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Why MDM projects fail – Data Management problems and solutions

Large numbers of companies encompassing multiple industries are now recognising how important product data is as an asset at strategic level. Clearly no longer driven by IT alone, the impetus behind a master data management project brings CDOs, CMOs, and other Digital Marketing roles into play.

Nevertheless, many initiatives for MDM end up failing to generate anywhere near what they set out to achieve. Below, we examine six of the most common reasons why MDM projects fail, and some best practices to avoid those risks.

Data management problems and solutions

A recent study by Stibo Systems and the Aberdeen Group noted that 45% of businesses are unable to locate their master data effectively. That means serious issues affecting business impact. When we pinpoint the reasons, the broad challenges connected to master data management are:

Siloed data – In and outside the organisation, multiple versions of data sources are used, as product data are maintained in several unconnected (and usually legacy) systems. The inevitable results are duplicates, data errors and organisational inefficiency.

Poor (or absent) data governance: data governance is next to impossible without a centralised hub. Not only does this absence prevent your organisation from fully meeting regulatory compliance and safety regulations, but it engenders a ‘free-for-all’ mentality when it comes to accessing, altering the nature of and storing this information.

Quality: Product information uses a lot of unstructured data. If you use inconsistent, incomplete, duplicated or erroneous data, it can cause problems throughout the business, including lack of compliance with key sales channels and marketplaces.

All of which leads to a LOSS OF TRUST: lacking the information to know which elements of your data are outdated or incorrect. Thus, throughout the lifecycle of products, traceability is low.

So, what are the solutions? Simply reverse the three problems above:

Ensure there is a unique version of each data point for everything in the company. Then, ensure that the unique version is stored centrally, and easily accessible in a role-determined manner.

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Six common problems

1. The ‘big Bang’ – An MDM project implementation in a hurry

The so-called ‘big bang’ approach to an MDM project is fraught with peril. A hurriedly planned transformation programme with enormous implications runs a high risk of failure in the execution phase – complex and critical issues inevitably arise and if these have not been addressed, the outcome is going to create more problems than n it solves. Cutting corners, going over budget, and failing to fulfil scheduled timelines, could even lead to a de-scoping of the project simply to deliver something.

However, the ‘big bang’ doesn’t happen in isolation. Its prevalence is usually the consequence of a series of other factors.

2. Lack of high-level sponsorship

Failure to enlist strong, C-level executive sponsorship will stymie the progress of a master data management project. MDM implementation must have consistent funding and support in place before the project starts in earnest. An executive with both a desire to champion the project as well as the power to make decisions provides that level of support and leadership. Of course, master data governance (more of which later) helps considerably, but a capacity and willingness to intervene is required, to ensure incidents are properly addressed and the project continues to move forward on expected timelines.

Furthermore, executive sponsorship is critical in two key areas:

  • At boardroom level, the executive sponsor will clarify, crystalise and drive forward the vision of what business value the MDM implementation will offer the company. They drive the impetus required on any transformation project to ensure timely delivery.
  • The tendency towards siloed data means that outside the boardroom, there are organisation-wide issues – competing departmental agendas, shifting priorities and even data hoarding. At a level of business process optimisation and engendering a data-driven culture, the sponsor is driving an agenda which demonstrates how working together will drive bottom-line results.

3. The absence of a fit-for-purpose data governance framework

Gartner, the renowned analyst firm, has noted that around 90% of businesses fail when first attempting to implement and maintain an MDM project. The main reason for this is a substandard data governance framework – namely, a lack of clarity in documenting processes alongside an inconsistent or incomplete set of rules-based enforcements.  

If the underlying purpose of master data governance is to put in place a rigorous set of checks and balances, that framework must be overseen by a ‘star committee’ of key stakeholders with a vested interest in ensuring the MDM project succeeds and achieves all its predetermined goals

4. Lack of knowledge

At the discovery stage (and throughout), the master data management project requires input from multiple decision-makers, SME’s, stakeholders and users. Nowhere is this more important than in the integration of the new MDM system with existing systems.

For the MDM project to successfully integrate with legacy systems, the partnership of consultants and solution providers need to discover and act on key information about these systems:

  • What data they actually use – how much, when and how often
  • What data is updated, how it is updated and how often
  • Who has the right to make changes to data – what change notice protocols exist, and how these are controlled and disseminated


In many cases, the answer could well be “we don’t exactly know.” Hence, what appear to be obvious good practices may be absent or unknown, creating a need for further clarification and imposition of rules (integrated into a data governance initiative). 

5. Inadequate (or absent) validation protocols5.

Linked to the above, MDM projects frequently go off track due to a failure to create sufficiently robust validation mechanisms for storing and disseminating master data. The consequences are inconsistent, incomplete and multiple versions of the same data – in a nutshell, bad quality data. Controls and automated validation protocols are the basis which ensures accurate, up to date and high-quality master data.

6. Failure to adapt business processes

At the end of the day, master data management is about business value. Integrating the best technological solution is all well and good, but without the requisite changes in business processes and workflows, it’s unlikely the project will result in optimal practices.

This is where external expertise is critical in guiding the organisation towards the realignment and restructuring of processes and workflows required to support successful MDM project implementation.

Master data management best practices

We have looked at the most common pitfalls in MDM projects as well as the impact they can have. We’ve also made brief mention of how to address these problems. But we can also outline the bases for best practices as a ‘philosophical’ underpinning for successful master data management project implementation.

They need time from the organisation’s SMEs because if there are several systems to deal with, critical information is locked into the project’s decision-making and action needs, be they business or technology oriented.  SMEs possess intellectual property, and this is critical to the successful delivery of an MDM project.

The agility of data modelling

The master data model used will mean significant differences in business operations. in today’s fast-moving digital environment, any MDM solution must be agile and adapt to the changes in complex systems. The technological tools certainly exist – the power of machine learning, AI and easily automatable processes and tasks are widely available nowadays. Therefore, any ambiguous or inactive master data model doesn’t solve the current problems it was intended to address. A fundamental tenet of an MDM project is, therefore, to define the layers of the data model.

That means addressing and developing the following:

  • Creation of the underlying data model
  • Definition of business rules
  • Specification of controls for data validation and quality control
  • Clarification of governance roles and security measures

Data Standards

An absolute must is to set the standard for master data in your organisation. It is also a challenging factor. Whatever standard set for master data should be fully aligned with and adaptable to all data types across the organisation. This standard should take place at the project planning stage.

High-efficiency data management through governance

Master data is amalgamated from various sources throughout the organisation. Both the meaning and use of that information must be closely defined, given that what looks like the same information from one department may well be interpreted in a different way in another. 

Data governance is absolutely key to developing enterprise data definitions which align with master data. 

Different businesses are at different levels of maturity when it comes to data governance frameworks. For those organisations whose data maturity level is behind the curve, the whole issue of data governance often appears to be an overcomplex and excessively time-consuming exercise. But, in trying to establish a common set of master data, it is a question of putting the business horse in front of the technological cart, and not the other way round!

Master data governance

Even if definitive data models and standards are in place, implementing an MDM project has further complexities. Governance is a vital element in terms of establishing robust policies and business rules to address these complexities. Essentially, it gives you a clear overview of all data operations throughout the process.

However, data governance should not be seen as a one-time data cleansing initiative. It needs to be an ongoing set of protocols and processes in identifying, measuring, and rectifying data quality issues in not only the source system, but also any other systems with which it interacts.

A data governance framework is certainly not something to be tacked on at the end of an MDM implementation project. It should be ideated and developed to a greater or lesser degree during the planning stage (depending, obviously, on the organisation’s level of data management maturity). This is an initiative with massive implications for all areas of the company, as it will to a large extent determine the viability of the overall business aims of the project.

Data Stewardship

So, inadequate governance means bad data quality, which, in turn, creates the risk of long-term problems for business operations. That is why data stewardship is fundamentally important for maintaining data quality. 

The key steps to consider are:

  • Clarifying who fills the various stewardship roles
  • Organizing the relevant tasks by these roles
  • Managing these tasks in relation to master data
  • Establishing rights and access for authoring and maintaining master data

The Power of a Single View

Organizations have acknowledged the benefits of bringing together all of their data from all of their disparate systems to maximize their data-driven problem-solving potential, identify new business opportunities, and increase the accuracy of machine learning models. This single view is not only used to power more accurate data analysis, but is also flexible enough to drive your operational business process.

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