This document discusses why Master Data Management (MDM) projects often fail and the implications for big data initiatives. Some key reasons for MDM project failures include a lack of enterprise thinking and executive sponsorship, weak business cases, treating MDM as an IT solution rather than business solution, unrealistic roadmaps, and poor communications planning. The document argues that establishing a data governance strategy, enterprise reference architecture, and prioritized project roadmap are important for MDM and big data success.

1. WHY MDM
PROJECTS FAIL
AND WHAT
THIS MEANS
FOR BIG DATA
ENTITY WHITE PAPER

2. ENTITY WHITE PAPER WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA
INTRODUCTION
There’s no doubt about it – the data universe is expanding at
a dramatic rate. Big data will affect every company, regardless
of size. Big data presents both an enormous challenge and an
enormous opportunity to those companies intent on extracting
value from their information.
According to IDC’s Digital Universe study, the digital universe will double approximately
every two years between 2012 and 2020. This is an intimidating prospect, considering that
80% of all data currently in the digital universe was originated in the last 2 years alone.
Gartner predicts that enterprise data will grow 8 fold in 5 years and that 80% of it will be
unstructured; while structured data continues to grow at a Compound Annual Growth Rate
(CAGR) of 20%.
Furthermore, IDC suggests that only 0.5% of the digital universe is currently analysed;
competitive advantage awaits those companies that succeed in mastering, analysing and
governing their information.
The convergence of several key industry factors is influencing the origination of this data:
the cost of information storage is reducing; mass market adoption of mobile technologies
(smartphones, tablets) means their users are generating lots of unstructured data; machine
generated data is on the rise; cloud adoption is increasing for both business and personal
use; and virtualisation is becoming commonplace within IT architectures.
If organisations are intent on extracting significant value from their data, then they must
first build the foundations for treating data as an enterprise asset.
Big data initiatives run the risk of failure because the foundations of information
management including a consistent enterprise reference data architecture, reference data
management, master data management (MDM) and information lifecycle management
are not in place. In each case organisations are attempting to gain insight and value from
information; Big Data is a larger, scarier version of the same problem.
In light of the fact that 80% of the world’s data was created in the last two years, it is
reasonable to ask whether organisations have progressed dramatically in managing data
in this time, whether they are gaining significant insight from their own internal enterprise
data, and whether they are ready for exponentially increasing volumes of data? Bluntly, in
each case, the answer is no.
Organisations are, however, starting to put their houses in order in preparation for Big Data.
The reasons are clear - if an organisation can truly learn to govern its data across the
enterprise, if it can master information, gain insight and distribute that insight back across
the enterprise to create value, then its people, processes and technology will be better
placed to derive significant value and competitive advantage from Big Data. If it cannot;
it will not.
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3. Data governance, information management strategy, master data management, reference
data management and information lifecycle management, therefore take on greater
importance in preparing the enterprise for Big Data.
Given the potential benefits of getting information management projects right, it is
surprising that only 24 percent of 192 large organisations surveyed in 2011 about data
quality (by analyst firm The Information Difference) described their MDM projects as
“successful or better.” Evidently, a number of MDM programmes are failing to deliver
expected outcomes.
These statistics lead us to ask why MDM projects fail, and what organisations can learn
from their MDM projects for the Big Data challenges ahead? The probability of failure of
MDM projects increases because of a number of factors:
ENTERPRISE THINKING
By its very nature, an MDM initiative requires integration of the information from
different divisions, departments and systems across the enterprise. This involves each of
those divisional and department heads and the system owners subscribing to a single
corporate vision. In many organisations, the MDM initiative is the very first time that the
entire enterprise has to act together to achieve a common goal. It is often very difficult
for this group of people, each with their own parochial interests at heart, to agree on a
common objective and the roadmap to the wealth of benefits that can be achieved.
The realities of business mean that quite often data is defined at the business unit level, in
separate businesses prior to a merger, or at product level. This results in siloed information
strategies, siloed solutions and siloed data. While it is true that nobody starts from a green
field when looking at their data from an enterprise perspective, an effort must be made
when defining an MDM strategy to understand the viewpoints and needs of all of the key
stakeholders of business systems. Business owners will have their own projects, their own
resources and their own budgets that will colour their perspective.
In TDWI’s report on Next Generation MDM, 25% of 219 respondents had more than 10
definitions of customer (while a further 15% didn’t know) and 26% had more than 10
definitions of product (and a further 17% didn’t know). Our own experience working
with multiple global enterprise MDM initiatives more than bears witness to these findings.
The examples above beg the question whether organisations perceive the customer as a
customer of a department or of the whole enterprise; this underlines the need to change
the mindset of the organisation to start thinking and operating at an enterprise level, to
bring data together at an enterprise level and to start seeing the customer (and customer
data) as an enterprise asset.
EXECUTIVE SPONSORSHIP
Associated with the need for Enterprise thinking, is the need for effective executive
sponsorship. Somebody at the top of the organisation must own and care deeply
about the MDM initiative and expect significant return on investment through the
implementation of an enterprise solution. Again, our experience bears out this assertion.
In order for MDM programmes to be successful they require cross departmental thinking
and organisational change and therefore need C-Level buy-in and leadership. Without the
backing of senior management to make changes across the organisation and to start the
process of thinking at enterprise level then these projects will fail.
WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ENTITY WHITE PAPER
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4. BUSINESS CASE
As with any major business change initiative, a business case or compelling business driver
is essential for an MDM project to be successful. According to a 2010 survey by Information
Difference, only 60% of projects were progressed at that time with a robust business case.
Ultimately, all projects within an organisation are competing for resources and those whose
benefits are clearly understood stand more chance of progressing. Furthermore, those
projects without a business case are more likely to be cancelled or to be categorised as
failures, simply because quantifiable business outcomes were not defined for the project at
the outset. The probability of re-prioritisation of projects increases as organisations operate
through the current economic downturn.
Defining the business case for an MDM initiative is especially important as MDM tends to be
an enabler to future value rather than delivering direct business value itself.
The business case for MDM can be expressed in many ways including customer satisfaction,
cross sell, up sell, operational efficiency, improvements to strategic decision making,
regulatory compliance, data quality and governance. Whichever of these benefits you
ascribe to your MDM initiative, it is important to understand, document, agree and
continually measure, the value that each benefit has to which areas of the business and
when that value will be delivered.
MDM AS AN INFRASTRUCTURE SOLUTION RATHER
THAN A BUSINESS SOLUTION
This consideration is aligned with that of the business case above. An enterprise MDM
solution is an essential component of a well worked Information Management architecture
that enables an IT organisation flexibility and scalability to support changing business
priorities into the future. This is a good thing and often leads to comments from senior
executives like ‘the case for MDM is a given’. In this scenario, the implementation of MDM is
driven from an IT perspective, rather than from a business one. Whilst it is undoubtedly true
that MDM forms a cornerstone of an effective information management architecture, the
complexity of enterprise thinking and the need for business change to support it mean that
it must be driven from a business rather than an IT perspective.
Often, large companies attempt to implement multi-domain master data management
programmes in a single programme. They may use the same technological platform
(e.g. IBM Infosphere MDM or Informatica MDM) to master a number of business critical
data entities across departments, business units or functions. The technology chosen,
however, does not answer the reasons “WHY” the organisation is embarking on an MDM
initiative. The “WHY” is the business outcome that is expected from the programme. MDM
programmes should align to business objectives - the technology / infrastructure solution is
simply “HOW” you get there.
As long as an organisation allows technology to shape business decisions rather than the
opposite then the strategic goals and the business benefits hoped for from the MDM
initiative will never be reached.
ROADMAP
Too often, organisations attempt a “big bang” approach at mastering numerous data
domains across the organisation. They attempt to integrate multiple silos without really
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5. considering what data should be within the scope of the programme and when.
A properly defined information management strategy will identify an organisation’s optimal
roadmap for deriving the most business benefit, in the shortest timeframe, from its information
management projects. Quite rightly, in today’s economic climate, time to business value should
be a critical factor in prioritising each project. However, it is important that each initiative is
implemented within the constraints of an enterprise information strategy and reference data
architecture.
It is not uncommon for organisations to see the need to master customers, vendors and
prospects at different times and in different ways and therefore to treat them as distinct
projects and deliverables and then to discover that an important part of the business case is to
identify which customers are also prospects and vendors. If the overall roadmap and business
case were understood, then customer, vendor and prospect could be mastered as a single
domain ‘Party’ – still potentially implemented as separate projects but deriving increased value
as each is implemented over time.
Another important consideration is where to start? Don’t start your MDM initiative with a
simple domain that gives limited business value. It is a common mistake to start with something
technically simple, with a clear scope and limited impact. It is important, however, that the first
project delivers real value that can be heralded as a huge success across the organisation, and
that it proves the entire concept from a technological and infrastructure perspective.
COMMUNICATIONS PLANNING
While MDM enables joined up data and therefore thinking across the organisation, it is only
possible if the people working on the project communicate to make it happen. Often, MDM
projects will be implemented across functions, across product lines and across business units
– key stakeholders will often only understand their own individual information requirements
rather than cross-enterprise requirements. This inevitably creates blockers to the success of the
project, unless an effective communications plan is put in place to mitigate their concerns.
An effective communications plan must communicate the progress and successes of the
initiative, with all successes against the business case measured and quantified; successful
information management projects are more likely to gain widespread adoption across the
enterprise if people know about them.
BUSINESS CHANGE PROGRAMME
Master Data Management programmes cause change: to data, to systems, to business
processes, to people and to the enterprise. An organisation should map out their organisation
to identify the data, systems, processes and people affected by the initiative, and how they will
be affected.
This mapping should ask questions not only of existing systems, roles and departments but also
of future ones. For example - should data governance be centralised? Who owns the mastered
data post-implementation – the department or the enterprise? How does this change existing
processes? Where does the data stewardship role fit – it didn’t exist previously – is it a central,
enterprise role now? What changes need to be made now to existing systems to manage
changes to master data? How does this affect users?
If your organisation is not mapped out and these questions are not asked, normal business
operations will be disrupted and the MDM initiative will be dropped at the first sign
of resistance to change. Andrew White, Research Vice President of Gartner, identifies
organisational change as one of the primary barriers to MDM adoption. 5
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6. PRODUCT SELECTION / UNIQUE SKILLS
Information management is often misunderstood and is not a technical exercise; neither
is it a business exercise; it is both – and as such requires a unique set of skills for effective
planning, product selection and effective implementation.
According to TDWI’s Next Generation MDM report, 26% of organisations surveyed had
attempted a “homegrown” MDM solution while only 2% preferred that option in place
of dedicated MDM tools. Often such homegrown solutions were Proof of Concepts that
now require scaling across the organisation. MDM solutions have however matured far
beyond this into a comprehensive mix of data model, workflow, integration, authoring,
stewardship, matching, linking and survivorship. It is questionable whether a homegrown
solution could meet all of these objectives effectively. Given their sizeable investments in
R&D, made possible only because the solutions can be deployed with multiple customers,
only enterprise scale commercial solutions are likely to be effective long-term.
While these organisations were able to hand-code an MDM silo, a number of them will find
that they are unable to implement, govern and maintain MDM across the enterprise. Unless
you have the right people in place with the required blend of technical skills and business
understanding, your chances of successfully implementing your Master Data Management
strategy across the enterprise are negligible.
Understanding why MDM projects fail will help to mitigate these risks. The steps below
offer a practical approach for addressing these problems and for implementing MDM
successfully across a complex organisation.
INFORMATION MANAGEMENT / DATA GOVERNANCE STRATEGY
The purpose of the Information Management Strategy is to define an Information
Architecture and strategy that meets the needs of your business as it changes over time.
Once the strategy is understood and agreed, an optimal roadmap is identified for
deriving the most business benefit from your information management projects as they are
implemented incrementally - the objective is to quickly provide recommendations on areas
where possible improvements could be made based on strategic goals/drivers.
Master Data Management is an essential component of the wider enterprise information
management strategy. MDM is pivotal within an information architecture as it supplies and
maintains master data across enterprise systems.
Of course, any information management projects within your information management
strategy must each be supported by a compelling business case for implementation.
ENTERPRISE INFORMATION REFERENCE ARCHITECTURE
A successful Information Management solution architecture must enable master data to
be managed consistently across all people, processes and systems within the enterprise.
However it involves far more than just implementing a central repository of data. The
architecture and design approach should be based upon a well-defined set of configurable
components. These include:
An enterprise data model which standardises a consistent model of both reference
data and master data. It should provide a business glossary and consider both the
operational and analytical requirements of the enterprise.
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7. Information Lifecycle Management and Data Quality components to allow new master
data and reference data to be created, collaborated, managed and retired by the
enterprise in a consistent manner.
Data Stewardship components that allow data quality issues to be managed and Identity
Analytics components to detect potential duplicates within the data.
Data profiling components that measure and monitor data quality against objective
targets set by the data governance board.
Analytical components such as data warehouses to provide enterprise level query based
reporting and event based analytics to provide real time operational intelligence.
Content Management components to manage unstructured data and to cross link it to
standardised reference data and master data.
Security and Audit components to ensure that master data can only be accessed by those
systems and people that are authorised to do so.
Integration and connectivity components to enable information to be flowed easily and
quickly to the processes and systems which need it within the enterprise.
A number of relevant Enterprise Reference architecture patterns exist such as IAAS
(Information as a Service) and SOA (Service Oriented Architecture). These two examples
promote best practice integration principles such as consistent service reuse, flexibility and
loose coupling between systems. They lower the cost of system integration and provide a
platform for growth and change without requiring a restructure of the organisation and
its systems. Other important architectural considerations include providing highly available
services, rapid performance and the ability to scale the architectural components to support
the Big Data volumes of the future.
The enterprise architecture in many organisations has typically suffered from having to
respond to pressures of growth, business and technology change. MDM and associated
information management principles provide a unique opportunity to put a reference
enterprise architectural vision in place and to begin incrementally reducing the amount of
redundant information and systems within the business.
PROJECT PRIORITISATION AND ROADMAP
A ‘heat map’ process provides an objective mechanism to identify the information pains
within an organisation and then to prioritise solution delivery within the constraints of
effective information management strategy. It is an effective mechanism to derive and
manage a programme roadmap over a period of time.
This heat map enables executive level management to visualise the information maturity of
their data entities across the organisation. It will highlight which information management
projects should be tackled first and enables the organisation to create the optimum
roadmap for tackling projects incrementally with a view to deriving maximum business
benefit.
When considering master data initiatives it is inevitable that the provision of mastered
solutions for individual data domains (Customer, Supplier, Product, Part, Location, etc) will
have different relative priorities for different organisations. Prioritising the development
and delivery of these in the context of a wider information management strategy, 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 solution delivery.
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8. INCREMENTAL BENEFITS
The roadmap should allow for manageable scope within specific areas of the business
(e.g. the Customer Master) rather than attempting everything at once. The ‘how do you
eat an elephant?’ quip; answer - ‘one bite at a time’ is highly appropriate here. This
controlled focus should enable business benefits to be realised quicker, and lessons to be
learned by the organisation as it progresses projects incrementally along the roadmap.
This approach lays the foundations for information management project delivery. It
allows for a business case to be made for each stage of the plan and when each stage
is successful, against measurable and quantifiable benefits, then organisational change
is more widely accepted and trusted. This feeds the desire for and therefore speeds
the adoption of enterprise information sharing initiatives such as MDM, as long as
these quantified successes are communicated across the organisation. Approaching
your information management strategy with this “agile” approach vastly increases the
probability of success versus a more traditional “big bang” approach.
EFFECTIVE SPONSORSHIP
Effective sponsorship at the right level in the business increases the probability of MDM
project success. Executive level sponsors are more likely to fund projects that align with
the strategic objectives for the organisation. The likelihood of effective sponsorship
therefore increases when master data management projects help the organisation to
meet strategic goals. This point may seem to be a statement of the patently obvious,
but it is remarkable how many information management and MDM projects commence
without being effectively tied to business objectives and success.
Effective sponsorship, however, requires a lot more than being an advocate for an
information management programme and securing its funding. Effective sponsorship
requires that you lead 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.
COMMUNICATIONS PLANNING
Your roadmap will be designed to meet both business and data requirements from key
stakeholders throughout the organisation. This will also create the structure of your
communications plan informing key stakeholders how their business processes will be
affected prior to, during and post implementation of information management projects.
Regular status updates should inform key stakeholders of the progress of information
management projects along the roadmap, which benefits, both tangible and intangible,
have been realised, and all benefits should be evangelised to C-Level to help speed
Information Management adoption across the enterprise.
MEASUREMENT AND METRICS
The metrics used to measure the success of your information management projects
should be linked to the business drivers outlined in the business case(s) for the
programme.
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9. An important strategic goal, for example, might be increased revenue from cross-selling
and upselling. Key related metrics are therefore the improvements in customer data
quality and customer data integrity over time. Other strategic goals might include
staff efficiencies, for example a reduction in data entry processes; a key metric would
therefore be the number of man hours spent creating management reports. Key data
metrics should be included in management reports to business leaders whose strategic
goals are affected by them, so that they are engaged by ongoing data governance.
It is critically important to understand the metrics that report the efficiency of particular
business processes and to measure them before, during and after the implementation of
any master data or information management initiative. This is a key component of any
data governance programme.
Lastly, compliance to data policies, rules and standards (across business units) should
also be measured on a periodic basis to help focus the organisation on effective data
governance post information project implementation.
PROJECT AND PROGRAMME GOVERNANCE
A key consideration of MDM success is effective implementation project success; and
this can only be achieved with effective project governance. A properly governed
information management project should ideally contain the following elements:
A compelling, documented business case.
Agreed and documented business level requirements.
Unambiguous specification of project deliverables, agreed by all stakeholders.
Clearly documented projected scope.
A process for measuring that the completed project meets its original objectives.
Project sponsorship is in place, is appropriate and is being implemented effectively.
An effective project steering process.
The relationship between all internal and external groups involved in the project is
understood and documented.
Project stakeholders are identified, engaged and are communicated with effectively
at appropriate intervals.
Effective project management processes are in place.
Appropriate status and progress reporting mechanisms are in place.
Project review checkpoints and processes are in place to review that it continues to
meet its business, commercial and time goals.
Project documentation is recorded effectively and is held in a central, accessible
location.
Processes are in place for the effective management of project risks, issues and
changes.
Processes for the review of the quality of key project deliverables and of project
governance procedures.
Processes for conflict management.
A project governance approach such as this enables effective management of
information management projects and is repeatable as initiatives are progressed along
the roadmap.
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10. BUSINESS CHANGE PROGRAMME
The roadmap defined during the definition of your information management strategy will
have identified areas of the business that will be affected by the MDM programme. As long
as you understand how MDM will impact business units, systems, processes and people -
you will be able to define a business change programme to ensure that the programme is
successful. The complexity of this activity however should not be underestimated.
Furthermore, there will be organisational change to cope with as a consequence of
information management initiatives – for example a single view of customer might identify
an unexploited market opportunity that requires a new sales structure to capitalise on this
information which, in turn, might require the creation of new master data attributes.
SUMMARY
The ability to exploit the information within an organisation as an asset of the entire
enterprise is arguably the defining feature of the successful business of the future.
An effective information management strategy, of which master data management is
an essential component, is foundational to meeting the coming challenges of Big Data.
Competitive advantage from complex analytics and from Big Data is achieved through
building on a consistent information platform for the entire enterprise. This in turn can
only be implemented though a structured information management strategy and reference
architecture.
For any enterprise, large or small, getting from where they are now to this state of data
nirvana sounds like a huge task which is just too complex to undertake. This is not the case!
Through strategic planning, a structured approach to information management strategy,
sponsorship at the right level, prioritisation of delivery against incremental and measurable
business cases, understanding and managing business change, strong management and
constant communication, this elephant can be eaten and even enjoyed.
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 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.
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11. REFERENCES
IDC: The Digital Universe in 2020: Big Data,Bigger Digital Shadows, and Biggest
Growth in the Far East
http://www.emc.com/leadership/digital-universe/index.htm
Data Quality, Governance Critical to MDM Success, Loraine Lawson
http://www.itbusinessedge.com/cm/blogs/lawson/data-quality-governance-critical-to-mdm-
success/?cs=47414
Next Generation Master Data Management, TDWI
http://tdwi.org/research/2012/04/best-practices-report-q2-next-generation-master-data-
management.aspx
Building a Robust Business Case for High Quality Master Data, Information Difference
Whitepaper, Andy Hayler, February 2010
http://www.melissadata.com/enews/business-case-for-mdm.pdf
Gartner Says Master Data Management Is Critical to Achieving Effective Information
Governance, January 19th 2012
http://www.gartner.com/newsroom/id/1898914
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12. Entity House
980 Cornforth Drive
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KENT ME9 8PX
United Kingdom
www.entity.co.uk
For more information please contact:
James Wilkinson
Chief Executive Officer, Entity Group
james.wilkinson@entity.co.uk
www.entity.co.uk