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    Vendor Profiles

    Amalto and MDM - April 2008
    Ataccama and MDM - August 2009
    Cadis - August 2010
    D&B Purisma and MDM - April 2008
    Data Foundations and MDM - August 2009
    Datanomic and Data Quality - May 2009
    Exeros and MDM - April 2009
    Experian QAS and Data Quality - August 2010
    Golden Source and MDM - April 2008
    Heiler and MDM - September 2008
    Hybris and MDM - September 2008
    IBM and MDM - August 2009
    Initiate and MDM - August 2009
    Kalido and MDM - August 2009
    Netrics and Data Quality - May 2009
    Oracle and MDM - August 2009
    Orchestra Networks and MDM - April 2008
    Pitney Bowes Software and Data Quality - May 2009
    QAD and MDM - December 2008
    Riversand - August 2010
    SAP and MDM - April 2008
    SAS and MDM - April 2008
    Silver Creek Systems and Data Quality - April 2008
    Siperian and MDM - August 2009
    Stibo and MDM - April 2008
    Sun and MDM - October 2008
    Teradata and MDM - January 2009
    Tibco and MDM - April 2008
    Trillium Software and Data Quality - May 2009
    Visionware and MDM - April 2008

    To see a sample profile click here.

    White Papers

    How to get high quality master data
    A paper by Cliff Longman published December 2009

    The idea of “Six degrees of separation” is that any human being can be connected to every other human being through six relationships at most. Data is a little like this: through data relationships, all data is connected. When it comes to managing shared data (the primary focus of master data), you should plan to include all data types within your scope because, ultimately, it is all connected.

    Cliff Longman has identified four different “strata” of data: transactional data (e.g. a payment is made), dependent data (e.g. sales regions), independent data (e.g. customer, product) and policy and reference data (e.g. categories of products, currencies) which have different characteristics. There are then four alternative strategies which ultimately end up with all shared data under management through a system of data mastering.

    The Master Data Management Endgame or
    How I learned to stop worrying and love federation

    A paper by Andy Hayler published April 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. This paper argues that there is a second architectural dimension that has not had enough attention paid to it, and that is the one of a centralised versus federated approach to MDM.


    Mastering Data: Beyond Customer and Product
    A paper by Andy Hayler published April 2008
    The master data management (MDM) market has seen a battle of ideas over the last couple of years between "cross domain" approaches and specialist hubs for separate classes of master data, particularly "customer" and "product". In this new white paper Andy Hayler charts this battle and discusses the types of MDM architectures which play out in large enterprises.


    Research Reports

    How Does Your Data Governance Initiative Stack Up?
    This Information Difference survey returns to investigate the relationships between data governance, master data management (MDM) and data quality (DQ). In 2010 we conducted an in depth survey on this topic against the background of considerable media focus on the crucial importance of data governance and data quality initiatives to ensure the success of MDM implementations. Now, six years on, we return to this area in an effort to once again explore and understand what progress has been made over the years.
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    How much are you spending on Data Management?
    Recent surveys of the Information Technology market have suggested an overall increase in expenditure in 2015. What proportion of this increase will be devoted to the area of data management (including data governance, data quality, data warehousing and master data management) in 2015? How do you compare with your peers?
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    Is the Data Warehouse Dead?
    Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
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    Data in the Clouds
    Cloud computing has recently received wide press attention, however there is still surprisingly little concrete information available regarding the state of cloud computing initiatives in business. In this survey we investigate the adoption of cloud computing in depth.
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    Big Data Revealed
    The Information Difference survey, “Big Data Revealed”, was conducted over the Internet during the period October to November 2013. Although Big Data has recently received wide press attention, there is still surprisingly little concrete information available regarding the state of Big Data initiatives in business. We believed the time was ripe to survey this area in depth.
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    The Adoption of MDM by Business Revisited
    In April 2008, the Information Difference conducted a survey into the take-up and adoption of master data management (MDM) software. In 2013, we revisited this area to understand what has changed over the past five years.
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    The State of Data Quality Revisited

    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. In this survey we revisit the topic of data quality and examine what has changed over the past four years.
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    Does Big Data Mean Big MDM?
    There has been a great deal of recent interest in the industry about how to deal with so-called Big Data. Despite this media attention there is scant concrete information on how organizations are addressing the issues underlying Big Data. How does this relate, if at all, to master data management (MDM), and what are organizations doing to consider the impact of the Big Data trend on MDM initiatives? This survey aims to answer these questions.
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    MDM Real-World Experiences
    Master Data Management (MDM) is increasingly becoming a mature technology and successful implementations have been the subject of recent media attention. There is, however, little concrete information reflecting real-world experience of its adoption and implementation. This survey addresses this gap.
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    MDM Market Research Survey 2012
    Master Data Management (MDM) is increasingly becoming a mature technology and successful implementations have been the subject of recent media attention. There is, however, little concrete information reflecting real-world experience. We were interested to better understand the current experience of those enterprises with live MDM programs or plans to deploy MDM, and the challenges that drive interest in MDM.
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    What’s the Link Between Data Governance and Success with MDM?
    It has often been said that data governance is key to master data management (MDM), but is it? In this survey we wanted to get feedback from customers who have data governance activities and MDM projects, to see to what extent the two really are linked. In particular, we wanted to identify best (and worst) practice in data governance and MDM.

    We conducted a survey, sponsored by Pitney Bowes Software, which was aimed at understanding better the views of businesses regarding their current data governance and MDM initiatives.
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    Return on Investment (ROI) from Data Management Projects
    Companies spend a lot of money on master data, data warehouse and data quality projects, but how successful are these initiatives in monetary terms? There is to date little information available relating to the return on these, often substantial, investments. In this survey, sponsored by Sand and Tibco, we explored how companies justify their expenditures, how they measure success, and how they assess their data management projects in monetary terms.
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    Data Quality and MDM - The Missing Link?
    Currently, master data management (MDM) and data quality are treated as separate markets, yet any MDM project has a significant data quality component. Many authors have highlighted in the media the crucial importance of data quality initiatives to ensuring the success of MDM implementations. There is, however, little information on the approach being adopted by organizations that have implemented or plan to implement MDM.

    We have therefore conducted a survey, sponsored by Informatica and Talend, aimed at understanding better the views of businesses regarding their current data quality and MDM initiatives.
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    How Reliable is your Data Warehouse?
    Data warehousing has been with us for almost three decades now. More than 25 vendors currently offer a wide range of approaches and software products dedicated to data warehousing. These range from packaged applications to cloud-based solutions and data warehouse appliances. We have recently reviewed this area in our Data Warehouse Landscape 2011.

    In the past, many data warehouse implementations gained the reputation of having a high failure rate, often delivering inconsistent data, with the consequence that businesses inevitably lost trust in their reliability. A root cause of this lack of reliability was that little or no attention was paid to ensuring the quality of the data loaded into the warehouse. Has the state of data warehousing in organizations improved over the past two decades?
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    Data Governance Benchmarking Survey 2010
    Data governance is gaining increasing importance and focus in the media, but how effective is it in practice? How does your organization stack up against others? The Information Difference, working together with The Data Governance Institute, has developed a framework for data governance that has been reviewed and vetted by an expert panel of multinational companies with well-established data governance activities. This report presents a detailed benchmarking survey into the various aspects of data governance across a wide range of organizations.
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    How Data Governance Links MDM and Data Quality
    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 therefore conducted a survey into the link between data governance, MDM and data quality.
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    The Link Between Data Warehousing and MDM
    "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. We have therefore conducted a survey into the link between data warehousing and master data management.
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    Master Data Management Projects in Practice
    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.
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    The State of Data Quality Today
    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.

    We have 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.
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    Styles and Architectures for Master Data Management
    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.
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    Impact of the Financial Crisis on MDM and Data Quality Initiatives
    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?

    At The Information Difference we have conducted a survey 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.
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    Adoption of Data Governance by Business
    Data Governance is the process of establishing and maintaining cooperation between lines of business 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.

    The Information Difference has conducted a survey into the take-up and adoption of data governance by business. 233 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. 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 best survey I've seen on the topic to date" Jill Dyché, Partner, Baseline Consulting
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    Growing Adoption of Master Data Management by Business?
    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.