The document argues that effective data governance is essential for the financial services industry, particularly in light of regulatory reforms and increasing complexity from mergers and acquisitions. It emphasizes the necessity of coordinating people, processes, and systems to manage data accurately, highlighting the benefits of trusted data for compliance and customer centricity. Failure to adopt effective data governance can result in operational inefficiencies, regulatory penalties, and loss of competitive advantage.

1. IS EFFECTIVE DATA
GOVERNANCE
A CHOICE OR
A NECESSITY
IN FINANCIAL
SERVICES TODAY?
ENTITY WHITE PAPER

2. ENTITY WHITE PAPER IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY?
INTRODUCTION
The global banking industry is experiencing a period of uncompromising change that will
permanently alter its shape, its ways of doing business, its winners and its losers.
“Flowing complete, accurate, timely data to the people and processes
that need it is the lifeblood of a modern organisation.”
Ambuj Goyal, IBM
Data Governance is the activity of co-ordinating people, processes and systems to ensure
that the information that powers an enterprise is accurate and trustworthy.
COST OF COMPLEXITY WITHIN BANKING SYSTEMS
While the banking industry was one of the first to adopt the concepts of Enterprise Data
Management, there is still a lot to do; effective Data Governance is a moving target and is
still a distant dream for the vast majority of enterprises.
In the last decade, the finance industry has witnessed constant mergers and acquisitions
as the market has consolidated. This adds to an already complex system architecture map
for most of these large organisations. Numerous legacy systems, a proliferation of product
centric silos and duplicated data results in costly manual intervention and a loss of control.
The integration of multiple systems over time has led to business rigidity rather than
flexibility. In 2011, IBM’s Institute for Business Value estimated the cost of complexity for
global banks to be $200bn.
REGULATORY REFORM
The 2008 financial crisis triggered an ongoing period of global and national regulatory
reform. Banks are subject to a bewildering array of regulation including AML, KYC, SAMA,
BASELII and Dodds-Frank and are fined huge sums for non-compliance. According to the
2012 Ernst and Young survey, “Progress in Financial Services Risk Management”, the scope,
timing and potential impact of regulatory reform was the top challenge noted by almost
75% of respondents.
Regulatory reform necessitates making risk “everyone’s business”. Modern Banking
is both “data driven” and “data dependent”. It logically follows therefore that the
effective management of the data in the enterprise should be everyone’s business. To
truly make it so and by treating data as an “Enterprise Asset” requires an effective Data
Governance programme.
Regulation relating to capital requirements in banking groups (also known as regulatory
capital or capital adequacy) will also create ripples along the information management
supply chain. Investment banking operations are being ring-fenced, and regulators require
that these operations report risk both independently and at group level. This will strain
banking systems’ architecture, integration, data quality and Data Governance processes.
Failure to provide regulators with the information they require, in the format they require, when
they ask for it, adds reputational risk to the plethora of risks already facing a modern bank.
2
IS EFFECTIVE DATA GOVERNANCE
A CHOICE OR A NECESSITY
IN FINANCIAL SERVICES TODAY?

3. Compliance is largely a costly and painful activity whose prize is the right to operate. From
a data management perspective, however, its side effects are hugely beneficial as trusted,
quality data that is available and usable in an appropriate form, at the point of need,
has major advantages. These benefits can be felt in all business functions, whether retail
banking, commercial banking, finance, risk, or marketing.
CUSTOMER CENTRICITY
Historically, the banking business model was built in product centric silos and paid no heed
to a holistic view of the customer relationship. Today, however, customers expect banks to
offer them the right products, at the right price, when they need them (e.g. at important
milestones in their lives), and wherever they choose to buy them. All channels, whether at
the branch, through customer service, online, or via mobile banking, should be incorporated.
This requires trustworthy, relevant, consistent and timely information about customers. An
enterprise level single view of customer data, spanning all aspects of the customer relationship
and including household and social data, is fundamental, to be able to offer the right product
combination, in real time, and to maximise cross-sell and up-sell opportunities.
Social media is changing the manner in which a bank communicates with many of its
customers. It provides the opportunity for retail banks to communicate with, listen to,
and build strong relationships with its customers. This improved interaction, however, will
also heighten customer expectations. Not only do customers want 24/7 banking from an
organisation that understands their particular circumstances but they also want immediate
answers to their key financial questions, and they will gravitate towards organisations
that can provide them. This will continue to fuel further rapid change in the industry. To
capitalise on the opportunities presented by social media, banks will need to include Big
Data in their Information Management and Data Governance strategies.
In a tough, mature market, with significant regulatory constraints, growth is reliant on
nurturing the right relationships with the right customers.
INFORMATION SECURITY & PRIVACY
Research conducted by the Economic Intelligence Unit has identified that IT Security is high
on banks’ list of priorities; this is understandable given the reputational risk associated with
security breaches and data theft. With data managed across a complex network of internal
and third-party systems, almost 90% of respondents in this report stated that failures have
severe consequences for compliance.
Furthermore, protecting personal data and preventing fraud is the minimum that customers
expect from financial organisations. The cost of getting these wrong is high in terms of
fines, loss of business and damage to the brand.
Effective management of each of the above issues is essential for banks to achieve
compliance and to retain and build competitive edge in this new era of banking.
Transparent data for regulatory reporting, customer centricity to increase uptake of
personalised financial products, trustworthy management reporting, and an enterprise view
of risk and liquidity are the pillars on which this competitive edge can be built.
The drivers for accurate and governed information at an enterprise level dominate today’s
banking industry. Right now, effective Data Governance within banking is not a choice, but
a necessity.
IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY? ENTITY WHITE PAPER
3
MANAG

4. WHAT IS DATA GOVERNANCE?
Data Governance is the activity of co-ordinating people, processes and systems to ensure
that the information that powers an enterprise is accurate and trustworthy.
All enterprises rely on information from many sources, both internal and external, to
feed critical business processes. Data Governance, if implemented effectively, can enable
better decision making, reduced risk, regulatory and legal compliance, enhanced sales,
enhanced customer satisfaction, business efficiency and reduced operational cost.
Effective Data Governance plays a pivotal role in deriving value from the data the drives
the organisation.
WHY DO DATA GOVERNANCE PROGRAMMES SUCCEED OR FAIL?
The probability of the success, or indeed the failure, of Data Governance programmes
increases due to a number of factors.
LACK OF AN IDENTIFIED BUSINESS PROBLEM
A number of Data Governance initiatives fail because organisations implement them
focusing purely on Data Quality without considering the context and scope of the business
problem. Data Governance is too often implemented for the sake of Data Governance.
Without a compelling strategic business driver that C-Level decision makers are aware
of and care deeply about, the likelihood of obtaining Executive Sponsorship for a Data
Governance programme is low. Furthermore, if the programme commences without clear
definition of its objectives, board level backing will be finite as other, better specified
projects compete for important resources; the programme will only progress if there are
measurable and incremental results against strategic business drivers.
It is therefore important to understand and define the specific business problems that a
successful Data Governance initiative will address.
In the main, in the banking industry, C-Level concerns relate to regulatory compliance, risk
management, revenue generation, operational efficiency, decreased cost, customer loyalty,
customer service and improved decision making.
A specific business need might be to improve the identification of credit risk with respect to
organisations with complex global ownership structures. In our experience, the foundation
of this is rooted in data quality, master data management, hierarchy management and data
governance principles. These contribute to customer onboarding and ongoing customer risk
management processes.
Alternatively, timely and accurate regulatory reporting is predicated on the ability to aggregate
data in a consistent and meaningful form across many business units and geographies.
Defining the business problems specify “WHY” an organisation is tackling Data Governance
and what outcomes are expected. Individual Data Governance initiatives should align to
business objectives – data quality processes, data stewardship, the technology utilised - all
relate to “HOW” you get there.
If an organisation allows process and technology to shape business decisions rather than
the business priorities driving technology and process, then the business benefits hoped for
from the Data Governance initiative will never be reached.
Conversely as a Data Governance programme incrementally tackles the identified business
problems, it gains support from the business functions and momentum to extend its scope
to additional areas.
4
ONS
ENTITY WHITE PAPER IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY?

5. OBTAIN EXECUTIVE SPONSORSHIP
A passionate belief that data is the lifeblood of a modern organisation is essential at a very
senior level if a Data Governance initiative is to be successful.
Somebody at the top must own and care deeply about Data Governance and expect significant
return on investment through its eventual implementation across the organisation. In order
for Data Governance initiatives to be successful they require cross departmental thinking and
organisational change and therefore need C-Level buy-in and leadership.
Effective sponsorship at the right level in the business increases the probability of success with
the Data Governance initiative. Executive level sponsors are more likely to fund projects that
align with the strategic objectives for the organisation.
In a banking context, one would expect the newly required (according to Dodds-Frank) Chief
Risk Officer to be an Executive-Level sponsor, given the impact that accurate data has on the
effective assessment of enterprise-wide risk. Support should also be forthcoming from other
C-Level Executives including finance, marketing, customer service, retail banking, corporate
banking and compliance, as each function is dependent on data to perform effectively.
Effective sponsorship, however, requires a lot more than being an advocate for Data
Governance and securing its funding. Effective sponsorship requires passionate leadership with
a vision for business change, that the project is funded, and that you make those responsible
for implementation accountable for realising the business benefits outlined in the business
case. This awareness of accountability at the early stages of the Data Governance programme
increases the probability of a successful implementation.
BUSINESS CASE
Executive-level sponsorship is more likely if the initiative is reinforced by a strong business case,
with quantified Return-on-Investment.
Executives must allocate resources to competing projects, and those with clearly identified
benefits and value have more chance of moving forward. Furthermore, those projects without
a clear business case are more likely to be cancelled or to be perceived as failures, simply
because quantifiable business outcomes were not defined for the project at the outset.
As with Master Data Management, Data Governance is an enabler of future value and its
benefits can be realised across multiple business functions simultaneously, for example, finance,
customer service, risk management and marketing. Example business cases might be expressed
in terms of customer satisfaction, cross sell, up sell, operational efficiency, improvements to
strategic decision making, or regulatory compliance.
In the current post-recessionary climate, projects that progress also tend to be those whose
value can be derived as a series of deliverables staged over time. ‘Big Bang’ initiatives where
all the value is at the end of a multi-year programme have proven to fail all too often and
therefore don’t even get off the starting blocks in the current climate.
It is important to document, agree and continually measure the value of each business benefit,
and when that value will be delivered.
CONDUCT A MATURITY ASSESSMENT
Before any Data Governance programme is initiated, you must first understand the maturity
of the organisation in terms of existing policies, practices and processes. An assessment must
also be made as to the desired/attainable future state (which may not be the perfect end state)
but represents a sensible vision for Data Governance. This high level Data Governance Maturity
5
IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY? ENTITY WHITE PAPER

6. Assessment will lead to recommendations in order to achieve a desired future state, and
these recommendations will need to be prioritised as part of a Data Governance roadmap.
The programme should deliver value incrementally and ultimately lead to Data Governance
maturity across the entire organisation. Data Governance should become part of the DNA of
the organisation and not a separate function.
Failure to understand the gap between as is and to be and failure to measure processes and
progress will inevitably lead to programme failure. Most organisations have an idea that
they are not where they want to be, but not where that is or where they are. They never
understand how to get there.
It is important that all of the stakeholders across business units are involved in the
assessment; ideally from business functions that will be improved by Data Governance,
identified when defining the business problem.
A Maturity Assessment should not be a one-off event. It should be conducted at regular
intervals, to measure progress along the Data Governance Maturity Curve for the data
relevant to the particular business issue being solved.
The results of a Maturity Assessment should be documented and presented to senior
decision makers, outlining next steps and priorities key to the success of the Data
Governance programme. This helps to ensure continued buy-in and sponsorship.
BUILD A ROADMAP
Rolling out an effective Data Governance framework and discipline across a complex
organisation is not simple, cannot be done overnight, requires buy in from multiple
stakeholders and involvement from teams across the entire business. For these reasons, it
is impossible to undertake a big bang approach to programme execution. Also, Executive
Sponsorship tends to be predicated on successful delivery on an incremental basis and
wanes rapidly if this is not forthcoming.
The business problems to be solved, together with the Maturity Assessment, allow the
identification of a reference architecture to support the strategic aims of the business. A series
of smaller, prioritised by business case, more tactical projects can then be defined within the
confines of an enterprise programme that deliver the strategy but in incremental units.
These early wins facilitate continued Executive Level sponsorship as success and measurable
Return-on-Investment give confidence that resources are being allocated appropriately.
The roadmap should highlight the types and timings of Data Governance projects that
deliver value on an incremental basis. Each project should stand to win its right to resources
based on the strength of its individual business case within the programme structure. Key
stakeholders should agree and understand which data domains will be governed (meta
data, reference data, master data (customer, account, product, etc)) and how implementing
policies and processes around them will impact and contribute value to the business.
Drawing up a heatmap presenting a two dimensional matrix of all of the ‘information pains‘
identified during the Maturity Assessment, with business value on one axis and investment
on the other, provides an objective mechanism to prioritise implementation. It is an effective
mechanism to derive and manage the programme roadmap over a period of time.
The Maturity Assessment enables Executive Level management to visualise the data maturity
of key factors including data quality, data security, data auditability, data consistency, data
accessibility and data availability across the organisation. Together with the heatmap, it
enables the organisation to create the optimum roadmap for tackling projects incrementally
with a view to deriving maximum business benefit as soon as possible.6
T SOLUTION
ENTITY WHITE PAPER IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY?

7. Prioritising the delivery of these Data Governance initiatives, taking into account the practical
considerations of resourcing service delivery, is not straight forward but leads to effective
planning and management and therefore minimises the costs and timescales of implementation.
The roadmap should also include, if it is not already in place, the creation of a Data
Governance Council, which will be made up of those key decision makers whose business
functions are affected by a lack of properly governed data. Within banks, depending on
the business problem, C-Level Executives from risk management, compliance, finance, retail
banking, corporate banking, customer service and marketing should consider participation.
Any initial and periodic successes should be communicated widely across the organisation
(as part of a structured communications plan) as this will facilitate the further adoption of
Data Governance across the enterprise.
CONCLUSION
This paper has sought to highlight some the pressures facing the financial community in
terms of compliance, security, privacy, customer service, market consolidation and business
centricity. These are recognised as many of the major threats and opportunities in the
financial world today.
We have sought to highlight that in each case, the management of these threats and
opportunities is exclusively down to the effective management and control of the data within
the enterprise as an asset of that Enterprise. Data truly is the lifeblood of a modern organisation!
For a financial organisation wishing to survive the pressures currently facing it and needing
to enhance its competitiveness and agility, it is safe to say that effective Data Governance
holds all of the cards. Effective Data Governance is not a choice, it is a necessity.
Having drawn this conclusion, however, the world is littered with failed, stalled or
ineffective Data Governance programmes that do not deliver value. This is largely because
the value and complexity of a change programme that touches the entire organisation is
not properly understood at the outset.
Success is achieved through hard work and a strong enterprise level programme, a
structured, iterative and agile approach with strong leadership and management, an
operating group, operating model, reference architecture, data architecture, data
management, data quality, data assurance, metrics and measures. Nobody said it would be
easy, but the benefits to a modern financial services organisation are immense.
Effective Data Governance is truly the key to business success in the 21st Century.
ABOUT ENTITY GROUP
Entity Group is an information management solutions specialist. Entity provides
independent consultancy, software solutions and services that exploit the value
of information and deliver competitive advantage to large scale clients across the
information management lifecycle; its services range from an information management
strategic review, through to analysis and implementation services for Big Data, data
modelling, information integration, master data management and analytics.
Entity provides information management services across the finance and insurance,
pharmaceutical, automotive, media and utilities industries.
Entity is committed to long term collaboration with our clients and partners, most of
whom continue to work with us over many years and multiple projects. In addition to
working directly with end-user organisations, Entity’s bespoke data management and
domain expertise often sees the company called in to solve unusual or highly-challenging
business data issues on behalf of global IT services companies and software vendors.
7
SOLUTION
IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY? ENTITY WHITE PAPER

8. Entity House
980 Cornforth Drive
Kent Science Park
Sittingbourne
KENT ME9 8PX
United Kingdom
www.entity.co.uk
For more information please contact:
Sam Thomsett
Marketing Manager, Entity Group
sam.thomsett@entity.co.uk
www.entity.co.uk