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Big Data in the Fund Industry: From Descriptive to Prescriptive Data Analytics

NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:

The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.

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Big Data in the Fund Industry: From Descriptive to Prescriptive Data Analytics

  1. 1. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics NICSA Technology and Innovation Committee January 2014
  2. 2. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Big Data in the Fund Industry Big Data is big news in the fund industry. Every industry publication seems to include an article on how Big Data is being used to increase marketing effectiveness, and every industry conference seems to have a panel focused on the operational support it requires. While the media has plunged into Big Data, the industry’s implementation of data analytics has been much more deliberate. The fund industry has never really been a data-driven industry, at least not when it comes to customer interactions. In fact, a recent survey by industry consulting firm kasina found that fewer than half of firms in the industry rely on customer knowledge to develop the corporate strategy for distribution, product development, reputation management and customer care.* As a result, Big Data has represented a significant change in mode of thinking for many firms. However, NICSA’s Technology Committee believes that continued cost pressures will push more firms toward data-driven decision-making. In other words, data analytics will be increasingly used as a prescriptive tool. This white paper summarizes what the Committee sees as the “state of play” of Big Data in the fund industry today. It has 4 sections which will review: 1. The history of the concept of Big Data. 2. The definition of Big Data in the context of industry applications. 3. The movement toward prescriptive analytics driving future decision-making in the fund industry. 4. Common misconceptions about the use of predictive analytics. For those who’d like to delve into Big Data in more detail, the Appendix contains the Committee’s recommendations for further reading. * The kasina study referenced throughout this white paper is by Julia Binder. Digital, Data-Driven, Differentiated: The Future of Marketing for Asset Managers and Insurers. kasina. 2012 2
  3. 3. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics A Little History of Big Data Big Data seems like a new-fangled invention, the ultimate product of the Internet era. Certainly, the terms “Big Data” and “data analytics” weren’t much used before 2011, as this trend chart of Google search data shows. Big Data search term Data Analytics search term 2005 2007 2009 2011 2013 Of course, people have been keeping and counting records since the dawn of civilization, and many activities predating the computer involved lots of data – just look at the United States Census. However, while the advent of computers in the 1950s and 1960s dramatically speeded up our processing of those records, our basic idea of what data essentially is – what it consists of and how it should be analyzed – has remained remarkably stable. It took the Internet to change our very concept of data. The worldwide Web itself was the product of a massive investment in technology in the late 1990s and early 2000s that created the platform for a new kind of business conducted electronically. Suddenly, consumers were able to shop from the comfort of their desk – whether at home, school, hotel or office. In addition to convenience, this e-business, as it came to be known, offered unparalleled transparency. Shoppers online didn’t just learn about prices and product features. They could check inventory levels, track the status of their order, find out when it was going to be delivered and read reviews of products (and write them, too!) Even more amazingly, this information was updated in something close to real time, rather than in a “batch” process overnight. At the same time, businesses were worried about the potentially catastrophic effect of the Y2K problem. In a worst case scenario, this “millennium bug,” as it was dubbed in the media, would cause computers around the world to crash because they couldn’t distinguish the year 2000 from the year 1900. To avert it, many companies embarked on massive systems upgrade projects. The combined investment in e-commerce and Y2K focused attention on data – while also dramatically expanding the quantity of information that could be captured. And this information wasn’t just standard numerical data – it was words, music and pictures as well. 3
  4. 4. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics In February 2001, Doug Laney, an analyst for META Group (later acquired by Gartner,) soon brought definition to this effusion of data. In his whitepaper “3D Data Management,” Laney argued that businesses needed to think of data as having multiple dimensions. He proposed three: volume, velocity and variety. WEEK of Feb 5, 2001: NASDAQComposite Even though he was writing in the midst of the bursting of the Internet bubble (as the NASDAQ chart above illustrates,) Laney’s concept captured the attention of the information technology community. They ultimately gave this multifaceted river of information the name “Big Data” – and suggested additional dimensions to add to the original three. The one additional dimension that has become most broadly accepted is “veracity.” 4
  5. 5. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics The 4 Vs in the Fund Industry Together, the 4 Vs of volume, velocity, variety and veracity make up the most widely-used definition of Big Data. Let’s take a closer look at each of these four dimensions and how they apply to the fund industry. Volume The first dimension of Big Data is the most obvious: its sheer volume. Data used to be collected judiciously – at certain times (maybe at point of sale) and by specified means (often through employee data entry). Today, Big Data is created 24x7 and through channels like social media that are often outside a firm’s control. Data is also being retained for longer periods given the low costs of storage. Thumb drives take up a lot less space than file cabinets. And, data collectors are more likely to see data as a matrix of rows and columns. Rather than just viewing data as one-dimensional, they are appending additional data elements. For example, a retail firm may want to know where customers are making purchases, so they will add geo- positioning information to sales transaction records. Rows(records) Columns (data elements) How big is Big Data? It’s a relative concept, especially since volumes that created processing challenges a decade ago are a walk in the park today. Doug Laney gave a very apt definition recently, when he said, “Big Data is data that’s an order of magnitude bigger than you’re accustomed to.” However, volume by itself does not define Big Data. 5
  6. 6. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Volume in the Fund Industry | Exchange Traded Products A great example of the growth of data volume in the fund industry involves exchange-traded products. Traditional open-end mutual funds are priced, purchased and redeemed only once per day. With just five sets of data per week, fund managers found it relatively easy to analyze purchase and sale trends. By contrast, the intraday indicative value for exchange-traded products is updated continually, and shares can be bought and sold throughout the day. With so many more data points, identifying patterns in sales requires much more sophisticated tools. Velocity Not only is more data being collected, it’s being updated more frequently – often almost constantly. For instance, an online retailer will now update inventory records as sales occur, rather than overnight in a batch process. Smart devices – whether phones, thermostats or traffic cameras – are capturing a steady stream of information from our everyday activities. Consumers also actively create the raw material of Big Data, in the form of user-generated content on social media sites and in other online venues. Velocity in the Fund Industry | Books of Record Velocity is driving change in the way many funds keep track of portfolio positions. Traditionally, funds have updated portfolio positioning records just once daily, after the end of trading. The fund would take the custodian’s official “accounting book of record” from the prior day and adjust it for the trading that took place during the day. This procedure gave the investment team an accurate statement of positions at the start of each day’s trading – a statement that would be out of date by the end of the day. As computing has become more powerful and systems more integrated, some funds have begun to use an “investment book of record” that is updated as trades occur, giving portfolio managers and traders position data that is accurate in real time. 6
  7. 7. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Variety Big Data doesn’t have to be alphanumeric. “Unstructured” videos, recordings, pdfs and emails are now as much fodder for analysis as the numbers that we’re used to manipulating in databases and spreadsheets. Digitalization has made this possible. Take the example of how music recording has evolved over the past 50 years, since cassette tapes were invented. Analog cassette tapes could only be analyzed one by one. The introduction of the digital compact disc in the early 1980s made music easier to store, but playback was still limited to specialized devices and analysis wasn’t scalable. It wasn’t until the late 1990s – when the digital data was moved to virtual formats like MP3 – that music became independent of its medium and analyzable en masse. Analog  Digital CaptiveContent  Free Content From a business perspective, the ability to combine data sets and data types in novel ways shows great promises in illuminating insights that were simply not possible in the past. Variety in the Fund Industry | Voice Recognition Call a fund transfer agent today, and you may be asked to prove your identify by providing a PIN code or answering challenge questions. Call a fund transfer agent tomorrow, and they may recognize you by the sound of your voice. Digitalization – coupled with more efficient data retrieval and analysis methods – allow call centers to match your current call with past recordings with an extremely high degree of accuracy. Big Data variety is enhancing both customer service and security. 7
  8. 8. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Veracity Combine volume, velocity and variety – and you’re likely to get lots of inconsistency. Maybe some of the data is collected at a different time, or definitions aren’t consistent throughout the sample. Frankly, it’s hard to keep this much data clean. As a result, Big Data analytics starts with some amount of data scrubbing. Algorithms must sort great data from not-s0-great data from garbage – and then correct the problems that can be fixed. This analysis must be able to handle ambiguity. Put simply, Big Data is imperfect. Veracity in the Fund Industry | Omnibus Accounts Analyzing sales through omnibus accounts maintained by intermediaries is an example of the challenge of veracity in Big Data. Funds may have omnibus relationships with hundreds of intermediaries – and each may provide slightly different data in a slightly different format. To see trends in this stew of data, fund managers first have to invest in smoothing out as many inconsistencies as possible. 8
  9. 9. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics From Descriptive to Prescriptive Analytics Just having Big Data isn’t enough. It has to be analyzed to have any use in business decision-making. The rewards of investing in data analytics are commonly acknowledged to be great. A recent kasina study found that firms believed that business data could help them achieve the following ends: 7% 20% 20% 20% 53% 67% 73% 80% 80% 7% 27% 53% 60% 27% 27% 13% 20% 20% 53% 33% 27% 20% 20% 6% 7% 27% 20% 7% 6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Improve product development Increase share of wallet Improve risk management Improve customer service Increase customer acquisition Increase customer retention Enhance customer segmentation Increase operationalefficiency More informed decision-making Percent of firms OBJECTIVES FIRMS WANT TO ACHIEVE WITH BUSINESS DATA Critical Very important Important Somewhat important Not important ©2013kasinaLLC Fund industry leaders know that decisions will always be better when underpinned by data as well as experience. Yet surprisingly for a numbers-focused business, data analytics has only a small toehold in the fund industry. Yes, the investment area uses data extensively, especially in quantitative approaches. Sales organizations use data analysis to reconcile sales transactions and determine commissions, parse channel and territory production and review marketing campaigns results. But these uses are only scratching the surface of data’s potential. For example, the same kasina study found that: • Fewer than half of firms rely on customer knowledge to develop the corporate strategy for distribution, product development, reputation management and customer care. • One of five firms does not use customer data to develop sales strategy. • Just 2% of firms are currently using predictive analytics. 9
  10. 10. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Overall, the industry is using data to describe the past rather than guide future strategy. Of course, implementing a data-guided approach is far from easy. Data analytics is far from an exact science – in fact, it’s really an exercise in probabilities. Number crunching can fairly easily identify correlations between sets of data items. But determining whether these statistical relationships are the result of cause and effect – and not just coincidence – requires industry expertise. Implementing predictive data analytics effectively requires more than just hiring a team of data analysts to work with the current team. It involves a change in mindset. In data-driven organizations, everyone from senior executives to front-line staff knows exactly what is happening inside their companies and in the marketplace. They have up-to-the-minute knowledge of events and the power to anticipate and respond to opportunities, trends and anomalies. There’s broad demand throughout the organization for actionable intelligence, which is met with interactive dashboards and data visualization applications for real-time decision-making. In sum, everyone at the firm uses data to improve customer experience, manage risk, develop products, and enhance productivity. WHAT A DATA-DRIVEN ORGANIZATION LOOKS LIKE 10
  11. 11. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Myth vs. Reality Even with the challenges, implementing prescriptive analytics is within reach of any firm. Here are three common misconceptions about what it takes to become a data-drive organization: 1. You need perfect data. Firms that don’t yet have a predictive analytics strategy often believe that they can’t get started without perfect data – and they’re convinced that they’re the only firm struggling with this issue. However, data-driven firms recognize that: • A subset of the data available can still provide tremendous insight. For example, when analyzing sales data, even if you don’t have information on every transaction every day, there’s still value in getting some of your data to better understand what’s happening in your distribution channels. • No one has perfect data. In fact, Big Data is, by definition, imperfect. (Remember that fourth V for “veracity.”) 2. You need a big team. Many firms assume they need an army of rocket scientists to parse the mountains of data, and their executives wonder if they’ll be making regular trips to India to visit the tech team that they set up there. True, many firms who are deep into predictive analytics have often committed significant resources to the effort. But it’s ok to start small. Most firms can work with their existing marketing and sales resources – or by calling in the aid of an outsourced solution provider or consultants. In fact, the essential resource in many cases is leadership. A predictive analytics effort requires a leader to drive the effort, manage expectations and marshal needed skills. 11
  12. 12. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics 3. Predictive analytics is a black box. Many firms think Big Data is a black box: put in lots of data, and out comes a fully-formed sales, marketing or customer service strategy. Nothing could be further from the truth. There’s no magic here at all. Yes, predictive analytics does use complicated algorithms. And it can be applied to an incredibly large and complex data set. But the analytics simply examine the questions that users ask. For example, maybe the sales team wants to know if higher click through rates on the video content on the website lead to more productive wholesalers. Or maybe the transfer agent needs to know how voice response menus relate to caller anxiety. Predictive analytics is an objective assessment of the industry expertise that we think we have. In short, don’t assume you can’t use or aren’t ready for predictive analytics. Jump in. The 2% of firms who have done so already are seeing spectacular results. 12
  13. 13. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Appendix | Further Reading Top picks for the general reader Big data: The next frontier for innovation, competition, and productivity Great overview from McKinsey, with insights on how Big Data is being used today and the human resources constraints on increasing data analytic capacity. Includes a useful glossary. Smart Data Collective Online magazine devoted to data. Accessible to the non-techie. The Four V’s of Big Data Seminal infographic from IBM Big Data University Website with training material. Check out the “What is Hadoop?” video if you want an understandable description of a critical Big Data tool. Making effective use of Big Data Big data Analytics and Predictive Analytics An overview from Gartner The Forrester Wave™: Big Data Predictive Analytics Solutions, Q1 2013 Making Big Data a useful business tool Thinking Data, Talking Human Integration of Big Data and behavioral finance from the Lateral Group Visualizing Big Data Data Visualization and Discovery for Better Business Decisions Webinar. Requires registration. FILWD (Fell in Love with Data) Blog dedicated to data visualization Measurement Drives Behavior Series from top data visualization software provider Tableau. 13
  14. 14. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Websites dedicated to Big Data Big Data Gal Beye Network Big Data Landscape IBM: Big Data at the Speed of Business What’s the Big Data? For a deep dive: LinkedIn groups Advanced Analytics Advanced Business Analytics, Data Mining and Predictive Modeling Big Data and Analytics TDWI Business Intelligence and Data Warehousing Discussion Group Big Data use today Analytics: The real-world use of big data 2012 IBM survey on the state of the art. Registration required to download full survey. Mastering Big Data: CFO Strategies to Transform Insight into Opportunity Overview of current use in several industries, including financial services The Evolution of Decision Making SAS and Harvard Business Review study. Requires registration. 14
  15. 15. BIG DATA IN THE FUND INDUSTRY From Descriptive to Prescriptive Data Analytics Acknowledgements This white paper was written by members of NICSA’s Technology and Innovation Committee. Dan Cwenar Rubesh Jacobs Todd Glasgow ©2013 NICSA, Inc. 15