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

BIG DATA IN THE FUND
INDUSTRY
From Descriptive to Prescriptive Data Analytics
NICSA Technology and Innovation Committee
January 2014

BIG DATA IN THE FUND INDUSTRY
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
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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.
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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

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

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.
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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

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

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.
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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
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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

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

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

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

Acknowledgements
This white paper was written by members of NICSA’s Technology and Innovation Committee.
Dan Cwenar
Rubesh Jacobs
Todd Glasgow
©2013 NICSA, Inc.
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