This article was initially published in bobsguide.com

In Geoffrey A. Moore’s seminal book Crossing the Chasm he describes a five technology adopter lifecycles of innovators, early adopters, early majority, late majority and laggards. The “chasm” is the most difficult step of innovations transitioning between visionaries (early adopters) and pragmatists (early majority). In my experience at each of the cycles noted below, the transition over the chasm is where the most vocal resistance and calls of “hype” are usually heard. The posit for this article is that Cloud Computing is well into the early majority phase while “Big Data Analytics” is just now crossing the chasm in the Financial Services industry, and suggests how you might think about potential impacts and practical approaches.

As background, it may be interesting looking back at many of the technology innovation cycles the have impacted Financial Services over the last 30 plus years:

·       1980’s: The explosion of the Personal Computer, Ethernet, and introduction of car phones signaled the beginning of individual control of information and being location agnostic.

·       1990’s: Relational Databases (RDBMS), client-server architecture, electronic trading, flip phones and early adoption of the World Wide Web began changing core business and operating models and improved access to data.

·       2000’s: The early majority acceptance of the Internet as a business platform, complex event processing, Blackberries, laptops vs desktops, business intelligence tools, open source software and early adoption of Cloud Computing delivered unprecedented productivity improvements for people working literally anywhere in the world.

·       2010’s: The current cycle of mobile computing, social media, early adoption of Big Data Analytics and early/late majority acceptance of Cloud Computing/virtualization is changing the way that we interact with each other on a personal level as well as with our employees, employers, customers and business providers. The visionary Internet of Things (IoT) promises to change the way we interact with just about everything else.

Cloud Computing

Marc Benioff of Salesforce.com was the first person really beating the drum regarding the power of Cloud Computing. More than just hosting software as an ASP – often with a terminal emulator supporting fat client applications – the Cloud was, and is, all about functionality and interoperability with variable pricing and minimal IT support needed. In theory a business user can put down a credit card and have immediate access to a fully supported platform to support a specific business function. Should they need additional functionality, the user can access that quickly from the Cloud ecosystem of other providers to meet their specific needs. The reality is that there is usually some conversion and implementation needed, but Cloud computing makes running a business vastly easier that installing and integrating on-premises systems.

For example, PanoVista.co’s partners offer solutions such as data governance, global equity research and portfolio construction, all asset class performance attribution, and collateral management delivered via Amazon Web Services (AWS) and Microsoft Azure clouds. Clients benefit by being productive within days or weeks rather than months or years, and our partners can quickly develop new functionality and keep all clients current with drastically lower support costs.

Big Data Analytics

Big Data is a term that references the 3 V’s: Volume, Velocity and Variety. Whereas structured data in Financial Services often contains very large data sets and real time pricing and transaction data, it’s really the huge Volume and Variety of unstructured data that adds complexity that traditional relational databases cannot support. This is where new - often open source – Big Data technologies and techniques come into play. Hadoop, NoSQL, and text search are all technologies that can scale horizontally (using distributed servers) across internal and Cloud-based structured and unstructured data sources to feed analytical languages (such as R and Python) and business intelligence applications to bring new insights into business.

Big Data Analytics is an area that has not yet crossed the chasm from the visionaries to the pragmatists in the Financial Services industry.  For examples of how early adopters are using these technologies, visit www.panovista.co/blog.

Security

One common theme of all of the innovation cycles noted here is that as our access to information and productivity has steadily increased, so too have the information security challenges. The perception of many firms is that on premise data is more secure than either private or public Cloud providers. However, recent information breaches by companies either not keeping their applications current or not encrypting their data in place, along with the great strides made by Cloud providers, is starting to change that perception.

At the recent AWS Summit in New York in July, Nate Sammons, Principal Architect at NASDAQ, provided a case study of their data security and encryption approach. He noted that encryption slows down processing speed, about 25% in their case; this performance drag is one explanation of why many internal IT teams might not enable encryption. However, one advantage of AWS Elastic Compute Cloud (EC2) is that NASDAQ can dynamically scale up processing as needed to overcome the encryption overhead across the millions of daily trades processed, as well as to support the predictive analytics they perform using a variety of open-source technologies.

What does this mean to the Financial Services industry?

As the industry continues to change, Cloud Computing and advanced Big Data analytics will have a growing positive impact on streamlining existing and creating new business models. However, it is possible to start with smaller projects by looking for key decision points that may lead to incremental change. Some questions to ask yourself and your business include:

·       Can our existing technologies be moved to the Cloud for a positive ROI? For example, is a core platform overdue for an upgrade along with the supporting hardware and software, and is a Cloud option available? If the core platform is due for replacement, what is the true TCO (total cost of ownership) of competing on premise vs. Cloud offerings?

·       Are we competitive enough? Do we have control of our data or do we have gaps in data ownership and quality? Do our teams have the right tools to streamline and automate their daily work? Are we really getting -– and using -- the best information possible to manage our investments and make important business decisions? Are we using data visualization effectively and pro-actively, and is the underlying information feeding our Business Intelligence tools correct and validated?

·       Are we responsive enough? Can we uncover new opportunities by mining our own data? When new client or business opportunities arise, can we react fast enough or do our internal processes bog us down? Is our environment flexible enough to scale up and down as needed?

Of course, information security should be foremost in considering these questions and potential alternatives. A final note from the ASW Summit noted above is that many healthcare providers are adopting the Cloud – including the need to support HIPAA compliance.

In closing, the recommendation is to always think about how you can improve your business, either by making it more efficient, more accurate, more responsive and more competitive. You may find that Cloud Computing and potentially newer technologies will play a big part in achieving your goals.

Though not yet mainstream, we are seeing thought leading Financial Services firms using Big Data technologies and techniques in at least four main areas: Marketing, Security, Enterprise-wide Business Intelligence, and Investment Research.

Big Data is a term that references the 3 V’s: Volume, Velocity and Variety. Whereas structured data in Financial Services often contains very large data sets and real time pricing and transaction data, it’s really the huge Volume and Variety of unstructured data that adds complexity that traditional relational databases cannot support. This is where new - often open source – Big Data technologies and techniques come into play.

PanoVista.co recently conducted a short survey to Financial Services firms that indicates that while structured internal data management is maturing with active projects planned or underway, the state of unstructured internal and external data is less mature with fewer projects—even though there is an awareness that change is needed. So, what are the practical use cases for Big Data that thought leading firms are engaged in?

Marketing

In many ways, social media has been the biggest driver of the explosion of Big Data, and that is true for retail oriented Financial Services firms such as banks, insurance companies, mutual funds and wealth management firms. Marketers are creating highly segmented campaigns and using predictive analytics to nurture leads to increase conversion rates online and via social media optimization, or to pass “marketing qualified leads” to the sales team to confirm if the leads are “sales qualified leads” in their pipeline.

Security

The ability to search large volumes of data looking for inconsistencies has a natural benefit for evaluating system logs looking for security breaches or curious user behavior. Furthermore, the horizontal scalability of Big Data platforms can overcome the performance hit associated with database encryption, reducing potential financial and reputational risk of the theft of data left unencrypted in place, which has been a shockingly common occurrence over the last 24 months.

Enterprise-wide Business Intelligence

As noted above, Financial Services firms typically manage large data, and more advanced firms have centralized and put quality controls around their structured data. However, much of this data still resides in silos by business unit, and few firms have taken the step to fully integrate internal unstructured data. The Big Data approach is often called a Data Lake, leveraging Hadoop’s ability to read across structured RDBMS and unstructured data such as email, documents and video and treating all of the data as a common logical data platform. Business Intelligence and advanced data analytics can then be used to generate new insights into the inner workings of an organization to improve efficiency, reduce risks and provide better client servicing.

Investment Research

Top tier hedge funds, asset management firms and banks are the early adopters using Big Data in the Investment Research area. This is a little more controversial since most results today are anecdotal versus backed by formal research, however the anecdotes indicate that traders and portfolio managers are indeed gaining a competitive advantage by having broader insight into their investments, often before traditional data providers are aware of news or can update their feeds. Social media activity prompted the SEC to rule on April 2, 2013 that public companies can use social media outlets to disclose material non-public information. One area that is less controversial is that very large research databases have been proven to be managed and mined more effectively on Big Data platforms such as Hadoop and NoSQL than on traditional relational database platforms.

Considerations

Big Data is gaining rapid acceptance across most industries more quickly than in Financial Services. The technologies are evolving quickly, and often there is a mix of technologies that are needed for to support a given use case. These technologies are designed to scale horizontally across many commodity servers, so before investing in huge data farms it may be worth considering cloud deployments to support scaling only on-demand and to also facilitate mixing technologies as needed to support evolving use cases. As every data scientist will attest, knowing what questions to ask and plumbing the data are critical steps for good analytics. An Agile project approach is best suited to support the iteration that will likely be needed before locking down an operational process. Therefore, planning on the approach, tools, resources, and anticipated results and benefits should remain part of the business case, just as with traditional technologies.

As depicted in the Chart, above, the early results from our short survey to Financial Services firms show that structured internal data management is maturing with active projects planned or underway, while the state of unstructured internal and external data is less mature with fewer projects—even though there is an awareness that change is needed.  

The maturity of internal structured data is reflective of the growth of data management initiatives over the last ten years to control the cost of acquiring data and also meet the growing regulatory reporting mandates for data and investment transparency. Automating data governance is the final phase of the maturity curve, with many firms currently evaluating options in this space.

Big Data often references the 3 V’s: Volume, Velocity and Variety. Whereas Structured Data in Financial Services often contains very large data sets and real time pricing and transaction data, it’s really the huge Volume (such as log surveillance) and Variety of unstructured data that adds complexity to traditional relational databases and opportunities for those firms who master it.

Anecdotally, we have seen that only the “early adopter” top tier firms -- buy side, sell side and banks – have started to invest in Big Data projects to enhance their unstructured internal and unstructured external data, and that the majority of smaller firms are aware they need to do something yet have not begun or are not sure where to start. This is despite the often quoted saying that 80% of Enterprise data is unstructured, and the well-publicized rapid growth of social analytics.

Unstructured internal data often contains valuable business insight into investment research documents (street and internal), staff on-boarding, knowledge management and client communications. Thought leading firms in many industries are investing to harvest this internal information, yet it seems to be a lower priority for financial services firms, perhaps due to the current investments in structured data projects.

Unstructured data from the web and social media is where the 3 V’s of Big Data are gaining a lot of traction with early adopters within the Financial Services industry.

• Marketers are creating highly segmented campaigns and using predictive analytics to increase conversion rates online and via social media optimization. 
  
• Traders and portfolio managers are gaining broader insight into their investments, often before traditional data providers are aware of news or can update their feeds. 
  
• Social media activity prompted the SEC to rule on April 2, 2013 that companies can use social media outlets to disclose material non-public information. 
• The power of data science and predictive analytics working on vast data sets are being used to provide competitive advantage to investors.
• Very large research databases can be managed and mined more effectively on Big Data platforms such as Hadoop and NoSQL than on traditional relational platforms.
  

It’s rewarding to be involved in such an exciting time of rapidly changing technologies.

I was extremely honored to be invited to chair the Performance Measurement and Risk track at TSAM NYC 2015 last week. Kudos to the organizers, panelist and attendees, as it was a very informative, interactive and enjoyable experience. Here are some thoughts and observations from discussions at the event:

·       The Performance Measurement teams are one of the few groups in an investment management firm that interacts with every other department from front-to-back office, prospects and clients, and increasingly with regulators. They often catch errors (trade, pricing, reference data and accounting) before anyone else.

·       Performance returns are the ultimate in story telling: A company’s strategy, research, Portfolio Management decisions, trader’s execution, client’s mandate expectations vs. benchmarks are all fully exposed. Data Visualization can help tell that story. Daily controls are key to ensure its accuracy.

·       Most Performance teams now report into Operations, but there is a growing trend to report into Risk Management. Reporting lines have been in flux for years due to the first bullet point, above.

·       Communication remains one of the biggest hurdles in resolving issues and ensuring alignment, which is exacerbated by email, Instant Messenger and text. It’s better to walk over and talk to someone if you can, or use a video chat if you can’t. The exception is to use email to document expectations with teams and vendors.

·       From a project standpoint, many firms have difficulty keeping senior management involved and engaged in long term projects (more than six months). Agile project management can help alleviate that -- if done correctly -- by delivering iterative results. Most firms engage in Proof of Concepts (POCs) when selecting a new vendor in order to fully understand functionality and potential gaps, but agree POCs are better left for a final vendor or two if a bake-off is needed.

·       Fixed Income Attribution is more commonly used for client communications, yet many front office FIA tools don’t match the official returns, creating a data management and operational challenge for many. This is a bigger challenge for multi-asset managers. (Please note that one of our Partners can help resolve this issue.)

·       Cleared Swaps have not removed clearing risk, only transformed CCP’s into systemically important risk. Furthermore, the lack of cross boarder coordination is most evident in that Dodd-Frank prohibits the US from providing liquidity to the CCP’s, which will put the pressure on the Bank of England and ECB should a shock occur again. This transformation of risk is somewhat analogous to the creation of Credit Default Swaps after the LTCM crisis, a “credit risk reduction” vehicle intended to reduce risk with the unintended consequence of adding fuel to the financial crisis fire in 2008.

·       At a round table discussion at the end of the day, it was noted how regulatory-driven bank balance sheet restrictions are adding volatility in the most “risk free” market of all: US Treasury’s. Traditional Capital Markets desks – those that remain -- are doling out liquidity based on relationships in order to keep their balance sheets in line; the slack is being taken up to some extent by hedge funds, insurance companies and even corporate treasuries. Again, what are the unintended consequences going to be?

·       While this track had little discussion regarding Big Data, we had a number of very good conversations with people in the Data Management and Sales & Marketing tracks.

We are pleased to announce that Bob Leaper has been invited to Chair the Performance Measurement, Attribution and Investment Risk track at TSAM NYC on June 18, 2015. The goal of this track it for performance measurement professionals to deepen their network of peers in investment management. It is a truly neutral, unbiased platform, where the buy-side’s leading practitioners determine the agenda and deliver the program. TSAM itself is the leading event for developing efficient investment management companies, with additional tracks covering Data Management, Technology and Operations, Client Reporting and Communications, and Marketing & Sales communications. FInd out more at http://www.tsam.net/newyork.

We are please to announce that Bob Leaper has been invited to moderate a panel discussion at FTF's SecOps USA conference on June 1-2, 2015. The panel will be on IT Operations: Transitioning from Legacy to New Systems. The panelists will be Timothy Brodeur from Neuberger Berman, and Kristin Soule from Boston Advisors. The session will focus on the operational impact and lessons learned of adopting new technologies while keeping the business running. Find more information at http://www.ftfnews.com/event/sec-ops-2015/#.VVtTGPlViko.