnX Enterprise Solutions, 44 1892 724595; email: nigel.woodward@onx.com; web: www.onx.com/High-Performance-Trading/

A growing number of innovative companies are abandoning on-premise ERP in favor of cloud-based solutions accessible through any web browser, from anywhere. But not all hosted software offerings marketed as “cloud” are true cloud solutions. Many on-premise vendors are trying to pull the cloud over your eyes by “cloud-washing” their applications. It is incumbent upon you to do the due diligence and make the right choice.

So how can you tell a true cloud solution from a fake one? And, more importantly, why should you care?

True Cloud Solutions

True cloud vendors design their solutions from the cloud up. Purpose-built for the cloud and running on hundreds of servers at a secure data center with multiple levels of data redundancy, true cloud solutions offer greater performance, scalability and reliability and are managed by dedicated teams with expertise in cloud computing.

A key component of a true cloud solution is multi-tenancy—in other words, all customers access the same solution. This gives customers continuous and instantaneous access to the latest product upgrades, while the time-consuming upgrade process is handled by the vendor.

Customization is another differentiator of a true cloud solution. The best cloud vendors provide a flexible platform that allows you to not only customize your application on their platform, but also ensures that all of your customizations continue to work when a new product enhancement is rolled out by the vendor. This means you get the latest functionality immediately and don’t need to constantly re-implement your customizations and integrations every time a new product version is released.

The Fake Cloud

ERP/financial systems designed to run on-premise but billed as running in the “cloud” have distinct disadvantages compared to true cloud solutions. In most cases, they operate in a way similar to the application service providers (ASPs) of 20 years ago. By subscribing to a “fake cloud” on-premise ERP application that’s simply hosted at another company (as compared to a "true" cloud solution, which supports hundreds and even thousands of users via a multi-tenant shared infrastructure), you experience subpar efficiency, performance and reliability, as well as:

· Costly and time-consuming implementation

· Delayed and painful product upgrades

· Expensive, unstable integrations and customizations

· Need to spend money up front (not a pay-as-you-go model)

Conclusion

Fake cloud providers are trying to blur the lines between true cloud systems and hosted offerings. Yet the advantages they cite for their own systems are those touted by on-premise vendors. In fact, a “fake cloud” solution is a wolf in cloud’s clothing.

When it comes to speed of upgrades and deployment, performance, reliability, value, customization and service, it is simply not much of a comparison between a true cloud solution and the imposters.

Vishrut Parikh, Director, Product Marketing at NetSuite, email: vparikh@netsuite.com; web:www.netsuite.com.

Brilliantly simple unified communications technology helps financial institutions like First Security Bank satisfy customers and strengthen their bottom line.

Retail banks and stores may support different industries, but they have traits in common just the same. Chief among them is the importance of great customer service.

That’s why First Security Bank, a Searcy, AR-based financial institution with 70 branches and some $4.2 billion in assets, views telephony as a strategic technology. “The tools we use to communicate with customers are crucial to our success,” said Brody Walker, the company’s vice president and IT manager.

Until recently, however, those tools were costly and ineffective. “We were running on old, expensive, and outdated phone technology,” Walker says. Several technologies, actually, as each branch had its own hardware, voice service, and installation partner. “It was very costly to the bank as a whole, and highly inefficient,” Walker added.

Eager to standardize on a modern, centralized communications platform, Walker began searching for a sophisticated, all-in-one offering that wouldn’t overwhelm his busy technical team. “We have a small IT staff,” he says, “so it was imperative that the new solution be not only easy to manage but also easy to install.”

These are qualities most banks seek in unified communications (UC) systems, according to Bernard Gutnick, senior director of product marketing at UC vendor ShoreTel. “Banks often have large numbers of widely dispersed locations with no onsite technical support,” he observes. “They need communications solutions that are simple enough for overworked IT professionals to deploy rapidly and administer centrally, or anywhere else for that matter.”

This describes ShoreTel’s UC solution perfectly, Gutnick continues. Built from the ground up for simplicity of use and installation, the system also features an intuitive, unified management interface that makes administering even a sprawling network of branch sites a point-and-click affair. “You can basically manage thousands of users in hundreds of locations from a single console,” Gutnick says.

“ShoreTel provided a simple solution that’s very robust but easy to maintain, configure, and support.”

That’s a statement none of the other vendors Walker evaluated could make. “We tested three VoIP vendors and hands down, the winner was ShoreTel,” he says. It’s also easy to deploy, Walker adds. In fact, after watching a solution provider perform the first rollout, First Security completed all of the subsequent ones itself.

Branch employees love the new system, Walker reports. “Our employees are finding the ShoreTel system very simple and effective for their needs,” he says. As a result, First Security is providing better service than ever. “When customers call, the ShoreTel system makes it very easy to find the right person the first time,” he notes. Telecom spending is way down, too. “For the branches we’ve completed, we’ve seen savings upwards of 80 percent,” Walker says.

To date, First Security has upgraded 34 branches. Not surprisingly, managers at the other 36 are clamoring to be next in line. “Obviously our goal is eventually to have every site on ShoreTel,” Walker says. That way the entire company will be equipped to realize the customer satisfaction that every retail institution—including retail banks—strives to achieve.

For more information, contact ShoreTel, 877-807-4673, email: info@shoretel.com; web: www.shoretel.com.

Consumerization

“Consumerization” means that business customers have begun to expect the same ease of information access in their professional lives as in their personal lives – where they use apps for everything from tracking REM sleep cycles to hard-boiling an egg.

Consequently, financial institutions must offer highly competitive products and ever more sophisticated digital (read mobile) services. This encompasses several facets, including analytical and reporting capabilities, which can deliver dramatically improved insight into a client’s finances, better meeting heightened expectations.

The benefits of utilizing new, easier to use BI (business intelligence) solutions are not restricted to clients. Employees performing transaction evaluation on any level, including for risk compliance, can leverage these tools to work with data in intuitive, customizable drag-and-drop dashboards. This means more employees can have ROI-improving insights and make proactive recommendations – analysis is no longer the rarified realm of the data analyst. (Of note, the appropriate BI solution must be capable of scaling to support the increased number of employees using it simultaneously.)

By using the features of a dynamic and scalable business analysis solution, institutions can increase revenue and customer loyalty, operational efficiencies and effective decision-making (thus mitigating or avoiding risks) whether delivering information in print, to the desktop or to touch devices.

Big Data (What to Do With It)

The trend toward Consumerization comes into its own as financial institutions unlock and exploit ever-increasing stores of machine, transactional and behavioral data. Choosing the most compelling data visualization tools becomes even more critical.

The goal of managing Big Data is, first, to organize all the institution’s sources of data, no matter how large or which format, into an easily “mineable” architecture such as Hadoop. Next, institutions must perform initial data reduction or data mining via context-specific analytic tools* to obtain a manageable set of relevant data (Stage 1: Organizing/Analyzing). See ZDNet article: “Top 10 categories for Big Data Sources and Mining Technologies.”

Then, leading organizations will put in place the right tools to display trends and flags from band activity, via intuitive, customizable visualizations. In order for visualizations to produce insights, all data must be accessible for integration, including data from paper and digital archives, social network behavior and consumer influencers, columnar data, data warehouses, in-house intelligence programs, cloud data, all other tracked Internet-generated data, etc. (Stage 2: Visualizing).

Finally, the insights gained from these visualizations must be infused into decision processes in the organization at strategic and tactical levels. Because these insights will evolve with every person who participates in the process, the chosen platform must be able to adapt on the fly. (Stage 3: Operationalizing).

The result of managing Big Data this way is the ability to make habitual “more intelligent use of intelligence."

Whether an institution starts by addressing Consumerization or Big Data, evidence suggests that the institution delivering the consistently best investment performance, the soundest qualitative advice, and the most exceptional customer “experience” will win. Never was that more true than in today’s digitally driven world.

*Context-specific analytic tools include those made for predictive analytics, sentiment analysis, log activity analysis or data stream analysis.

Jeff Morris, Vice President Product Marketing Actuate, 650-645-3522; email: jmorris@actuate.com; web: www.actuate.com.

Algorithmic trading has long been used by buy-side institutions and proprietary trading desks to slice up large orders to minimize price impacts or for arbitraging between competing market places. However with the advent of regulations such as RegNMS (Regulation National Market System) in the US and MiFID (Markets in Financial Instruments Directive) in Europe, together with advances in ultra-low-latency networks and trading platforms, algorithms have been developed in order to gain a slight but crucial time advantage over other market participants. They effectively see the market ahead of other players. These time advantages are now measured in tens of nanoseconds (1ns = 1 billionth of a second).

HFT firms account for 56% of equity trades in the US and 38% in Europe by value (Tabb Group – 2010).

Types of Algorithms

Algorithms broadly fall into the main categories detailed below:

Latency

Latency is the time delay between two events, and in the context of HFT this could be the time delay between discovering the best bid/offer for a particular market, calculating the opportunity, submitting the order and finally receiving an order execution acknowledgement back.

Every link in the chain adds latency to the overall system, so it’s very important to understand where it is introduced and how it can be monitored.

Detailed here are some major areas of interest where latency will need to be measured and understood:

» Network/hardware Level: Physical layer latency measurement and timestamp generation; latest advances in ASIC’s (Application Specific Integrated Circuits) and 10 GBit Ethernet

» Packet Level: Latency of packets carrying order / execution data can be measured to monitor the quality of service (QoS)

» Protocol Level: Latency can be measured between a protocol request and response, e.g., between a TCP data packet and a corresponding TCP Ack (Acknowledge) packet

» Application Level: Latency can be measured for an application, e.g., from the exchange perspective - for the matching engine: from an incoming order to the outgoing execution message

» Transaction Level: Latency can be measured for a transaction that might involve the exchange of several frames and multiple sub-systems, e.g., for measurement of an outgoing order to the order appearing on the market data feed

Throughput

Another important consideration when testing HFT systems is order throughput. This is the frequency of orders or messages for a given time period and can affect latency, as a higher message rate will invariably increase latency at some point. With a high order rate, the impact of a software bug can be magnified with disastrous consequences, as has recently been experienced by a leading market maker.

Data rates are currently growing at around 40% per year depending on the exchange / platform.

So far in 2012 the Options Price Reporting Authority (OPRA) volume peaked at 4.1 mps and OPRA has projected that its ceiling for January 2013 could be raised to11.8 mps.

Protocols

Protocols are evolving to provide faster message turnaround times. The FIX FAST protocol (FIX adapted for streaming) was specifically designed for optimizing data transmission and is used to support high-throughput, low latency data communications between financial institutions. The major exchanges have also developed their own faster transaction protocols and advances have been made to increase throughput and reduce latency at the middleware messaging layer.

Challenge Recommendations

With increasing complexity, advanced technologies, ever-increasing HFT volumes, reducing latency and a changing regulatory environment come many challenges. Thorough testing becomes more and more critical as firms and the regulatory agencies seek to reduce risk and ensure the stability of the financial system.

To meet these challenges a robust testing program is needed. HFT systems tests should include the following main activities:

White Box testing

White box testing focuses on the internal workings of a system as opposed to its overall functionality (black box testing).

It is critical that when hardware and software are being pushed to maximum performance, testing techniques are appropriate for the applications.

High-frequency trading is highly sensitive to processing speed and accuracy. Concurrency issues are common and call for a repeatable testing procedure. Most of the bugs in this area are characterized as "Heisenbugs" named after their uncertainty and intermittent occurrence. Tools and techniques need to address effective discovery of such bugs in a reproducible manner.

Hardware / OS Certification / Tuning

Continuous advances in hardware and operating systems together with the release of numerous patches require platform certifications as well as testing of modified tuning parameters of middleware messaging and trading protocols. It is critical that this testing be performed to ensure a stable platform on which to perform application-level testing.

Functional testing

Functional testing ensures that HFT algorithms and trading applications are performing correctly in normal as well as adverse market conditions. Some techniques to be considered are:

» Functional scenarios in varying market conditions to keep up with changing markets, rules and regulations

» Regression testing to ensure continued functionality of new software releases, patches, hardware, OS, protocol updates, etc.

» Algorithm functionality needs to be tested at specifically identified price transitions and timing thresholds

» Comprehensive protocol testing

» Identification of possible runaway modes and unstable feedback situations

» Statistical comparison against live data

Performance Testing

HFT algorithms need to react very quickly to changing market conditions where latency can mean the difference between profit and loss. Not only does latency need to be measured for all components of the system, but this needs to be measured under high throughput rates as well. It is also desirable to implement continuous latency monitoring technology. Note that trading venues now also publish their latencies in real-time, or close to real-time via a data feed.

Technical / DR Testing

To ensure correct functionality of critical HFT infrastructure, technical testing needs to be factored into the testing plan. This includes measuring memory usage, critical queue length / growth, CPU utilization, process / component failover and primary / secondary system synchronization.

Conclusion

As HFT systems and algorithmic trading increase in usage and complexity and order volumes continue to increase with regulations struggling to keep up with this fast changing landscape, thorough and accurate testing becomes more and more critical, not only to ensure profitability, but to build stability and confidence in the financial markets as a whole.

For further information please contact Stephen Massel, Head of Solutions Engineering – Independent Testing Services (ITS), CSC at 646-216-9589 or smassel@csc.com.

About CSC

CSC is a global leader in providing technology-enabled business solutions and services. Headquartered in Falls Church, Va., CSC has approximately 95,000 employees and reported revenue of $15.7 billion for the 12 months ended September 28, 2012. For more information, visit the company's website at www.csc.com.