The Economic Value of Data: A New Revenue Stream for Global Custodians

•

Cognizant 20-20 Insights

The Economic Value of Data: A New
Revenue Stream for Global Custodians
Big data initiatives in the areas of cross-selling, digital experience and
operational agility can yield big payoffs for global custodians by boosting
revenues.
Executive Summary
With the commoditization of core services and the
impact of macro forces on interest rate regimes,
FX rate volatility, etc., traditional revenue streams
for global custodians have dried up. Custodians
are increasingly looking at new opportunities to
make up for these lost revenue streams.
Among the newer revenue streams upon which
some firms have embarked is to monetize the
large volumes of data assets they hold on behalf
of their clients to meet regulatory mandates – the
maintenance of which carries a major technology price tag for custodians. Leveraging this data
in aggregated form to offer time-series analysis, predictive analytics, business intelligence
and visualizations provides significant insights
to decision-makers at custodian firms and their
operations teams. This is made possible with
technology innovations in big data management
and analytics.
Many global custodians have come to realize
this and are now making significant investments
in big data technology to generate new revenue
streams. In fact, big data has emerged as one of

Cognizant 20-20 Insights | november 2013

the top investment themes in 2013 among global
custodians, as the following points attest:

“Northern Trust has set aside $1.7 billion in
2013, over the rolling three years to support
information delivery in the areas of asset
servicing and asset management. Although
reporting is considered a core proposition
of custody services, there is currently
very little value generated from the historical
information held by the custodians. It is possible to charge additionally for value offered
using this historical information through a big
data solution.”1
“StateStreet is preparing a new business on
information solutions called Global Exchange,
which is going to focus on delivering data and
analytics solutions to clients by 2014. They are
engaged with clients in discussing possible use
cases until November 2013. StateStreet spends
$80 million annually on reporting.”2
“BNY Mellon has been leveraging big data
in several key initiatives around enterprise
search, internally and externally focused analytics solutions and visualizations since late

2012. These projects are expected to continue
into 2014. Focus is on changing the user’s
experience, when accessing their e-commerce
platforms. “3, 4
The top big data investment themes global
custodians are undertaking include:

•

Data aggregation: Focus on integrating and
managing data from different sources, internal
and external.

•

Risk management: With the increase of investments in alternative asset classes, the ability
to generate exposures covering basic asset
classes and alternative asset classes, such as
private equity, real estate, hedge funds, etc.

•

Digital experience: Contextual user experience based on user profile and Web usage
analytics across various products in the
custodian’s e-commerce platform.

•

•

Operational agility: Time series analysis
of operational and client inquiry data to identify service patterns that can improve service
levels and proactive fault identification and
resolution.
Cross-selling: Significant investments are
being made to identify buying patterns and
perform peer client group analysis among
global custodians’ clients. This requires the
analysis of transactional data across different
lines of business to identify cross-selling scenarios.

Since big data initiatives focus on core service
offering differentiation through value addition,
we firmly believe that investments will help global
custodians deliver better revenue streams, with
more sustained return on investment, compared
with newer offerings such as middle-office and
collateral management services, where the gestation period is often elongated.

The Big Deal About Data
For global custodians, big data refers to the accumulation and maintenance of transactional data,
which is ever-increasing with the advent of new
financial products and high frequency trading.
This makes it a challenge for custodians to barely
meet current state reporting requirements with
conventional data management and analytics
solutions. Harnessing big data to generate insights

cognizant 20-20 insights

from usage-related information over time helps
firms to create differentiated and value-added
services effectively. This requires a very different
type of solution compared to the current state of
data management strategy.
Data aggregation and risk-management-related
big data investments are a logical extension of
global custodians’ current data management
strategy. While this offers immediate alternative revenue streams, they may not generate
long-term sustainable advantage because they
are easily mimicked and leapfrogged by fast
followers. For this reason, big data solutions
should evolve beyond transactional information
to become more contextual and user-centric to
focus on digital experience, operational agility
and cross-selling, through improved data management strategy.
According to Forrester Research,5 a holistic big
data strategy should leverage all types of data,
including:

•

Structured data from systems of record, which
remains important for decision-making.

• Unstructured

data, primarily from social
systems of engagement, which will help drive
the customer engagement process.

Based on the New Vantage Partners Big Data
Executive Survey,6 more than half of financial
services firms that participated felt that their
current big data solution is less than adequate
to meet their analytics needs. A holistic solution
could handle the explosion in data faced by global
custodians, not just from the conventional transactional data, but from unstructured (e.g., e-mail)
and social sources, a lot more effectively. More
than 80% of Fortune 1000 companies estimate
that nearly 50% of the data they handle arrives
in unstructured format.7 For global custodians,
this unstructured file-based data could originate from thought leadership articles and videos
shared via social media and research content, as
well as regulatory and agreement documents.
To generate desired value from big data, a
holistic solution should address the following
requirements:

•

2

Persistence: Large global custodians are
currently equipped only to meet the
reporting requirements and maintenance of

historical information for regulatory reasons.
As a consequence, the data that is needed
most for reporting is available on-demand;
data that is of little use, but maintained just
for regulatory reasons, is archived on slower
media, such as magnetic tape. This is not ideal
for analytics and business intelligence, where
data value tilts toward historical information,
in providing more samples for hypothesis and
trend analysis (see Figure 1). The data infrastructure to enable this, therefore, should also
be capable of managing current and historical
data in the same way, to improve accessibility
and relevance for different needs.

•

Completeness: One of the biggest challenges facing global custodians is the lack of
an enterprise data warehouse that provides a
complete and accurate customer profile. Even
in the current state, reporting is conducted in
silos across various business lines from different warehouses, requiring detailed inputs
from users to extract the right information.
Custodians are therefore adopting a variety
of approaches to address this. Among them:
offering enterprise portals that can aggregate
data based on predefined use cases. However,
such solutions do not address the real problem
of generating a 360-degree client profile and
are not conducive to support analytics-based
decision-making. While creating an enterprise
data warehouse to generate client profile
requires significant investments and business
sponsorship, the onus is on IT stakeholders to
demonstrate the value of consolidating data to
business stakeholders and secure coordination
on ownership and maintenance of this data.

Data governance focused on quality, ownership and stewardship is critical to maintaining
an enterprise data warehouse, which cannot
be achieved without business sponsorship.

•

Context: Context is multidimensional and is
exceedingly vital to deliver relevant information. Context is inferred based on multiple
sources of data – structured and unstructured.
» Structured: User profile and Web usage
analytics data is assembled to identify user
need by assessing prior interactions with
the application and inquiry analytics from
a centralized platform to manage all client
inquiries.
» Unstructured: Content from research
sources, blogs and other social media is
combined to reveal insights and to provide
a context to the structured information,
based on user interest.

The big data solution must be capable of maintaining and analyzing contextual data, which
helps in delivering a relevant digital experience to the custodian’s clients and to provide
predictive inputs to the firm’s operations team
to anticipate client inquiries and respond
proactively.

•

Visualization: Delivering contextually relevant information to elicit action requires the
representation of information through visually recognizable patterns. There is significant
research going on in this space to generate
sophisticated visualization patterns, as studies

Time Value of Data: Reporting vs. Analytics

Low

Volume of Data

Time Value of Data

High

Days
old

Weeks
old

Months
old
Age of Data

Reporting

Forever

Analytics

Source: Adapted from SGI Whitepaper: Time Value of Data.
Figure 1


Years
old

3

have determined that human beings’ ability
to perceive information through patterns is
far better than their ability to process large
amounts of numerical or text data,8 which is
typically encountered in big data analytics.
Visualization should also depend on context,
especially on the user profile, as different information could have varying levels of importance
to different users.

Delivering the Big Value
Once a big data strategy is defined, custodians must
then focus on execution. Key use cases around data
aggregation and risk management are already
in use by global custodians. These include:

• StateStreet

Private Cloud focuses on consolidating all of the clients’ information that
the firm manages in a data warehouse that
is available for on-demand access by their
custody clients.9

•

BNY Mellon Risk View consolidates risk reporting data from client systems, its proprietary
systems and third-party service providers
to offer an integrated view of risk exposure
across basic and alternative asset classes.10

These initiatives focus primarily on structured
data that is generated by custodians, asset managers and third-party service providers. Even
with structured data, however, challenges around
standardization emerge (see Figure 2). There
is significant information that can be gained by
leveraging unstructured data, via a holistic big
data solution.

Examples have emerged that illustrate how
holistic data solutions are being applied to
cross-selling, digital experience and operational
agility, where unstructured data from internal
and external sources is used to generate better
contextual insights. All of these use cases focus
on improving client service which is very important for overall client satisfaction, in line with a
joint investor survey by Chatham Partners and
Investment Metrics, from which client service has
emerged as the top parameter used to measure
client satisfaction, with 40% of the votes. Client
service delivery can broadly be classified as client
digital experience and operational agility, which
collectively accounted for more than 70% of the
responses in determining client satisfaction (see
Figure 3, next page). We have developed business
use cases that illustrate the big data value addition that could be generated in each of the areas
identified, for improving the client service delivery and thereby the revenues of a global custodian.
In order to improve operational agility, cross-selling of services and the client’s digital experience,
it is necessary that large volumes of historical
structured data, around transactions and usage
history, is available on-demand for analysis. In
addition, unstructured information should be
analyzed to provide qualitative and subjective
insights beyond the analytical information from
transactions and usage patterns.

•

Digital experience: One of the wish list items
that most investors/managers request from
managers/custodians is the ability to view a

Big Data and Cloud Service Offering of Global Custodians
Social Media
and Public
Information

Data Source

Custodian

Asset Manager
Proprietary

Third-Party
Service Providers

Type of Data
Generated by
the Source

Structured,
Standardized

Structured or
Unstructured

Structured, But Not
Standardized

Unstructured

StateStreet
Private Cloud

In Scope

Not in Scope

Not in Scope

Not in Scope

BNY Mellon
RiskView

Partially in Scope,
Limited to Risk
Reporting Data

Partially in Scope,
Limited to Risk
Reporting Data

Partially in Scope,
Limited to Risk
Reporting Data

Not in Scope

Holistic Big Data
Solution

In Scope

In Scope

In Scope

In Scope

Figure 2


4

Client Service Delivery: Emerging Priorities
Market/investment knowledge
of portfolio team
Clarity of investment reports
Problem resolution skills of client
service representative
Frequency of contact of client
Timeliness of investment reports
Ease of navigation of Web site
Level of preparation for investment
review meeting
Client service representative
understands my unique needs
Responsiveness of client
Reporting capabilities of Web site
0%

5%
10%
Client Digital Experience

Source: Chatham Partners

15%
20%
Operational Agility

25%

Survey base: 1,726 investors

Source: www.cognizant.com/InsightsWhitepapers/Asset-Management-Reinventing-Reporting-for-the-New-Era-ofTransparency-and-Compliance.pdf.
Figure 3

360-degree risk profile of all business engagements. This typically consists of structured
portfolio information from a data warehouse
and unstructured risk-related information from
research reports and blogs. The risk-related
information is analyzed for likelihood and direction of impact and is applied to the client’s portfolio. The risk factor with the highest weight
is visually highlighted in the tag cloud with a
large font (see Figure 4). Such a 360-degree
profile view of the client portfolio illustrating
the impact of risk factors is a value-added offering that can be charged back to clients.
Use Case 1: Risk tag cloud: Weighted value list
of text that is considered as a standard big data
visualization.

Use Case Description
1 List of risk factors aggregated from unstructured data.
2 Likelihood of risk and shocks to the risk
factors are identified based on sentiment
analyzer on data in third-party research
report, blogs.
3 Apply the likelihood of the risk events and
the shocked risk factors on the portfolio
holdings of the client to calculate the risk
exposure.
4 Size of the risk factor in the cloud would be
based on the risk Impact to the client.

Visualization of Use Case 1

Source: http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
Figure 4


5

5 Each tag allows drill down into portfolio view
for that risk.
Dependencies
1 Association of risk factors to specific
portfolios: machine learning.
2 Association of risk factor information sources
to the particular user: blogs, reports and
market databases.

•

Operational agility: Real-time dashboards are
often leveraged by operations teams to report
status to custodian clients. Typically, clients
tend to focus on exceptions and are interested
in understanding their operational impact on
other processes and portfolios as much as they
are on the resolution of the exception. Based on
the historical data maintained by custodians, it is
possible to pictorially represent the interconnections among the processes and portfolios that
are typically affected by a certain exception. The
likelihood of impact could also be emphasized by
the number of prior instances of such an impact.
This provides the clients with a view of the potential impact, thereby reducing inquiries, and also
provides the operations team an opportunity to
proactively look into the interconnections for failures and plan for rectification well ahead of an
SLA issue. As a consequence, SLAs could also be
improved, thereby up-selling better SLAs to custodian clients at a relatively lower cost.

Dependencies
1 Usage pattern of user monitored – accounts
accessed,
functions
within
accounts
accessed.
2 Analysis based on email threads and conversation logs between operations and clients.
3 Multichannel client-inquiry-related information.
4
Availability
of
enterprise
warehouse
consolidating all of the portfolios of the client.

•

Use Case 2: Interconnection views: Depicts
dependencies across different nodes to predict
failure causality in operational process.

Cross-selling services: Cross-selling is
accepted widely as an effective way to improve
revenues from the existing client base.
Cross-selling typically involves classification
of custodian clients into different segments
and comparing their footprints in terms of
business value per line of business, vis-à-vis
the segment’s average business values. This
offers a quick insight into additional services
opportunities. In addition to determining the
areas, a probabilistic view of selling additional
services can be generated using client data
of a similar profile within the segment. The
profile itself constitutes the client’s business
transactional pattern across various services
to accurately determine the probability of
cross-selling a particular service to the client. The heat map could also factor in current
macroeconomic and firm-specific news events,
thereby ensuring that a cross-selling offer can
be made in real time and is business relevant.
This will significantly help the sales team in

1 Alerts on accounts frequently accessed by
the user – represented by the nodes.
2 Alerts based on frequently viewed business
function for those accounts – corporate
action, reconciliation and performance – of a
different color.
3 Alerts based on other users accessing the
same accounts, potentially for the same
functions or different functions.
4 Alerts based on historical interconnections
between accounts and functions prone to
issues, thicker interconnections indicate
higher probability of impact in the other
account or function. Top reasons for failure
could be captured as well to provide the
operations team with insights on resolution.


Sources: (1) Financial graph: https://addepar.com/
technology/
(2) Global interconnection map: http://reports.weforum.org/global-risks-2013/section-seven-online-onlycontent/data-explorer/
Figure 5

6

identifying and prioritizing sales leads and
realize higher conversion rates.
Use Case 3: Correlation heat map: Gradient
cluster view of cross-selling possibilities based
on likelihood, when compared to an average
value (benchmark).
1 Identify the segment of clients (large pension funds, etc.) to be analyzed.
2 Identify the set of correlation variables
(AUM, list of services subscribed, etc.).
3 Identify the unit of measurement for volume –
number of accounts, number of transactions and revenue – wallet share, potential
revenue).
4 Define/calculate correlation likelihood based
on the segment and based on analysis of relation between the unit of measurement across
different correlation variables and comparison to a benchmark or average value.
5 Plot heat map for each client segments
(segmented on correlation variables of unit
of measurement) based on the likelihood.
Dependencies
1
Availability
of
enterprise
warehouse
consolidating all of the portfolios of the client.

2
Multichannel
information.

Client

Inquiry

related

3 Client profile information from Public and
Unstructured sources.
It is quite possible to identify several such use
cases in the areas of the client’s digital experience and operational agility to derive additional
revenue-generating opportunities. It is up to the
imagination of the global custodians to wield the
true potential of the data they hold.

Looking Ahead
Global custodians have already jumped on the
bandwagon to exploit the revenue opportunity arising from big data analytics, and rightly
so. Custodians can address the technology
investment required among buy-side and sell-side
participants, to leverage the business benefits
that big data analytics can deliver.
Similar to the analogy in the core services, where
custodians have passed the scale benefits to
their clients, custodians must find the same value
equation to justify their investments in big data
infrastructure. As such, we believe the business
opportunity arising out of big data analytics is
a win-win situation for both custodians and
their clients.

Likelihood of Cross-selling

Potential Revenue Opportunity ($ ‘000s)

Very
Likely

Very
Unlikely

500

400

300

200

100
65

70

75

Share of Wallet (%)
Size of Bubble represents current volume of business
Figure 6


7

80

85
Average Value

Footnotes
1

www.efinancialnews.com/story/2013-05-31/custody-banks-surf-data-wave.
www.moneymanagement.com.au/financial-services/2013/state-street-global-exchange-datatrading-needs.

2

w w w. i n f o r m a t i o n - m a n a g e m e n t . c o m /n e w s / b n y - m e l l o n - r e i m a g i n e s - b i g - d a t a - a n d collaboration-10023503-1.html.

3

4

www.americanbanker.com/btn/25_11/bny-mellon-aims-to-change-user-experience-1053870-1.html?zk
Printable=1&nopagination=1.

www-01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=IML14318USEN.

5

6

http://newvantage.com/wp-content/uploads/2013/02/NVP-Big-Data-Survey-2013-Summary-Report.pdf.

www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf.

7

www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.

8

9

www.americanbanker.com/magazine/123_1/state-street-remaps-technology-infrastructure-tobenefit-cloud-and-big-data-1055145-1.html?zkPrintable=1&nopagination=1.
www.bnymellon.com/assetservicing/riskview.pdf.

10

About the Authors
Sathyanarayanan Palaniappan is a Consulting Director within Cognizant Business Consulting’s Banking and Financial Services Practice, and is one of the practice leader within its global capital markets
consulting practice. Sathya has more than 14 years of experience in functional, process, operational and
IT consulting for capital markets clients and has extensive experience in IT strategy definition, process
redesign, product evaluation and implementing software solutions in the capital markets domain. He has
consulted on engagements across geographies, handling program delivery in North America, Europe, UK
and the Asia Pacific region. Sathya can be reached at Sathyanarayanan.Palaniappan@cognizant.com.
Lakshmi Narayanan, V is a Manager within Cognizant Business Consulting’s Banking and Financial
Services Practice. He has eight-plus years of experience in capital markets, information delivery and
asset servicing with top-tier banks in the U.S. and has a management degree from the Indian School of
Business, specializing in finance. He can be reached at Lakshminarayanan.v2@cognizant.com.

About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process
outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered
in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep
industry and business process expertise, and a global, collaborative workforce that embodies the future of work.
With over 50 delivery centers worldwide and approximately 166,400 employees as of September 30, 2013, Cognizant
is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among
the top performing and fastest growing companies in the world.
Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.

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