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Styles and Architectures for MDM

$850.00

Styles and Architectures for MDM

A New Research Report from The Information Difference – March 2009

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.

The Adoption of MDM by Business Revisited

$850.00

The Adoption of MDM by Business Revisited

A New Research Report from The Information Difference – July 2013

In April 2008, the Information Difference conducted a survey into the take-up and adoption of master data management (MDM) software. Now, in 2013, we revisit this area to understand what has changed over the past five years. In particular we addressed the following:

  • How many companies have implemented MDM?
  • How much effort are organizations investing in it?
  • How successful have they been?
  • What benefits are they seeing?
  • Which tools are being used to help?

A key objective of the study was to understand the extent to which the adoption of MDM has progressed in the past five years since our initial study.

Some 108 respondents from around the world took the survey, 66% from organizations with annual revenues of over $ 1 billion. 54% were from North America and 28% from Europe. A broad spectrum of industry sectors was represented.

The report has 22 pages.

The Link Between Data Warehousing and MDM

$850.00

The Link Between Data Warehousing and MDM

A New Research Report from The Information Difference – April 2010

“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.

The State of Data Quality Revisited

$850.00

The State of Data Quality Revisited

A New Research Report from The Information Difference – April 2013

Although the issue of data quality has been with us for decades now it still remains an area of concern and debate. In 2009 we conducted a detailed survey of the state of data quality across enterprises. Given the substantial investments in tools and support, has the state of data quality and data management in organizations genuinely improved over the past four years? Or is data management generally still in a ghastly state with organizations feeling that the problem is overwhelming? Despite a surfeit of tools vendors (more than 30), and much press attention, there is still surprisingly little concrete information available regarding the state of data quality in business. In this survey, which was sponsored by SAP, we revisit the topic of data quality and examine what has changed over the past four years.

210 respondents from around the world completed the survey. Just over half of the respondents (53%) were from organizations having annual revenues greater than US$ 1 billion. Respondents represented a broad spectrum of industry sectors.

The report has 40 pages.

The State of Data Quality Today

$850.00

The State of Data Quality Today

A New Research Report from The Information Difference – July 2009

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.