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Growing Adoption of Master Data Management by Business?
A New Research Report from The Information Difference
Master Data is data that is shared between computer systems, such as customer, product, asset, location or contract. The management of this data is known as master data management (MDM). The mounting pressure on businesses to increase fiscal data compliance, accountability and transparency has driven a growing number of organizations to put a tentative toe into the waters of MDM. This has in part been fuelled by the explosion of publications in this area aimed at convincing businesses that the route to consistent business information lies in effective management of their master data. There is, however, scant concrete information relating to the motivation and adoption of MDM by business.

Against this background, The Information Difference has conducted a survey in April 2008 into the take-up and adoption of master data management (MDM) software. Data was collected for the survey from 112 participants with 65% representing businesses with revenues in excess of USD 1 billion. Some 56% were from North America and 24% from Europe. The purpose of the study was to gain understanding of amongst other factors the level of take up, the business motivation and the preferred approach to implementation.

What is the Take-up of MDM?
In terms of adoption we found that 29% of respondents have an MDM project in progress. 19% of companies responding to the survey have no active MDM project at present. 25% have completed an MDM project, and half of those now have MDM as an established, ongoing activity. 8% had tried an MDM project but abandoned it. 17% have MDM projects planned within the next two years and 2% have no plans.

Is There a Business Case?
There is clearly a strong business case for master data. The cost of incorrect master data is large, with only 14% of companies reporting that costs directly attributable to poor master data are less than USD 1 million per year. 21% of companies believe it costs them USD 10 – USD 100 million per year, with 6% participants reckoning that annual costs were over USD 50 million. The root cause of these costs can be seen since just 1% of companies had a unified source for their master data. The median number of systems holding customer data was six and for product data was nine, but 13% of companies have over 100 systems storing customer data, and 11% have over 100 systems storing product data.

What is the Preferred Approach to Implementation?
Of those planning to implement an MDM system, 47% intend to buy a package, 18% will build in-house and the rest are not sure. However 59% would prefer a unified platform that can deal with all types of master data ("cross domain") compared to just 14% who preferred hubs that specialise in specific data types such as customer and product.

The report has 20 pages.

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Adoption of Data Governance by Business?
A New Research Report from The Information Difference

The mounting pressure on businesses to increase fiscal data compliance, accountability and transparency has compelled businesses to realize and accept that ERP systems and data warehouses alone are insufficient to really tackle the problem of inconsistent, inaccurate and unreliable data. In particular, there is a growing awareness that the processes that create and update corporate data need to be addressed if the data dragon is ever to be slain. This involves understanding, documenting and controlling the business rules that surround the creation of new business classifications (such as a new customer code, a new product line or brand, an updated hierarchy of engineering assets or organizational structure). This is commonly termed "data governance".

Data governance is the process of establishing and maintaining cooperation between lines of business and management to establish standards for how common business data and metrics will be defined, propagated, owned and enforced throughout the organization. It is closely related to master data management (MDM), which is the management of data that is shared between computer systems, such as customer, product, asset, location or contract.

Although a growing number of organizations have put a tentative toe into the waters of master data management and data governance, there is scant concrete information relating to business motivation, level of maturity and adoption of data governance by business.

Against this background The Information Difference conducted a survey in August-September 2008 into the take-up, adoption and present level of maturity of data governance in business. The research addresses the following questions:

  • What plans have business to adopt and deploy data governance?
  • What is the current level of adoption of data governance by business?
  • How are these initiatives funded?
  • What is the nature of the data governance organization and how is it managed?
  • Who is responsible for driving data governance in business?
  • What does business expect from data governance?
  • What are the key business drivers, perceived benefits and roadblocks?


The purpose of the study was to gain understanding of, among other factors, the level of take-up, business motivation and the preferred approach to implementation.

The survey was sponsored by four vendors: Datanomic, Initiate Systems, Kalido and Silver Creek Systems and by media sponsors DM Review and Obis Omni.

233 fully qualified participants took part in the survey, 60% from companies with over USD $1 billion in revenue. 20% of respondents hold the job title of "Chief Architect" and 16% are CxOs or VPs. 64% were from North America, 20% from Europe and the rest elsewhere. 38% were drawn from the business and the remainder from IT.

Some key insights included:

  • There is clearly significant interest in data governance by businesses
  • To date data governance is relatively immature
  • The majority of businesses are failing to count the cost of poor data
  • Most businesses are not measuring data quality
  • Most data governance initiatives are currently focused on improving BI and reporting
  • A very clear requirement is to move beyond "Customer" and "Product" to encompass a broader range of (master) data
  • In general success with data governance initiatives is limited


The report has 46 pages.

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Impact of the Financial Crisis on MDM and Data Quality Initiatives
A New Research Report from The Information Difference
The recent financial crisis has had widespread impact on both individuals and businesses. It is against this background that a number of comments have appeared in the media urging businesses not to be distracted from their current IT plans but to focus even more on the areas that will bring growth. It seems entirely likely that consulting firms will be faced with delayed or cancelled projects and many software vendors will experience a slowdown in orders. But will most businesses realize that Master Data Management (MDM) and Data Quality (DQ) initiatives hold the key to delivering improved business information and therefore that it makes sense to pursue these initiatives even against the current economic background? In other words, are MDM and DQ immune from the crisis?

There is little hard information concerning the response of businesses to the impact of the financial crisis in this area. At The Information Difference we have conducted a survey, sponsored by Harte-Hanks Trillium Software, aimed at gaining greater insight into the views and plans of businesses regarding their current or planned MDM and DQ initiatives.

The objective of the survey was to explore the views of businesses on the impact of the financial crisis and "credit crunch" on their current and future plans for implementation of MDM and DQ.

92 respondents completed the survey from all around the world, the majority from North America (57%) and Europe (31%). Most of the respondents were from companies having annual revenues greater than US $ 1 billion and represented a wide spectrum of industries.

The key findings from the survey are summarized below.

  • 58% of those surveyed have implemented DQ, MDM or both (32%).
  • Of the remaining 40%,

--------o 23% have no plans to implement DQ or MDM in the foreseeable future.
--------o 42% plan to implement DQ and MDM in 1-3 years.
--------o 11% plan to implement DQ in 1-3 years.
--------o 10% plan to implement MDM in 1-3 years.

  • Of those who currently have implementations, an impressive 40% plan to accelerate the implementation, but 37% are undecided as to the best course of action.
  • Overall, of those surveyed, 31% reported that they were uncertain as to how to move forward.
  • Some 9% reported that they planned to delay implementation for a year.
  • On a positive note, only 8% proposed to put their implementations and plans on hold.
  • 50% of those surveyed agreed that in the light of the financial crisis implementation of DQ and MDM should be given higher priority.


These results taken together support the view that we are unlikely to see a major retrenchment in the implementation of DQ and MDM in the business community. We believe this is positive news for both vendors and end users/enterprises alike.

The report has 16 pages.

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Styles and Architectures for MDM
A New Research Report from The Information Difference
Master data management (MDM) has emerged as a key area of information management in the last few years. Initially much of the architectural debate was about whether to be data domain-specific or not, with vendors focused on providing customer or product-specific hubs. In the past couple of years the industry has realised that organisations want a uniform approach to all their master data and vendors have started to address this requirement. There is, however, still a significant divide about the style of implementation, with some vendors specialising in certain areas, and some confusion has arisen around terms such as "operational MDM", "registry" and "analytic MDM" used for various approaches to implementing MDM. Implementation approaches focus either on managing master data associated with business intelligence and reporting (termed analytic MDM) or managing master data associated with transactional systems (termed operational MDM). There has also been discussion of the use of federated approaches for MDM.

At The Information Difference we believe it is important for both organizations and vendors to understand how MDM is generally being implemented, so as to gain insight into the underlying reasons for the approaches selected and the available experience to date. We have therefore conducted a survey aimed at gaining deeper insight into the views and plans of businesses regarding their current or planned MDM initiatives, focused on the styles and architectures adopted or planned to be implemented.

Some 188 respondents completed the survey (sponsored by Microsoft™) from all around the world, the majority from North America (59%) and Europe (20%). Most of the respondents were from companies having annual revenues greater than US $ 1 billion and represented a wide spectrum of industries. The responses were split between two groups - those that had already adopted MDM and those planning to do so.

The key findings from the survey are summarized below:

  • Fully one third of organizations have already adopted MDM and a further 32% plan to do so within three years.
  • There is a high diversity of data domain types in the two groups with an average of five data types being/planned to be managed by MDM. These mostly include, but are not limited to, Product and Customer. Less than 15% in both groups were focused on a single data type.
  • Around two-thirds of organizations had implemented (or planned to implement) using a single hub/database for MDM. Surprisingly a significant portion (20% for those already having MDM and 25% of those planning to implement) had opted for a federated MDM architecture - mostly following their organizational structure (line of business/business unit) rather than geography.
  • Encouragingly, two thirds reported that their current or planned scope was enterprise-wide.
  • Of those who had already adopted MDM, 23% had adopted analytic MDM, 37% operational MDM and a further 33% both. A similar trend was found for those planning to implement MDM. Over half had implemented analytic MDM, based on the need to improve their management reporting.
  • Reported success rates were high and respondents generally considered their implementations "somewhat successful" (60% for analytic MDM and 63% for operational MDM). Significantly around a quarter of respondents told us their implementations had been "very successful" (26% for analytic MDM and 31% for operational MDM). Less than 6% reported that their implementations had had little or no effect.
  • For around a third of organizations the size of the MDM hub was between 1 and 2 million records. Overall sizes reported ranged from 20,000 to 25 million records.
  • The majority of those already having MDM implementations had elected to use the co-existence model (28%) closely followed by the Consolidation (see page 6 for definition) model (22%). Surprisingly, as many as 17% had chosen the potentially more challenging Transaction model. Among those planning to implement there was no clear preferred option.
  • The average cost of implementation was about US $ 7 million with a median of US $ 3.5 million. The corresponding figures for annual maintenance of the MDM systems were a mean of 8 FTEs (Full Time Equivalents) with median value of 4.5 FTEs.


The report has 30 pages.

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The State of Data Quality Today
A New Research Report from The Information Difference

The topic of business data quality has been with us for decades. Given the large number of vendors offerings dedicated to resolving data quality (DQ) issues, one might be forgiven for believing that the problems have all but been resolved. A glance through the current literature reveals, however, that the problem of poor data quality is still very much alive.

Has the state of data quality in organizations improved over the past two decades? Or is data management, and data quality in particular, still in a ghastly state with organizations and senior management feeling that the problem is overwhelming? Despite the clear concerns from business and a plethora of software vendors, there is surprisingly little concrete information available regarding the state of data quality in business.

In June 2009 we conducted a survey, sponsored by Pitney Bowes Business Insight and Silver Creek Systems, aimed at gaining deeper insight into the views of businesses regarding their current or planned data quality initiatives.

Some 193 respondents completed the survey from all around the world, the majority from Europe (47%) and North America (44%). A high proportion (39%) of the respondents were from companies having annual revenues greater than US $ 1 billion; respondents represented a wide spectrum of industries.

Some of the main findings from the survey are summarized below:

  • One third of respondents rate their data quality as poor at best and only 4% as excellent. Fully half considered their data quality as good, although this may be somewhat over-optimistic when set against other results from the survey. For example one respondent told us “Poor data quality and consistency has led to the orphaning of $32 million in stock just sitting in the warehouse that can’t be sold since it’s lost in the system.”
  • 63% have no idea what poor data quality may be costing them.
  • Surprisingly, 17% have no plans at all to start a data quality initiative, compared with 37% who currently have some form of data quality initiative in place. The remainder plan to introduce data quality in the next one to three year period.
  • Some two-thirds plan for, or currently have, data quality spanning either the entire enterprise or one or more lines of business.
  • A remarkable 81% say that their data quality is focused wider than just “name and address” yet this latter is the area in which most (>90%) vendors currently have their base!
  • The top three data areas for DQ were ranked as: 1) product data; 2) financial data; 3) name and address data. It is interesting that financial data occupies second place but virtually no current DQ vendors specialize in this area. Product data is rated as a higher priority than customer name and address, yet only a few data quality vendors specialise in dealing with product data quality.
  • The top two barriers to adopting data quality were: Management does not see this as an imperative and It’s very difficult to present a business case. This is interesting given that the majority (63%) have not attempted to calculate the cost of data errors.


The report has 33 pages.

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Master Data Management Projects in Practice
A New Research Report from The Information Difference

Master Data Management (MDM) has received growing attention recently as an essential component of information management alongside data governance and data quality. Alongside this growth in interest in master data management, the provision of services for the implementation of master data management is featuring with increasing prominence in the portfolio of services offered by many Systems Integrators (SIs).

While many SIs currently claim or suggest they have extensive implementation expertise in master data management, there is little concrete information available regarding the use of systems integrators by end-user organizations for implementing master data management programs in business. We have therefore conducted a survey of both end-user organizations and systems integrators aimed at gaining deeper insight into the levels of expertise, experience and usage of systems integrators specifically related to undertaking MDM implementations.

Some 131 respondents completed the survey from all around the world, the majority from North America (47%) and Europe (30%). A high proportion (42%) of the respondents came from companies having annual revenues greater than US $ 1 billion. The respondents represented a wide spectrum of industries.

The study throws up some fascinating results, including showing the amount of effort that is needed to maintain MDM systems, how much effort you should budget for to deal with data quality issues, and what are the main lessons from current project practice. The report also shows just how happy companies really are with the systems integrators they use for MDM projects.


The report has 57 pages.

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The Link Between Data Warehousing and MDM
A New Research Report from The Information Difference

"Analytic MDM" has become established as one of the styles of MDM implementation adopted by businesses needing to effect a significant improvement in the speed and quality of their business reporting, often centered around one or more national, regional or enterprise data warehouses.

This is unsurprising since the "dimensions" of a data warehouse are essentially master data (e.g., hierarchies of products, customers, locations, etc.). Despite the close relationship between MDM and data warehousing, a glance at even the recent literature on these topics reveals that these two important areas tend to be treated as entirely separate.

 At The Information Difference we were interested in exploring the linkage between master data and data warehouses and to understand the scale, scope and success rates of MDM and data warehousing initiatives in business. We have therefore conducted a survey into the link between data warehousing and master data management.

208 respondents completed the survey from all around the world; the majority from North America (57%) and Europe (27%). Over half the respondents (53%) came from companies having annual revenues greater than US $ 1 billion. The respondents represented a wide spectrum of industries.

Amongst other things the study reveals that almost half of the organizations surveyed have one or more data warehouse and MDM implementations.


The report has 38 pages.

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How Data Governance Links MDM and Data Quality
A New Research Report from The Information Difference

Many authors have recently highlighted in the media the crucial importance of data governance and data quality initiatives to ensure the success of MDM implementations. There is, however, scant information on the approaches being adopted by organizations that have implemented or plan to implement MDM, or indeed those who have chosen not to implement data governance.

We were therefore interested to explore the linkage between data governance, master data and data quality. In particular, to discover how organizations are tackling this area in practice. Additionally, we wanted to understand the scale, scope and success rates of data governance in relation to MDM and data quality initiatives in business.

We have conducted a survey to examine the link between data governance, master data management and data quality. A total of 257 respondents from across the world completed the survey. 56% came from North America and 26% from Europe. Fully two thirds of the respondents were from larger organizations having annual revenues greater than US $ 1 billion (62%). The respondents were drawn from a wide spectrum of industries including banking, finance and manufacturing.

A key finding from the survey is that 31% have already implemented data governance and have had active data governance implementations for a median of 2 years. A further 40% plan to implement within one year. Further, a significant number of organizations (39%) are electing to implement data governance alongside MDM (and data quality).

The report has 51 pages.

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