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Why Master Data Management Projects Fail and what this means for Big Data


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Understand the key success criteria and the pitfalls to avoid in the implementation of MDM projects.

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Why Master Data Management Projects Fail and what this means for Big Data

  2. 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. 2 WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA
  3. 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 3 MANAG
  4. 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 4 ENTITY WHITE PAPER WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ONS
  5. 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 WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ENTITY WHITE PAPER
  6. 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. 6 ENTITY WHITE PAPER WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA T SOLUTION
  7. 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. 7 WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ENTITY WHITE PAPER SOLUTION
  8. 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. 8 ENTITY WHITE PAPER WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA UTIONS
  9. 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. 9 WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ENTITY WHITE PAPER MANAGEME
  10. 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. 10 ENTITY WHITE PAPER WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA GEMENT SO
  11. 11. REFERENCES IDC: The Digital Universe in 2020: Big Data,Bigger Digital Shadows, and Biggest Growth in the Far East Data Quality, Governance Critical to MDM Success, Loraine Lawson success/?cs=47414 Next Generation Master Data Management, TDWI management.aspx Building a Robust Business Case for High Quality Master Data, Information Difference Whitepaper, Andy Hayler, February 2010 Gartner Says Master Data Management Is Critical to Achieving Effective Information Governance, January 19th 2012 11 WHY MDM PROJECTS FAIL AND WHAT THIS MEANS FOR BIG DATA ENTITY WHITE PAPER
  12. 12. Entity House 980 Cornforth Drive Kent Science Park Sittingbourne KENT ME9 8PX United Kingdom For more information please contact: James Wilkinson Chief Executive Officer, Entity Group