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Executive
Summary
The reference data problem continues to confound
the financial services industry. High levels of
data siloization, multiple external vendors and
disparate formats and lack of standards have led
to poor quality reference data. This has stymied
trade processing automation efforts on the one
hand while also putting pressure on operating
costs because of highly manual error rectification
and excessive duplication. Further, the market
uncertainty and new regulation such as the Patriot
Act are driving the urgency to accurately identify
and manage risks with respect to clients and different
counterparties. Organizations spend up to 10%
of their IT budget on data management and still
find themselves fighting data quality problems.
The business case for reference data management
could not be more compelling. The top 5 lessons
from our reference data experience with several
brokers, investment managers and custodians are
as follows:
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A necessary
and critical first step in reference data
management is clean data. No amount of sophistication
in your technology solution is going to make
up for poor quality data. Continuing skepticism
from the users about the integrity and quality
of data will only result in a failed implementation.
Get the data right, first and foremost |
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While it is
theoretically correct to aim for enterprise
wide integration, it is practically more feasible
to build your reference data solution step
by step, prioritizing business applications
and data types with the highest business impact,
while keeping your approach and architecture
open and flexible for enterprise wide integration |
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Prioritize
securities data and client/counterparty data
for downstream business applications such
as clearing & settlement systems and exposure
management systems where the business impact
of reference data will be the highest and
where cost efficiencies will be realized rapidly |
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Use a combination
of build and buy and outsource to maximize
your gains. Pure off the shelf products need
heavy customization to fit with your organization.
ASPs remain a question mark on the critical
data sensitivity issue |
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While the core
application is best built to your specific
requirements, there are potentially large
savings to be realized from outsourcing the
data and exception management operations to
an offshore facility. Organizations employ
up to 200 FTEs for these operations and are
looking to save up to $10-12mn by outsourcing
data management. |
Reference data, rightly managed,
presents a significant savings opportunity while
also ensuring that your organization is equipped
to manage risk and comply with regulatory requirements.
Its time for every broker, investment manager
and custodian to put reference data on their list
of strategic technology initiatives.
The Challenge
Reference data, strictly defined,
is the static information on securities and entities.
In practice, the problem is often expanded to
include market data and operating data central
to risk management and management reporting.
Exhibit 1: Types of reference
data
| Securities data |
Entities data |
Market and operating data |
| Securities identifiers,
description, sector codes |
Counter party data - identifiers,
BIC codes, account numbers, settlement details |
Price data |
| Equity attributes - earnings
ratios, dividend rates |
Client data - account information,
product usage, credit profile |
Corporate actions notifications |
| Fixed income attributes
- payment schedules, coupon rates, call/put
features |
` |
Client portfolio and investment
accounting data |
| Historical prices and index
data |
|
Position and AUM data |
| Currency and country codes,
tax rates |
|
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Reference data is the life blood
of the trade process and used through the trade
processing cycle.
Exhibit 2: Usage of reference
data across the trade process
Source: Capco
The financial services industry
faces a fundamental yet complex problem in reference
data management. Even while trade processing automation
reaches new levels, inaccurate and inconsistent
reference data remains the single largest stumbling
block to internal STP. Equally significant for
a profitability challenged industry, are the burgeoning
costs of manually maintaining and updating data,
error rectification and maintenance of duplicate
and often redundant systems. Add to it, the risk
of underestimated or unknown exposure to clients
and counterparties.
From our experience with firms
across the industry, several reasons have contributed
to creating the reference data problem. The most
significant of them is data siloization - the
Tower Group Sept 2002 survey showed that firms
have 37 systems on an average containing the same
securities and counter party reference data with
8% of the firms having more than 150 systems.
These are most often disparate systems, geographically
distributed with no common formats or standards.
Duplication of data across these disparate silos
coupled with manual and inconsistent updates result
in multiple representations for the same data
elements making data quality a huge issue.
Adding to the problem are multiple
external sources of market data again with little
standardization between them - on an average data
is purchased from more than 7 different vendors.
Surprisingly this is an area where there is little
automation - manual updates are still the norm.
Eventually the dubious data quality leaves every
downstream user application with no choice but
to have its own data correction and maintenance
operation leading to high manual operating costs.
In this scenario, while most
firms are recognizing the need to act, they're
caught wondering where to begin.
The Approach
Typically, questions faced by CIOs looking to
start acting on reference data management are
in the following areas:
Exhibit
3: CIO Questions
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Should I aim
for an enterprise wide solution vs. a step
by step approach? |
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How do I prioritize
reference data types and user applications
and locations? |
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How do I evaluate
technology solutions from bespoke developers
vs. off the shelf products vs. ASPs? |
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How do I phase
out my investment and expedite the savings
and benefits? |
From our experience with brokers
and investment management firms, while it is theoretically
correct to aim for enterprise wide integration,
it is practically more feasible to get a buy in
from one or more business units and build your
solution step by step while keeping your approach
and architecture open and flexible for enterprise
wide integration. A very significant challenge
firms face for enterprise wide initiatives is
the need to centralize data administration. This
is an organizational challenge with significant
change implications for organizational structures,
hierarchies and roles. This is best dealt with
once minimum scale has been achieved and the benefits
and returns are more visible across the organization.
We have found it useful to undertake
the prioritization of reference data initiatives
based on two factors - where is the business impact
likely to be higher and where can I start realizing
the cost efficiencies sooner.
On prioritizing between types
of reference data, we believe securities data
is the foundation of the trade process and critical
to all business applications. We did find however,
that banks and securities firms in Europe are
focusing on improving their exposure and risk
management practices and therefore according high
priority to client and counterparty data. This
will rapidly become the norm in the US as well,
given the urgency created by Patriot Act and "know
your customer" regulation. Investment management
firms under heavy scrutiny from plan sponsors
are prioritizing portfolio and performance data
for more effective client and management reporting.
Build and
buy and outsource
The evaluation of technology solutions
appears complex on the face of it, but is actually
quite simple if we look at different components
of the solution. Each component of the solution
could be bought off the shelf, built or outsourced
and an optimal combination can be arrived at.
Solutions available in the market, however, seem
to be segmented traditionally by product, ASP
and service offering as follows:
Exhibit
4: Comparison of technology solutions
| Offering |
Approach |
Assessment |
Products
Off the shelf products such as FTI's Streetmap,
Asset control, Fame data manager etc. |
Proprietary data model,
validation tools and interfaces to data vendors
and standard architecture for interoperability
between applications. |
Inspite of off the shelf
components, it still requires heavy customization
and integration with internal systems. Integration
costs together with software license costs
renders this category 30-40% more expensive
than bespoke development. For the same reason
it does not offer significant time to market
advantages. |
ASP
Acdex from Asset Control, Cicada, Synetix
(erstwhile joint venture between Reuters and
Capco) |
Independent utility with
a standardized repository. Receive data from
a firm's internal systems and data vendors
and return a "golden copy" after
data cleansing and validation |
3 open issues limiting
the growth of this model:
1. While market data cleansing can be outsourced,
firms are still wary of data sensitivity when
it comes to internal trade related data.
2. ASPs need critical mass to be cost effective.
3. Limited functionality with respect to incorporating
the cleansed data within a client's internal
applications or exception management |
| Bespoke
Development (internal IT departments
still dominant) |
Bespoke development of
data model, interfaces with internal and external
data feeds, normalization and validation engine
and interfaces with downstream user systems. |
Data management has largely
been the preserve of internal IT departments.
While these solutions are comprehensive and
integrate well with the firm's systems, they
tend to be less effective on time to market.
Internal departments also lack the collective
experience from multiple implementations and
knowledge of integration tools and packages
that only a systems integrator can bring.
Several firms today are sitting on a bundle
of redundant applications, data models and
infrastructure. |
Source: Internal analysis
and research
Compared to these stand alone
options, we believe there is an effective solution
to be found through a combination of build and
buy and outsource.
Exhibit 5: Build and Buy and
Outsource
We believe that an optimal combination
of build buy and outsource should be leveraged
to realize maximum benefits as follows:
Build
Our case experiences with several firms lead us
to believe that the reference data repository,
the underlying data model and the integration
with upstream sources and downstream user systems
requires a very large element of customization
specific to your firm's technology architecture,
platforms and legacy systems. Most firms also
consider the reference data repository to be the
heart of the solution and a competitive differentiator
which is worth building.
Buy
Some components such as the data dictionaries
are industry standards to be complied with. Further,
the appropriate middleware, integration &
reporting tools and packages can be bought off
the shelf and integrated into the solution.
Outsource
Error rectification and exception management are
functions which firms are looking at outsourcing.
The exception management system would entail the
identification of incorrect data and rectification
through a team of data analysts. Outsourcing this
function delivers the twin objectives of cost
rationalization and superior service. The 24*7
offshore delivery model has made it possible to
deliver cleansed and validated data before the
start of the trading day while also eliminating
graveyard shifts for many an unwilling data analyst.
The systems integration approach
to a reference data solution is clearly the right
approach for building a robust solution at an
effective cost.
Summary
We believe the flip side of the reference data
management problem is the significant opportunity
it presents to FSIs to cut costs and manage risk
better. A solution covering different reference
data types and multiple source systems and user
applications can typically be delivered through
a "build and buy and outsource" model
with an investment of $5-10 mn. When it comes
to benefits, firms can expect 15-20% reduction
in trade processing failures. The cost savings
come from various fronts. We have seen mid to
large sized brokers and investment managers save
upto $10 mn per annum from retiring multiple repositories
and through lower manual processing and reduced
operating and people costs. Consolidation of data
vendors can shave off up to 25% of your data purchase
costs (An average firm spends $3-5 mn per annum
on purchasing reference data). Add to that the
lowering of operational risk with accurate assessment
and reporting of party wise exposure. That makes
reference data management a very worthwhile candidate
on your list of strategic technology initiatives.
This article is authored by
Kavita Iyer a Consultant from the Solutions Design
Group of Wipro Technologies. For more information
send a mail to info@wipro.com
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