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Featuring the findings of the State Street 2014 Data and Analytics Survey.

Pathways to Data Dexterity

The Innovator’s JourneyData and Analytics

Data Movers

Data Innovators

Data Starters

Advanced data tools and practices will be one of the most powerful agents of change in the investment industry over the next decade.

This is a bold claim given the many other disruptive trends affecting investment organizations — ranging from

emerging investment strategies, the opportunities created by globalization, and a rising tide of regulation. But data’s key role in transforming the industry is highlighted by our new research. Our survey1 of 400 investment organizations reveals that more than four out of five organizations (81 percent) view data and analytics as one of their top strategic priorities for the years ahead.

Mastering data is both a huge challenge and opportunity for today’s investment organizations. Companies in the survey with the most advanced data practices believe they already have a significant competitive advantage. They can analyze risk and performance across today’s complex multi-asset portfolios. They have systems that streamline compliance and allow them to adapt to new stakeholder demands. And they have the flexible infrastructures and data skills to keep pace in a fast-changing environment.

Our earlier survey in 2013 identified a group of “data leaders” — organizations that are turning these capabilities into a source of commercial edge.2 Our new research deepens this analysis. We explore the journey that many companies have embarked on to achieve a higher level of data capability that we call data dexterity. We set out the stages of that journey, the qualities and challenges of companies at the different stages, and their next steps in developing and honing their data capabilities. The ultimate goal is to become agile — harnessing a broader range of data sources, mining that data to uncover new investment opportunities, and using those insights to accelerate their company’s business performance.

Three Steps Toward Data Dexterity With advanced data capabilities emerging as a key success factor, our research aims to benchmark the industry’s progress in this area. To do this, we asked investment organizations to assess their capabilities in six areas:

• Infrastructure

• Insight

• Adaptability

• Compliance

• Talent

• Governance

Our benchmarking survey reveals major variations in investment organizations’ capabilities across each of these areas. Analysis of the results allowed us to identify three distinct groups of investment organizations, each at different stages in the journey toward data dexterity.

2 “Leader or Laggard: How Data Drives Competitive Advantage in the Investment Industry,” State Street, November 2013.1 State Street 2014 Data and Analytics Survey conducted by Longitude Research. See full research methodology on the back cover of this report.

81%of investment organizations

view data and analytics as one of their top strategic priorities

DATA AND ANALYTICS

2 THE INNOVATOR’S JOURNEY 3

STAGE 1Data Starters: Breaking the Legacy Trap

Data Starters are beginning their journey toward more advanced data capabilities. Outdated legacy systems make it difficult for them to get the full value from their data and leave them exposed to a range of risks and challenges. Priorities for Starters include finding ways to replace or integrate today’s fragmented systems, and implementing more flexible reporting platforms to support fast-changing regulatory demands.

• Legacy IT issues are a major barrier to progress. Starters find it more difficult than any other group to leverage old data from multiple departments — 40 percent of them struggle with this versus only 25 percent of Innovators. Outdated systems make it hard for these organizations to adapt. Only 3 percent of Starters strongly agree that their data and analytics capabilities are keeping pace with the business, versus 39 percent of Innovators.

• Starters are stuck on compliance. The majority of Starters (65 percent) will struggle to cope with regulatory reporting requirements over the next three years. As new rules and reporting requirements arise, the danger is that these organizations will be overwhelmed. By contrast, only one-third (33 percent) of Innovators say the same, suggesting that the majority of them feel confident enough to move on.

STAGE 2Data Movers: A Focus on Data Integrity

Data Movers are making headway to more advanced data practices and capabilities. They’re ramping up their investment in data at a faster rate. They are replacing their data systems with an architecture that allows data to flow more freely across the organization. They may have modern tools for analyzing investment risks and performance at the level of individual asset classes — although gaining a whole-fund view of risk may still be challenging. Regulatory issues remain a major problem.

• Movers are upgrading. Movers are increasing their investment in data and analytics more aggressively than the Starters — a trend that signals their intention to overhaul key parts of their data infrastructure. They are beginning to see the rewards too: 65 percent of them agree that their data and analytics capabilities give them a competitive advantage (although only 15 percent strongly agree).

• They must walk before they run. Regulation is a bigger driver of change in Movers’ data strategy than for the other groups. But they are not investing as much in this area as the Innovators. With many Movers fearing that their systems are not keeping up with regulation, this could spell trouble. Tackling the compliance issue has to be a priority before organizations start to adopt more ambitious data strategies.

* Percentage of Data Starters, Movers and Innovators in overall sample

Source: State Street 2014 Data and Analytics Survey conducted by Longitude Research

4 THE INNOVATOR’S JOURNEY

STAGE 1 STAGE 2

STAGE 3

27%*

Data Starters

36%Data Movers

37%Data Innovators

Three Steps TowardData Dexterity

Companies at an early

stage in their data journey

Companies actively moving

toward betterdata capabilities

Companies with advanceddata infrastructure expertise

and high-quality data governance

DATA AND ANALYTICS

5

• Good governance is the first step toward more advanced capabilities. Companies need the right policies and controls to address data security — an issue that emerges as a top concern for 77 percent of organizations in the survey. Improved data governance would help the Movers to address their data integrity issues, and create the right foundation for more sophisticated data analytics. Unfortunately, the survey indicates that for many organizations, compliance and governance issues tend to be low on the list of priorities for investment.

STAGE 3Data Innovators: Ready for the New Age of Investing

Data Innovators have the most advanced data capabilities in our survey. Their superior technology resources are most likely to create a distinctive competitive advantage. Key differentiators for Innovators include the following:

• Innovators treat data as their top strategic priority. For nearly half (49 percent) of Innovators, data and analytics are their single most important strategic priority. Only 27 percent of Movers and 22 percent of Starters give data similar importance. This board-level backing will help ensure these organizations stay ahead in the world of data-driven investing. The rate at which Innovators are investing in data and analytics is also much faster (see the Investing in Data section on page 8 for more detail).

• Innovators are mastering a range of advanced data capabilities. A look at specific types of capabilities reveals where Innovators believe their comparative strengths lie. Innovators are significantly more confident than the Starters and Movers across a range of areas including scenario-generation and stress testing, managing multiple external and internal data streams, optimizing electronic trading, and cross-portfolio analysis of risk and performance.

• Innovators have the edge on electronic trading. Innovators are substantially more active in electronic trading than the other two groups: Over half (55 percent) conduct more than 70 percent of their trades electronically, compared with 38 percent and 39 percent of Movers and Starters respectively.

• They know how to extract commercial insight from their data. Innovators have the tools and capabilities to work their data harder. By accumulating these advantages, organizations accelerate their progress on the journey. Three times as many Innovators (44 percent) as Movers strongly agree that their investment data and analytics are a source of competitive advantage. This figure is nine times as many as for Starters.

• They can also adapt faster to new business needs. Innovators are more than three times as likely as Movers (39 percent vs 11 percent) — and more than 10 times as likely as Starters — to believe strongly that their data and analytics capabilities are keeping pace with business growth.

77%of investment

organizations see data security as

a top concern

DATA AND ANALYTICS


Investing in DataWhile most investment organizations are at

least somewhat increasing their investment

in data and analytics over the next three years,

Innovators are investing much more aggressively.

The survey also sheds light on where these

investments are being targeted:

• Multi-asset investment strategies demand new technology. The growing popularity of

multi-asset investment strategies — where

assets like equities and bonds are combined

with alternative asset classes — has made

it harder for companies to track risk and

performance across the portfolio. Older

technology systems were never designed

to support such a wide range of assets. In

our survey, investment organizations cite

expansion into new assets as the top driver of

change in the way they manage their data.

• Data talent is the top priority for spending. While systems upgrades are in the spotlight,

the number one investment priority overall

will be data talent. This is an increasingly

competitive area, as leading companies

battle to attract and retain top experts

in data science. Bringing data scientists

into the organization and integrating them

into the existing operation will be one of

the key sources of competitive advantage

for Data Innovators in the years ahead.

The Innovator’s Advantage — Data DexterityDue to their progress on the data and analytics journey, Innovators are able to outperform their competitors in a range of areas:

• Empowering business users to extract more insight from their data, faster than ever before

• Enriching their investment decisions with new sources of insight, including from unstructured data

• Harnessing multi-disciplinary teams, including data scientists, to “join the dots” on new business risks and opportunities

• Analyzing risk and performance at a whole-fund level, while also looking through the portfolio at a granular level

• Reducing the costs of compliance and reporting tasks across their global operations

• Protecting and enhancing their data assets, with a strong approach to data governance that strengthens security and compliance

These skills and capabilities will come to distinguish the winners of tomorrow. No one should underestimate the challenges on the road ahead, or the speed with which the landscape around them is changing. But by acting now, companies at all stages in the journey can prepare for a world where data skills and capabilities increasingly define their destiny.

Investment has remained flat or declined each year

7%

4%4%

Investment has been flat, or increased by less than 5%

26%

12%16%

Investment has increased each year by between 5% and 10%

50%

47%59%

Investment has increased each year by between 10% and 20%

15%

28%19%

Investment has increased each year by more than 20%

3%

10%2%

Poor/Starters Medium/MoversGood/Innovators

Which of the following statements best describes your organization’s investment in data and analytics capabilities in the past three years?


38%of Innovators have

increased investment in data by more than

10% each year

44%of Innovators agree their

investment data and analytics are a source

of competitive advantage

DATA AND ANALYTICS


TheInnovator’s

Journey

Investment organizations believe that achieving data dexterity is essential for their future success. More than 80 percent of our survey respondents state that data and analytics are among their organization’s highest strategic priorities. Just over one-third — and nearly half of the Innovators — rate it as their single most important priority.

How did the issue of data management find its way into the boardroom? The answer is simple: It’s become the pre-eminent driver of innovation and efficiency. It’s also one of the industry’s greatest risks and challenges.

Amid the transition to a data-led investment model, the pace of tech-nology-led change will only accelerate. Forward-thinking organizations are already exploring how advances in data science could help them compete in the new environment.

With the right data at their fingertips, investment organizations can achieve a much deeper understanding of risk. They can mine their data to uncover new investment opportunities. And they can move at unprecedented speed to act on these new sources of insight.

Operational efficiencies are also achievable. The smarter use of data can help organizations to streamline and consolidate onerous tasks, such as reporting to global clients and regulators. With the right infrastructure, data helps link smarter investment practices in the front office with operational efficiencies in the back office.

More disruptive innovations are emerging. Tech-savvy companies are starting to extract investment insights from unstructured data such as social media, video and audio files, text-based articles and reports. Predictive analytics tools may help investment organizations to model the behaviors of assets under different conditions. Investment organizations are also investing in new types of expertise — in particular, bringing in data scientists to help them achieve more with their data.

Figure 1: How do most senior leaders at your institution view the importance of investment data and analytics relative to other major strategic priorities?

It is a high strategic priority (near the top)

47%

It is the most important strategic priority

34%

It is a mid-level strategic priority

18%

It is a low-level strategic priority (near the bottom)

2%


DATA AND ANALYTICS


With our interactive online tool, you can benchmark your data capabilities againstthe 400 investment organizations we surveyed for this report.

Find Out Where You Are On the

Innovator’s JourneyDiscover your data point at

www.datatool.statestreet.com.

Coming in April!

“We’re just starting to see how trends such as big data and predictive analytics will impact the industry,” says Jeff Conway, head of State Street Global Exchange. “But it’s clear from our research that the Data Innovators have the right attitude and strategies to keep pace with these momentous changes.”

Some companies are already overhauling their data systems, tools and techniques. Others understand that data is a priority, but have more work to do to modernize their data systems and strategies. Barry Benjamin, US and global asset management sector leader at PwC, explains the challenge facing the industry: “I don’t think there’s a lack of focus on data or investment in the industry. The challenge now is how to make better use of all that data. What kinds of tools are being developed to access and analyze it?”

Charting the JourneyTo help gauge investment organizations’

progress in developing advanced data

capabilities, we asked industry executives

to assess their capabilities in six areas

depicted in the chart on the next page.

Survey respondents were asked to score their

capabilities in each of the six areas on a scale of

1 to 10, where 10 is the highest level of capabil-

ity. Using cluster analysis,3 we then identified

three distinct groups in the survey — Start-

ers (28 percent of the sample), Movers (36

percent) and Innovators (36 percent) — each

having similar capabilities and attributes. The

chart shows some of the key milestones the

investment organizations must address as

they progress from Starters to Innovators.

Some of the milestones depicted might be

reached in a different sequence, or in parallel.

It’s not a linear process — organizations will

find themselves going back to the earlier

stages as needs change or new technologies

emerge. Those at more advanced stages

need to continuously update their data

infrastructure and processes. They’ll constantly

explore how new tools and techniques might

create new opportunities for innovation. 3 Cluster analysis is a technique for statistical data analysis that is

commonly used to identify coherent groups (the “clusters”) within a dataset.

Infrastructure

Insight

Adaptability

Compliance

Talent

Governance

• Incompatible legacy systems

• Most data in separate silos

• Separate platforms for different asset classes

• Aggregation, visualization, other data management capabilities upgraded

• Improved data integration across investment portfolio

• Infrastructure and tools fully upgraded

• Silos broken down

• Seamless integration of unstructured data

• Limited ability to generate performance and risk analysis

• Fragmented view by asset class

• Rich, partly integrated performance and risk analysis in individual asset classes

• Ability to analyze performance and risk of wide range of alternative assets

• Ability to conduct scenario and stress testing across portfolios

• Integrated view of risk and performance across multi-asset portfolios

• Capabilities take much time and effort to adapt to any changes in investment strategy

• Reduction in time to market for new product additions, or new asset classes

• Capabilities can adapt quickly to support new products or investment strategies

• Systems struggle to keep up with existing regulatory requirements

• Systems fully compliant with existing requirements, but slow to adapt to new rules

• Excellent data quality and traceability back to source

• Reporting systems adapt quickly to new requirements across global footprint

• Data team can manage data collection and basic analytics functions

• Team can manage data collection and integration; some capabilities to perform and manage analytics

• Regular flow of insights; smooth interaction with fund managers

• Basic data policies and processes in place, but governance is patchy, little standardization

• Data gaps, errors identified; cleaning underway

• Coherent, agreed framework for data governance

• Large degree of standardization across internal databases; external providers aligned

• Strong leadership on data governance; policies and processes entrenched throughout

DataStarters

DataMovers

DataInnovators

Our Survey Benchmarked Companies on Six Paths to Innovation

DATA AND ANALYTICS


Investment Drivers

Time for anUpgrade?

4 See “Pension Funds DIY” and “Frontline Revolution: The New Battleground for Asset Managers,” both by State Street, 2014.

Any asset that can boost investment performance, reduce risk and deliver cost-saving efficiencies deserves investment. Technology spending is rising as asset owners and asset managers realize how critical data excellence is to their future survival.

Nearly 80 percent of executives in the survey report investment growth in data and analytics capabilities of 5 percent or more in the past three years. Our Innovators are the biggest spenders of all: 38 percent have been increasing investment by at least 10 percent annually. But Movers aren’t far behind: Almost 60 percent increased data and analytics spending by between 5 and 10 percent in the past three years. One-fifth increased it by more than 10 percent.

A number of trends are pressuring organizations to modernize their data infrastructures — causing the rise in technology spending. Our survey shows that four in particular stand out.

1. Expansion into new asset classes. Investment organizations are turning to alternative asset classes as a way to boost their returns in a low-interest environment. Earlier surveys conducted by State Street revealed growing demand for alternative assets such as private equity, real estate, infrastructure and hedge funds.4 As investors shift more of their allocations into alternatives, they find themselves managing a multi-asset portfolio that is much more complex than the traditional

1. Expansion into new asset classes

32%

2. Expansion into new regions

30%

3. Growing volume of trading data

29%

4. More stringent risk management standards

29%

5. Competitive pressure

27%

6. Increased demands from clients/investors

27%

7. Increased threats to cyber security

25%

8. Increased demands from regulators

24%

9. Electronification of trading

22%

10. Increased demands from internal staff

17%


Figure 2: Ten Industry Trends Driving Innovation in Data Strategies (Respondents could choose more than one option.)

DATA AND ANALYTICS


blend of domestic equities and bonds. Expanding into new asset classes raises two key challenges. First, obtaining reliable data on asset classes like hedge funds and private equity can be more difficult than it is for traditional asset classes. Organizations are also challenged with integrating and comparing this information to build a more integrated view of risk and performance across the fund. Both of these issues create the need for new data sources and analytical tools (see the Alternative Assets and the Data Challenge box on page 17 for more detail).

2. Expansion into new markets. This puts pressure on data systems to become more flexible. Reporting systems need to be customized to the needs of local regulators and investors. Distribution and client servicing platforms need to be optimized for the local market. Creating an architecture that supports these market variations — and keeps pace with different demands as they evolve in different locations — is one of the ultimate tests of organizations’ data dexterity.

3. High data volumes in the age of electronic trading. Investment organizations need to transact and process huge volumes of data, quickly. Our survey confirms that this trend will only accelerate, with half of the respondents believing

that 70 percent or more of their trading will be conducted on electronic platforms within three years. “We try to do as much as possible electronically. It’s cheap, less complex and you can get an earlier response,” says Christian Franzen, global head of Performance & Portfolio Risk with Allianz Global Investors. Executing trades rapidly, while making sure that the correct data is collected for reporting purposes, becomes a significant challenge.

4. More stringent risk demands. Amid a volatile risk environment, many feel pressure to develop stronger risk management tools and strategies. Richard Brandweiner, chief investment officer of First State Super, an Australian pension fund, says: “Data analytics gives us the ability to apply a more insightful lens on where the risk factors lie across the whole portfolio.” The challenge, however, is to bring data together across traditional and alternative asset classes so organizations can get a whole-fund view of risk.

These multifaceted challenges place tremendous strain on traditional data infrastructures. In this demanding environment, technologies that were once adequate are no longer fit for purpose. Inflexible legacy systems and variable data quality are holding back organizations.

Alternative Assets and the Data ChallengeThe shift into alternative investments

poses significant challenges for investment

organizations. The problems start with finding

good quality data. Private equities, for example,

play an increasingly prominent role in investment

strategies. But, according to Stefan Gmuer,

head of Sector Solution Sales for Europe,

the Middle East and Africa at State Street,

“There’s no transparency. It’s very difficult to

find timely, accurate and transparent data on

these instruments.” Richard Brandweiner of

First State Super points out that this applies

to alternatives as a whole: “Data is generally

available for listed markets. It becomes very

problematic, however, for alternatives. Basically,

it requires using proxies and more granular

modeling of each individual alternative asset,

which is cumbersome and imprecise.”

The shortage of good data in this area requires

new tools and techniques to plug the gap.

Brian Curran, investment senior vice president

for pension risk transfer with Prudential

Retirement, notes that organizations are

unlikely to get information on alternatives

delivered as a convenient automated data feed.

“We have some information on alternatives,

but no pricing service is going to provide you a

market value for them,” he says. “There needs

to be information on the underlying assets

and strategies to determine the value. And the

increased market volatility makes you want to

receive this information more frequently.”

The large range of strategies deployed by

alternative managers worsens the problem.

Curran notes that there is no such thing as a

typical hedge fund. “You have to understand the

strategy that underlies the investment thesis

of that hedge fund manager and the strategy

for that particular fund. The technologies and

data to help with this aren’t quite there yet.”

Solutions to this problem may be on the distant

horizon. With more demand for good quality

information on alternative asset classes, some

commentators believe that the industry will

begin to develop better databases and data

sources. “We have trouble getting good data that

is easy to consume and analyze on alternatives,”

says Sean Tarte, director of Finance Systems

Development at MUFG Union Bank. “You’ll start

seeing some traction around this over the next

5 to 10 years to help get the industry moving

toward better transparency and information.”

DATA AND ANALYTICS


The New ToolkitOur survey sheds light on where investment organizations are spending their money on technology.

Two areas compete for first place: Data management systems are cited by 72 percent of respondents as a major focus for investment. These systems provide the vital architecture enabling data to be captured and integrated, analyzed and delivered to end users.

A flexible data management system can greatly increase an organization’s ability to extract value from its information. According to Kevin Wong, State Street’s head of Sector Solutions, Asia Pacific, there is a need to move beyond today’s fragmented data systems to a more coherent platform for data management.

“Individual teams have gone out and sourced platforms that fit their own needs at the time. But then problems arise when the company tries to use that data elsewhere in the organization,” says Wong. “Meanwhile, the range of asset classes that the data infrastructure needs to support is growing. So there’s a real need for data management systems that can create a more seamless flow of information across the business, delivering the data in the right format to the right person at the right time.”

Investment organizations will also prioritize electronic trading platforms in the years ahead. This is an area where companies need to transact with data quickly and efficiently. Innovators in our survey are already demonstrating greater capabilities with this. They’re substantially more confident in their ability to optimize electronic trading strategies.

Risk and performance analytics are also tipped for increased spending. Investment institutions need to be able to integrate risk and performance data across a wide range of asset classes in the portfolio, including alternatives. The more effectively they can do that, the more successful they’ll be in the new investment environment,” says Scott Fitzgerald, head of sector solutions, North America, of State Street.

When combined, data management systems, electronic trading platforms and new analytics tools can create a technology engine capable of making

Figure 3: Over the next 3 years, what changes do you expect to the level of investment your institution makes in the following types of technology or data?

Electronic trading system

36%34%

Data management system

35%36%

Risk analytics

31%37%

Performance analytics

29%43%

Portfolio modeling/optimization system

27%37%

Order management/execution management systems

23%41%

Significant Increase Slight Increase

investment organizations more agile and efficient across almost all their activities. Starters, Movers and Innovators all view these capabilities as important areas for investment — but the Innovators set the pace on investment, while the Starters are much more tentative in their spending even on these priority tools.

From Investment to Return

Spending is increasing across a range of technologies — but does that translate into a commercial advantage? This is where the Innovators truly stand apart. Forty-four percent of Innovators believe strongly their investment data and analytics are a source of competitive advantage, compared with only 15 percent of Movers and 5 percent of Starters. Similarly, 39 percent of Innovators believe firmly their data and analytics capabilities are keeping pace with business growth. This compares with 11 percent of Movers and just 3 percent of Starters.

Simply purchasing new technology is not enough. The bigger goal of data dexterity requires companies to tackle a number of key challenges. Our research suggests that organizations tend to move through a number of stages toward extracting more value from their data. They start with basic data plumbing issues, move onto more advanced practices of data governance, before finally embracing the tools and capabilities defining the industry leaders.

Order management/execution management systems (OMS/EMS)

14%

34%19%

Risk analytics

25%

39%27%

Performance analytics

15%

35%32%

Electronic trading system

20%

46%39%

Portfolio modeling/optimization system

22%

35%22%

Starters MoversInnovators

Compliance/reporting

17%

35%20%

Accounting

18%

22%23%

Data management system

27%

44%33%

Figure 4: Innovators Outspending Movers and Starters on These Technologies

72%of investment

organizations are investing in data

management systems



DATA AND ANALYTICS


Stage 1

Breaking theLegacy Trap

Three Stages of Data Dexterity

Figure 5: The Importance of Data Integration

A lot of organizations’ investments focus on the need to generate insights in a complex investment environment. These include managing multi-asset portfolios, operating in global markets, and acting decisively in the fast-paced world of electronic trading.

But a number of barriers make these ambitions elusive for the majority in our survey. Most Starters, as well as a lot of Movers, are still struggling with the basics — integrating and managing data, and connecting or replacing their legacy systems. They don’t yet have the data governance that supports everything from data security and

regulatory compliance to high-quality analysis. Following are some of the other major barriers to progress.

Fixing the PlumbingLegacy systems are one of the biggest obstacles. Many investment organizations run core operations on a patchwork of old systems that are difficult to integrate. Valuable data gets trapped in silos, making it harder to use across the organization.

Practitioners and experts are united on this issue: “This industry has big legacy issues. The way databases were constructed in the past makes them harder to access and analyze for today’s purposes. In fact, the way we used to collect and organize information was decided at a time before people understood the real value and potential of data as an asset,” says Jessica Donohue, chief innovation officer at State Street Global Exchange.

There are also data integration problems, which affect investment organizations across all our different survey groups, although in different ways. Starters have particular problems in reconciling older data from multiple departments. Both Starters and Movers are more troubled by legacy databases than Innovators.

Data integration is the glue to be able to make investments, deliver reports, enhance clients’ experience and provide the information needed to function effectively.

DataReporting/

Architecture/Governance

DATA AGGREGATION

Risk

Effi

cien

cy

Client Analytics Regulation Investment

Intern

al R

epor

ting

Client Reporting

Regulatory Reporting

DATA AND ANALYTICS


Figure 6: What are the toughest data integration challenges your company faces?

Innovators say their toughest challenge in this area is working with the data they receive from external providers. Christian Franzen of Allianz Global Investors provides an example of where problems can crop up: “Let’s say you need to bring in a new data feed on an Asian market. Sometimes the providers don’t have a standard interface for providing this information. That makes it hard to get the new benchmark linked to our system.” This represents added business cost. Indeed, the survey respondents rank the cost of external data as one of their top integration challenges.

The goal of being able to use data flexibly, when and where it’s needed, will remain elusive until organizations find a way to overcome these integration barriers. These issues help explain why

Starters, and to a lesser degree Movers in our survey, struggle to extract more insight and value from their data.

Outdated Systems Fail the Compliance TestIn recent years, a wave of regulation has placed increasing demands on investment organizations’ data infrastructures. Looming on the horizon is the European Union’s MiFID (Market in Financial Investments Directive) II, which will come into force in 2017. This will keep investors’ data teams extremely busy. Vanessa Field, head of compliance at Principal Global Investors (Europe), points out that MiFID II transaction reporting obligations are extensive, with up to 93 fields reported on each individual trade (compared with 24 under the original directive).

Compliance is now an everyday business priority rather than a one-off challenge. Regulatory reporting requirements for investment organizations have been changing rapidly, and they will continue evolving. “There’s no way you can have an end in sight on compliance,” says Virginie O’Shea, senior analyst with research advisory firm Aite Group: “The market is constantly changing, leading to new and different ways to report information. These cause massive changes in how market players and regulators consume data. You have to be ready to adapt to keep pace.”

“There are large institutions who didn’t take the time to set up their data models properly. They’re surfing on years of giant data warehouse projects and are unfortunately avoiding the inevitable, which means they have to raze their models to the ground.”

PHILIP BIBER

Chief Operating Officer Banque Syz

Integrating unstructured data

32%

Reconciling older data from multiple departments

31%

Cost of external data

31%

Working with data from external providers

30%

Standardizing data formats across departments

28%

Integrating legacy systems

27%

Accessing external data

26%

Working with data warehouses that are too slow

23%

Keeping up with regulatory change is an area where outdated systems create considerable risks for organizations. Nine in ten investment organizations expect reporting requirements to increase over the next three years. Yet half (and 55 percent of those from Asia Pacific) say their data capabilities will struggle to cope with the changes. Here again, Innovators appear on much firmer ground than their survey peers. The majority of Starters (65 percent) and Movers (55 percent) will struggle to cope with regulatory reporting requirements over the next three years; only one-third (33 percent) of Innovators say the same.

One reason the Innovators may be in a stronger position to meet regulations is that they make sustained investments in their compliance systems. Just more than 40 percent of Innovators say they plan to prioritize investment in their compliance data reporting systems over the next three years. Among both Movers and Starters, less than a third (31 percent) say the same.

Movers are arguably in the toughest position of all three groups when it comes to compliance. They’re seeking to upgrade their overall data capabilities quickly, yet they’re not confident about their ability to keep up with changing regulation. This in itself creates an element of risk, but Movers are not prioritizing investment in this area — a warning sign that they urgently need to address their weaknesses in providing compliance-ready data before moving too much further on their data journey.

Figure 7: Which of the following statements best characterizes your expectations for regulatory reporting requirements over the next 3 years?

Reporting requirements will increase, but our data capabilities will cope adequately

21%

60%34%

Poor/Starters Medium/MoversGood/Innovators

Reporting requirements will increase, and our data capabilities will struggle to cope

65%

33%55%

Reporting requirements will largely remain unchanged from today

13%

5%10%

Reporting requirements will ease

2%

2%1% 50%

of investment organizations say

their data capabilities will struggle with

regulatory changes



DATA AND ANALYTICS



Stage 2

A Focus OnGovernance

Good data governance is vital for any organization operating in today’s investment environment. Weak spots in the way data is captured, structured and standardized can undermine the way investment organizations are able to use their corporate information.

Three major issues arise:

• Can we trace the data back to a reliable source? Increased regulatory scrutiny has forced investment organizations to be more considered in the way they collect data. It has put a spotlight on the quality of data that underpins their key reporting metrics. “Regulators are demanding that investors can show where their regulatory reporting data came from,” confirms Ivan Matviak, head of software solutions at State Street Global Exchange. “How did they get the

risk number and the net asset value they published? To do that, you need tools to trace and audit your analytics, and you need a good data governance framework to control that data.”

• Is the data accurate and up-to-date? Investment organizations need to move fast to respond to new investment opportunities, or to respond to rapid shifts in the risk environment. Each of the three survey groups lists accuracy as among the top data and analytics challenges they face today. For Movers, it is nearly as important as data security.

• Is the data secure? Cyber security emerges as a major issue in the survey (see the Cyber Security on the Agenda section on page 27), with 77 percent of investment organizations citing it as a major challenge.

These are big questions that today’s investment organizations need to address to satisfy the demands of their fund managers, their compliance teams, their clients and the regulators. But again, this is an area where there are large gaps between the three groups identified in the survey. When asked to rate themselves on a 1-10 scale, with the highest rating involving “strong leadership on data governance” and policies and processes “well entrenched throughout the organization,” 49 percent of Innovators claim a 9 or 10 score.

36%Security of data

30%Accuracy of data

22%Timeliness of data

21%Ability to extract

broader themes andforward-looking insights


Figure 8: What do you consider to be the top challenges related to investment data and analytics overall for your business today? (Respondents could choose more than one option.)

DATA AND ANALYTICS


By contrast, only 8 percent of Movers and 1 percent of Starters give themselves similarly high scores on governance. Most do no more than claim that they are making “good progress toward developing a more coherent governance framework.”

As with compliance, a failure to improve governance standards faster may hold greater risk for Movers than for Starters, if only because the Movers are making better progress in upgrading their data architecture and removing silos. Advancing quickly before the desired standards of accuracy, quality, transparency and ownership are embedded could cause companies serious headaches down the road in all facets of risk and performance analysis, as well as regulatory reporting.

The legacy issues discussed earlier in this section greatly complicate the task of establishing good data governance. At many investment organizations, data quality remains subpar and standardizing data across departments and databases

is far from complete. “The challenge,” according to one executive interviewed for this report, “is moving everything to a consistent standard; that’s a difficult task, requiring significant investment.”

At the same time, the need for data integrity is increasing, according to Sean Tarte, director of Finance Systems Development with MUFG Union Bank. Because of this, he says, “The industry probably hasn’t moved the needle in terms of perceived data quality.” In earlier years, for example, data systems did not cover complex financial instruments. The expectation now is that all of today’s complex instruments and derivatives, whether they are exchange-traded or over-the-counter, are properly covered in an organization’s systems.

There are no shortcuts to improving data quality or establishing strong data governance. But leading organizations point to some of the strategies that can help. Virginie O’Shea of Aite Group says organizations should undertake an inventory of their organizational data, so they can determine the state of their investment data before taking steps to improve the way data is organized and structured.

David Hand, chief scientific advisor with Winton Capital Management, suggests that some of the problems arise from trying to aggregate data from different sources. Values may be missing from one source, for example, while issues could also arise from the different formats used by different trading

“We must ensure that the data is being kept securely to meet our privacy obligations and that standards have been developed around that. That includes standards for information security. For regulators, there’s also a real focus on the accuracy of the data.”

MARK SHAW

Head of Compliance Australia, ANZ

exchanges. Statistical models can be built to try to adjust for these problems, he notes.

Ultimately achieving strong data governance requires managers across the business to take responsibility for the integrity of their data. “Cultural change is needed to shift attitudes on data,” says O’Shea. “Risk management goes hand-in-hand with establishing a data management culture.”

Issues such as data accuracy, integrity and security can never be addressed by technology fixes alone. A strong governance culture provides the foundation for organizations as they seek to protect their data and make sure the insights they generate are timely, accurate and 100 percent trustworthy.

Cyber Security on the AgendaCybercrime has long attracted the attention

of senior management at investment

organizations, but until recently attacks and

breaches seemed mainly to target retail

financial institutions, governments and other

sectors. This changed in 2014 with disclosure

of a major cyber attack on 13 investment

management and other financial institutions.

In this context, it’s not surprising that more

than three-quarters of survey respondents

agree that data security is now a key strategic

issue in the industry. Nearly a third (31 percent)

agree strongly with this statement. Security is

also rated as the top challenge for investment

data and analytics overall for investment

organizations today, ahead of data accuracy,

timeliness and volume. It’s notable that

Innovators appear to give greater emphasis to the

security challenge than the other survey groups.

“Security is about to become very important

in the industry,” affirms David Stewart, chief

investment officer at Santander. “It is inevitable

that financial institutions will allocate more

investment toward security than had been

the case. What was previously a ‘nice to have’

is about to become a survival factor.”

According to Mark Morrison, State

Street’s chief information security officer,

cybercriminals will target financial

institutions with a number of purposes in

mind. He categorizes five types of threats:

• Financial, including the takeover of client

accounts, and illicit wire transfers

• Sensitive information leakage on, for

example, investment and trading strategies,

board reports and company financials

• Targeted people and reputations — board

members, chief executive officers, chief

financial officers and other executives

• Front running or other manipulation of trading

• Denial of service — disrupting client access

and general operations

One of the keys to bolstering security is to un-

derstand which entities hold or have access to

different types of information. These may include

transfer agents, administrators, distributors,

broker-dealers, custodians, auditors and others

working within or with investment organizations.

Security defenses are also coming under growing

regulatory scrutiny. In the US, for example, the

Securities and Exchange Commission (SEC) and

Financial Industry Regulatory Authority (FINRA)

announced plans in 2014 to examine selected

broker-dealers and investment advisors on their

cyber security preparedness. Guidelines that are

issued are likely to be voluntary for the time being,

but any large future breach will almost certainly

increase the pressure on investment organizations

to demonstrate the strength of their defenses.

31%of investment

organizations strongly agree data security

is now a key strategic issue

DATA AND ANALYTICS


Stage 3

Advanced DataCapabilities


With the right infrastructure and data governance in place, investment organizations can get to grips with pressing industry challenges. They can become more ambitious in pursuing the opportunities of a new data-driven investment model.

The Innovators are experimenting in two key areas in particular. They’re pursuing the use of unstructured data and predictive analytics to deliver new investment insights. They’re also pushing the boundaries of data science — hiring in specialist talent to help develop more creative ways to manipulate and interrogate their data assets.

The Potential of Unstructured DataOne of the most interesting questions is what impact the use of unstructured data might have on the future of the investment industry. Unstructured data describes the non-numerical data found in social media, blogs, news reports, video and audio files, and other channels.

Leading organizations are beginning to explore the use of unstructured data as a way to spot emerging investment trends, or to develop a richer understanding of risks impacting the investment portfolio. This is now a key challenge for Innovators and Movers in our survey: Unstructured

data emerges as their second toughest data integration challenge. By contrast, the Starters don’t tend to view this as an issue — likely because they’re focused on the more basic data issues.

Many experts in the industry agree that unstructured data could open up new opportunities. “The use of unstructured data has the potential to drive significant change in the industry,” says Ronnie Sadka, professor of Finance at Boston College in the US. “People visit investment managers to get a good return, and the managers can differentiate with strategies based on unique data sets.”

The ability to harness unstructured data has the potential to open new frontiers in terms of how investment organizations analyze investment risks and opportunities. Many are likely to experiment with tools and techniques to exploit unstructured data. For example, some industry observers believe that sentiment analysis of social media could be valuable to predict market fluctuations. Sadka agrees that the likes of Twitter may be able to help predict stock returns in a way that could hugely benefit investors, although he adds that in practice these techniques remain unproven.

“Today, we try to fit same data sets into fatigued world views or paradigms, and we’re still using modern portfolio theory. In the future we’ll be using new data and data analytics in original ways that are unconstrained by the myopic approach we have now.”

ANGELO CALVELLO

Chief Executive Officer Impact Investment Partners

DATA AND ANALYTICS


Ken Froot, managing director of FDO Partners, and his colleagues have been using other types of unstructured data available from the Internet, such as news flows and company reports, to try and predict equity price movements with some degree of success. He warns, however, that the endeavor is complex: “Many firms, agencies and the Internet itself are generating lots of granular pieces of information; some of it is use-less, some of it is useful, some of it is only useful in a very specialized way. The challenge is figuring out what those ways are and understanding how to aggregate the information. After all, markets themselves serve to aggregate information.”

A separate challenge is that investment organizations’ data warehouses — which attracted considerable investment over the past decade — may not be set up to easily handle unstructured data. State Street’s Ivan Matviak notes that trying to bring in new forms of unstructured data to complement traditional cash transaction information often results in the data warehouse breaking down.

Taking advantage of unstructured data requires investment organizations to adopt a different kind of mindset. Analyzing this kind of information often produces answers that are less black and white than traditional data mining techniques. “Unstructured data presents great opportunity.

But if investment organizations aren’t comfortable operating in a realm where there is more ambiguity, and if they’re not willing to experiment, they’re unlikely to get value from it,” says Jeff Conway at State Street.

Ken Froot agrees that the move to embrace a broader range of information sources changes the way investment organizations need to think about their data. “Many people think that quantitative modeling is by definition very numeric, driven by inflexible algorithms in an ‘if-then’ kind of world,” he comments. “But there’s a tremendous amount of judgment needed to build models that work, that adapt, and that are sensitive and appropriate for current circumstances, which are never exactly the same as they were in the past. Broad information sources and big data can be useful, but they only reinforce the need to interpret the nature of the information, the nature of markets, and what’s likely to be important for them and what’s not.”

The new data frontier will therefore require companies to move out of their comfort zone on data. They’re heading into new territory and will need new types of specialist talent and expertise.

Analytics in the cloud Powerful number-crunching has traditionally

been handled in-house on custom-built or

best-of-breed platforms. But cloud-based

technologies are increasingly able to process

and analyze large volumes of data. Investment

organizations have been reluctant to entrust

data analysis to the cloud, but this may

change as the technology providers address

concerns over data privacy and security.

Big data lakes Hadoop, the software platform that enables the

distributed processing of very large data sets,

promises to streamline the process of data

analysis. Rather than design a rigid data model,

and then feed information into a traditional

data warehouse, the new approach allows all

sorts of unprocessed data to be dumped into a

“data lake.” The rules of analysis can then be

established along the way. This means that fund

managers and analysts will no longer be locked

into prescribed formats for data analysis.

Behavioral analytics Efforts to systematically predict investor and

market behavior are nothing new, but vast

increases in computing power will unlock

new techniques that could make this a reality.

Current advances center on the analysis of

news reports, social media and other diverse

types of information to divine sentiment and

support investment decision making.

Deep learning New machine-learning techniques will help

make some of the advances described above

a reality. Applying the principles of neural

networks, algorithms can be built into analytics

programs that can make sense of, and establish

relationships between, unstructured and

other diverse types of data. Fintech start-ups

are now applying this technique to trading.

Augmented reality What started as a gaming technology is now

finding its way into more practical business

fields, including investing and trading. It

may seem exotic in the world of institutional

investment, but viewing data and models

augmented by three-dimensional images

and graphics will eventually help fund

managers and traders — as well as clients

— to see market trends in a new light.

Five Technologies That Could Change the Investment World

DATA AND ANALYTICS


Talent in the MachineAdvanced technology can go only so far to help organizations extract greater value from their data. As the latter becomes a more valuable and versatile asset, organizations are hunting for the talent that could help them compete at the forefront of data-led investing. Once again, this is an area where Innovators in the survey rate their capabilities significantly higher than the Movers and, in particular, the Starters.

All three groups nevertheless agree that attracting specialist data talent is a top priority for the future. Data talent emerges in the survey as an even bigger target for investment than technology. Investing in talent trumps even such critical priorities as improving analytics and visualization capabilities, data system adaptability, compliance and governance.

There’s strong demand for all types of data skills, from traditional IT professionals to portfolio managers who are comfortable using some of the new techniques and tools. But competition is particularly intense for a select band of data scientists that understand cutting-edge analytics, and also have the knowledge required to apply these techniques in an investment environment. This is a critical combination.

These specialist roles are notoriously difficult to fill, according to Albi Satku, principal technology consultant with Glocomms, a recruitment firm. “It’s one of the sexiest jobs out there,” she says, “but the available talent is scarce. That’s partly because a lot of investment organizations don’t know what they’re looking for yet.”

One of the major challenges is finding data scientists who can also understand the company’s business model and investment services. Satku sees widespread recognition that individuals with the right statistical and technical skills — often armed with doctorate degrees — are very important in this role. “But what’s really important,” she says, “is getting individuals who can think outside of the box, who can visualize the data and ask, ‘What are you trying to achieve with this information?’”

Quality and availability of data talent

50%

Adaptability of your data systems to new needs

42%

Analysis and visualization of data

40%

Data infrastructure

39%

Data governance (e.g. clear policies, processes and leadership)

35%

Ability of data systems to keep pace with regulatory requirements

35%

Figure 9: In which of the following areas do you intend to prioritize investment in the next three years? (Respondents could choose more than one option.)

State Street’s Jessica Donohue agrees on the importance of technical and analytical skills, but also feels the right person has to have a passion for finance. “You can’t take a technical geek and stick him in the middle of a bunch of financial data and get something interesting out of it.” Christian Franzen of Allianz Global Investors also emphasizes the importance of financial industry knowledge. “There are some very good data analysts, but sometimes they don’t have any clue about portfolio management, which for us is really important.”

Some experts stress the importance of soft skills in the data department of the future. The most effective — and valuable — data scientist, they believe, is one who conducts a business discussion about the results of his or her team’s data analysis with fund managers themselves. “It’s no good having a genius who doesn’t talk to anybody or who just ticks everybody off,” says David Hand at Winton Capital Management.

The leading players will not only attract the right blend of data skills — they’ll also know how to integrate those skills effectively into their investment operations.

The Skills Needed: A Headhunter’s PerspectiveNew York-based Albi Satku, of recruitment

firm Selby Jennings, was among the first

to establish a recruitment practice focused

on data science. Hedge funds have been

a particular area of focus. “They look for

quant researchers who can figure out new

patterns and build algorithms,” Satku

says. “The technical background (in data

mining, for example) is important, but

what they’re looking for is individuals who

can think creatively and intuitively.”

A different category of data specialist is what

Satku calls “front office quants.” These sit on

the trading desk and help traders come up

with different investment strategies. “They

do price models; they evaluate the market

to see where there could be a new place to

invest; they give recommendations to the

traders and salespeople.” She stresses that

these are individuals who provide investment

strategy advice. But they do more than that:

they go out and meet customers, whether

they’re high-net-worth individuals or

institutions, to gain an understanding of how

and where they’re investing their money.

Data professionals with analogous skills exist in

growing numbers outside the financial industry,

Satku observes. It’s still early days in financial

industry, however, and new data-oriented roles

will continue to emerge in the coming years.


DATA AND ANALYTICS


Conclusion

Defining DataDexterity

The data and analytics journey we have described throughout this report is a long one. Among our investment organizations, the Innovators have traveled the farthest. They are obtaining more value from their data thanks to superior capabilities in six key areas:

• Integrated infrastructure. Innovators have found a way to replace or integrate the data silos that act as a barrier to harnessing investment information across the organization.

• Strong data governance. Innovators prize data quality and integrity. They set high standards for data governance and security that are entrenched throughout the business.

• Streamlined regulatory compliance. They have invested in state-of-the-art systems to support regulatory com-pliance and reporting. Their exposure to compliance failure is much reduced compared with Starters and Movers.

• Insights generation across the portfolio. They have advanced tools and techniques for conducting performance and risk analysis across investment portfolios, including alternative asset classes.

• Architectures that adapt. Their data and analytics capabilities will adapt swiftly to changes in investment strategies. Their reporting systems will adapt smoothly to changes in regulation across the globe.

• Data talent with a passion for finance. Data teams will be staffed with data scientists who know how to generate valuable investment insights and interact effectively with portfolio managers and other parts of the business.

As these wide-ranging challenges show, staying ahead in a fast-changing market will be as much about cultural change as about adopting new technology. The industry leaders of the future prize data as a strategic asset at every level of the business. They invest in it, protect it and work hard to squeeze every ounce of value from it. They know data can give them a competitive edge — and they are building the foundations now to succeed in the high-tech investment model of the future.

DATA AND ANALYTICS


©2016 State Street Corporation. All Rights Reserved. This document is for marketing and/or informational purposes only, it does not take into account any investor’s particular investment objectives, strategies or tax and legal status, nor does it purport to be comprehensive or intended to replace the exercise of a client’s own careful independent review regarding any corresponding investment decision or review of our products and services prior to making any decision regarding their utilization. This does not constitute investment, legal, or tax advice and is not a solicitation for products or services or intended to constitute any binding contractual arrangement or commitment by State Street and/or any subsidiary referenced herein to provide securities services. State Street hereby disclaims all liability, whether arising in contract, tort or otherwise, for any losses, liabilities, damages, expenses or costs arising, either direct or consequential, from or in connection with the use of this document and/or the information herein. This document contains certain statements that may be deemed forward-looking statements, which are based on certain assumptions and analyses made in light of experience and perception of historical trends, current conditions, expected future developments and other factors believed appropriate in the circumstances.

Risk associated with equity investing includes stock values which may fluctuate in response to the activities of individual companies and general market and economic conditions. Bonds generally present less short-term risk and volatility than stocks, but contain interest rate risk (as interest rates rise bond values and yields usually fall); issuer default risk; issuer credit risk; liquidity risk; and inflation risk. These effects are usually pronounced for longer-term securities. Any fixed income security sold or redeemed prior to maturity may be subject to a substantial gain or loss. Diversification does not ensure a profit or guarantee against loss. All information has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. There is no representation or warranty as to the current accuracy, reliability or completeness of, nor liability for, decisions based on such information and it should not be relied on as such. The whole or any part of this work may not be reproduced, copied or transmitted or any of its contents disclosed to third parties without State Street express written consent. Investing involves risk including the risk of loss of principal. Expiration: September 30, 2017 CORP-2237

©2016 State Street Corporation. 16-28722

About the Research

This report is based on a global survey of 400 senior executives at investment organizations, conducted by Longitude Research on behalf of State Street in October and November 2014. We asked respondents to self-assess their confidence and progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and governance. The respondents were drawn from 11 countries, with the majority from the US, China, France, Germany and the UK. The full survey sample is split evenly between asset managers and asset owners.

Half of the asset management firms represented use mostly equities and fixed-income strategies, and the other half pursue highly diversified (36 percent) or alternative asset (14 percent) strategies. Among asset owners, half of the respondents are from insurance companies, 11 percent from private pension funds, and the remainder from a variety of funds and other institutions*. To complement the survey, we conducted in-depth, one-on-one interviews with investment industry executives and other experts. All data featured in this report is derived from the State Street 2014 Data and Analytics Survey conducted by Longitude Research, unless otherwise noted.

*For sector-specific views of this research, please visit http://www.statestreet.com/ideas/articles/innovators-journey.html.

For more information on the trends identified in this research, email: [email protected]

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