MDM Assessment

Business Application Research Center

Master Data Management:

An Assessment and Outlook

A white paper about the significance and status of enterprise

Master Data Management and Data Governance initiatives

in Germany, Austria and Switzerland

Wurzburg, March 2008

Companies often implement Master Data Management initiatives without knowing which factors

influence their success. In addition to exploring the concept of enterprise Master Data Management,

the following white paper assesses the maturity level of companies in Germany, Austria and

Switzerland and advises on common implementation scenarios on the grounds of empirical research.

http://www.barc.de/

BARC – Business Application Research Center

2 Master Data Management: An Assessment and Outlook

Authors

Dr. Siegmund Priglinger

Analyst and consultant

Business Application Research Center – BARC GmbH Steinbachtal 2b 97082 Wurzburg Germany +49 931 880651-0 [email protected]

Dirk Friedrich

Analyst and consultant

Business Application Research Center – BARC GmbH Steinbachtal 2b 97082 Wurzburg Germany +49 931 880651-0 [email protected]

As an unbiased market analyst, BARC has written this white paper without any influence from third

parties. DataFlux has sponsored this white paper so that it can be distributed free of charge.



Table of contents

Executive summary ............................................................................................................................. 4

Framework of the empirical research project ..................................................................................... 5

Master Data Management as an independent discipline ................................................................... 6

Determining maturity levels as the foundation for Master Data Management initiatives ............. 12

Maturity levels in companies ............................................................................................................ 15

The maturity level of MDM products ................................................................................................ 21

BARC stands for neutrality, competence and quality ....................................................................... 22



Executive summary

Master Data Management (MDM), known in the German language as

Stammdatenverwaltung, covers various topics in the field of Data Governance.

MDM deals with business processes and fragmented core data within companies.

These along with Business Intelligence (BI) issues are the main catalysts for MDM

initiatives.

Without formal business rules and discipline in handling data, companies will only

experience limited benefits from an MDM implementation. Since MDM is a phi-

losophy implemented through technology, companies must address organiza-

tional as well as architectural issues.

Almost three quarters of the survey participants feel that corporate performance

is at a high risk when no MDM initiatives are in practice.

Maturity level models are an important resource for an organized implementation

of MDM initiatives.

People, corporate policies and technologies are three important success parame-

ters for MDM initiatives. Soft factors such as people and policies have a stronger

influence than hard ones like technology.

Companies must implement MDM initiatives as a holistic approach. There are

strong correlations among people, policies and technology factors.

The maturity level of the companies surveyed is consistently low for all factors.

Companies, solution vendors and consultants need to focus on positioning MDM

as an independent discipline. Before the affected departments receive lasting

support for these initiatives, senior-level management must first realize that

MDM is a critical factor of success. This is necessary to fully leverage the data’s

potential.

Software solutions for MDM are divided into two categories: specialized vendors

with a limited range of products and solution vendors with a wide range of offer-

ings. Since solution vendors have typically extended their spectrum through ac-

quisitions, their solutions often suffer from compatibility issues.



Framework of the empirical research project

In order to gain substantial insight on the market status, BARC conducted an empirical survey in the

fourth quarter of 2007. Using a questionnaire, BARC asked employees in companies throughout

Germany, Austria and Switzerland to assess various topics relating to the field of Master Data Man-

agement.

Methodology

42 of the survey participants attended the BARC MDM Conference on 31 October 2007 in Frankfurt.

An additional 145 contributors were notified through targeted newsletters and related Web sites. A

total of 187 people completed the survey. Participants having no personal experience with Master

Data Management projects were omitted from the final analysis. Those who completed the ques-

tionnaire in less than four minutes were also excluded, since it is impossible to answer the number of

questions with any thought in such a short time. The following analysis, therefore, is based on the

responses of 146 people.

Demographics

The participants in this survey primarily work for large corporations and enterprises in the manufac-

turing, banking, high-tech and telecommunications industries. The revenue breakdown of these

companies is relatively homogeneous among the clusters. 20 percent of the companies surveyed

ranked in the less than €73 million range, 22 percent in the €73 through €730 million range, 28 per-

cent in the €730 million through €7.3 billion range and 30 percent in the more than €7.3 billion

range. Almost two thirds (62 percent) of the participants have management positions; the same per-

centage has an IT-related position.



Master Data Management as an independent discipline

Although Master Data Management has been a hot topic for quite some time, the term itself is only slowly becoming common in Europe. MDM is a very comprehensive field covering data integration, data quality, data consolidation, the reconciliation of customer and product data, metadata man-agement and other related topics. All MDM solutions focus on a company’s most precious asset: the information in its operational and analytical systems. Without discipline and business rules for han-dling data, however, companies will experience limited benefits from an MDM implementation. Since MDM is primarily a philosophy implemented through technology, companies must address organizational as well as architectural issues.

Terminology

The topics and terminology related to Master Data Management initiatives have taken shape in re-cent times:

Master data refers to business objects or situations that are used for identification, classification and characterization purposes and are stable over a long period.

Through MDM initiatives for managing and administering master data, companies can:

Save key information about essential master data

Update master data centrally

Transfer data changes quickly and automatically to the target systems

So Master Data Management is an integral component of a company’s strategy and architecture for administering enterprise data and managing internal and external data flow.

The concept of MDM varies greatly in the Anglo-American and European markets due to the differ-ent origins of this topic. The driving factors in the USA and Canada were (and remain) everything that adds customer insight. The product view, however, dominates throughout Europe, particularly in its German-speaking countries. This can be attributed to the different traditions of the two continents. The focus of MDM, however, is evolving into a process view in which customer satisfaction is the main objective. Companies, therefore, must align their MDM measures accordingly. Figure 1 illus-trates this relationship between data and business processes.



Figure 1: Master Data Management as the foundation for business processes

This process view is very different from a Service Oriented Architecture (SOA), the solution that IT is

currently pushing to solve its problems. In general, SOA experts only address the topic of data inte-

gration on a side note. But the databases contain fragmented, persistent data. However this fact is

either ignored or viewed separately in the service mindset. Yet, the condition and use of the data,

especially master data, are still major impediments in improving business processes. During the

process flow, important data are often not available at the right time and in a consistent format. This

phenomenon is very common in call centers as well as service or sales departments with close cus-

tomer contacts.

Catalysts

In the past, companies often implemented MDM solutions to address problems with the flow of data

among transactional systems. The initiatives often began in the IT department to improve handling of

fragmented enterprise data. Today, most MDM initiatives start as part of Business Intelligence pro-

jects and finance or senior management often initiate them. The centralization trend associated with

Data Governance is a clear driver of this development.

The operative and strategic processes now are more tightly bundled, and data analysis is based on an

increasingly reliable data layer. This data layer is the foundation for the different user views. The

claim that MDM can generate a common view of data is incorrect. Companies can determine how

data is produced but not how users extract it from the central data layer or how they use it. Hetero-

geneous data extraction leads to different views of the data. The advantage is that employees can

manage their area of responsibility in real time. This concept, better known as Performance Man-

agement, is currently a key driver of the MDM movement.



Differences in MDM and BI methods

Both Master Data Management as well as Business Intelligence (BI) use a central data source with

consistent data. The “how” and “why” behind this synchronizing process is decisive. As a central data

layer in Business Intelligence systems, a Data Warehouse merely supplies these applications with

data from the connected sources (Figure 2).

Figure 2: Technical layers of a Business Intelligence architecture

The central data layer of an MDM solution, however, serves as a hub for synchronizing data from

operational as well as analytical applications. In the data flow of an MDM solution, all applications

serve a dual role as the source and target.

Most current MDM initiatives focus on synchronizing customer or product data. Although this is an

important step, companies will have to find a way to integrate the organizational and financial mas-

ter data required in Business Intelligence systems. This will pose a major challenge for large enter-

prises. Integrating operational data, with links to other master data such as quotations and con-

tracts, is even more complex.



Modern Master Data Management

In the past, the main implementation scenarios for MDM initiatives were the migration and the syn-

chronization of master data across all types of applications. The prime goal was improving the quality

of the respective master data.

Operational and analytical Master Data Management

Today’s MDM initiatives generally emerge from requirements gathered in Compliance and Perform-

ance Management projects. Since both disciplines interface with multiple operational and analytical

applications (Figure 3), companies must synchronize the hierarchies and relationships in the master

data in terms of the normalized (i.e. snowflake) or star schemas in the databases. This covers the

fields of Financial Consolidation, Financial Data Management as well as Planning and Budgeting.

Figure 3: Operational and analytical Master Data Management

Implementation scenarios for Master Data Management

The different MDM implementation scenarios influence both the flexibility for end users as well as

the complexity of the application. The scope of an MDM solution comprises building blocks for data

integration, data consolidation, data relations as well as collaborative processes.

The only way companies can securely manage the mapping to and from source systems is to choose

an approach driven by metadata. A good MDM tool, therefore, must be able to connect to a flexible

data integration tool.

Data consolidation, the classic form of MDM, centers on synchronizing customer, product and sup-

plier data into a single “golden record”. It also encompasses the administration of reference data

used in selection tables. Companies can and will need to automate many of these tasks due the sheer

volume of data. Since the software’s main task is specifying a system of rules, a task implemented by

users with a strong technical background, usability is not a key issue.



Another requirement for MDM solutions relates to data relations. Since data relations primarily af-

fect ties among business departments, business users should use an MDM solution. The software’s

usability and its handling of multiple time and structure versions are key issues for business users,

because the final application is the result of the way the user interacts with the automated rules. IT

and business experts need to collaborate to map specifications for business objects as well as data

relations with the MDM data model. The descriptions stored in the metadata tend to change slowly.

These requirements also demand a high level of software usability, for example, in switching be-

tween time and relationship levels as well as working with departmental data models.

Companies should also define their MDM activities through organizational rules or collaborative

workflow. There are roles and functions for each workflow participant as well as a set order of activi-

ties stored in the MDM system. To store business rules, companies need a workflow tool that sup-

ports:

Activity descriptions from the view of a single user

Links between the descriptions of the activities and the functions of the business users

Meeting these requirements and securing them through access rights also requires a high level of

usability from the standpoint of business users. These implementation scenarios are summarized in

Figure 4.

Figure 4: Implementation scenarios for Master Data Management applications



Commitment to Master Data Management

Enterprise data management is complex and requires a good deal of administration. Since MDM is

primarily a philosophy applied through technology, the implementation is an ongoing process. The

vast majority of the survey participants have confirmed the advantages of deploying MDM solutions.

The survey results show that companies which recognize the disadvantages of their MDM initiatives

do not use enterprise data management although their competitors do. Most also feel that compa-

nies are placing their performance at risk if they do not use MDM (Figure 5).

Scale from 1 (no risk) to 4 (high risk)

Figure 5: Performance risks for companies not using MDM (n = 146)

In this empirical study, almost three quarters of the respondents feel that companies who do not use

MDM solutions are placing corporate performance at risk (levels three and four). Almost one quarter

felt that the risk was high. This analysis confirms the visible market trend that Master Data Manage-

ment is an important discipline.

12

3

424%

49%

23%

3%



Determining maturity levels as the foundation for

Master Data Management initiatives

To ensure the maximum impact of their MDM initiatives, companies must first understand the cur-

rent state of their enterprise data management. Due to the complexity of this topic, they must con-

sider many different influences.

The purpose of maturity level models

Maturity level models originated in software development (CMMI) and IT Governance (COBIT). Ma-

turity level models help organizations:

Determine the status quo of factors such as people, corporate policies and technology

Develop processes over time to a state of excellence

A tried-and-true maturity level model includes the five stages:

1. Initial

2. Repeatable

3. Defined

4. Managed

5. Optimizing

As a company’s maturity level increases, the following areas should show continuous improvement:

Forecasting

Effectiveness

Manageability

The concept of using maturity level models to describe process improvements has spread into other

fields. In software, this includes sales, IT services as well as integrated product development.

Since MDM initiatives have no project character and are set up as long-term endeavor, they can also

profit from this model. MDM entails a group of processes that need to be implemented over time

and require collaboration from IT and business departments. Companies require strict rules and solid

structures to manage the permanent cycle of change and learning concerning the data content, ar-

chitecture and toolsets. This covers all MDM processes and technologies including:

Data Governance and Stewardship

Data Quality Management

Data Integration

Service Oriented Architecture



Enterprise data management refers to a set of business tasks that require strategic planning. Matur-ity level models help guide this strategy.

Implementing enterprise Master Data Management

While business departments are generally responsible for topics such as Data Governance, Steward-ship and Data Quality Management, IT is accountable for Data Integration and Service Oriented Ar-chitecture. To ensure the success and minimize the risks of MDM initiatives, companies should con-sider setting up a competence center, which is a common practice in BI projects. Since the content matter of BI and MDM projects are closely connected, it makes sense to manage both competence centers together or even merge the two. Once selected, the team will have to determine the matur-ity levels for people, corporate policy and technology factors. This is the beginning of the analysis phase and the first part of an interactive MDM optimization process (Figure 6).

Figure 6: Iterative MDM improvement process

In the analysis phase, the competence center will define changes, identify goals and success factors and outline possible solution scenarios. A multidimensional evaluation process will then determine the proper succession for this program:

Choosing the order of the MDM projects

Evaluating MDM projects by business value, costs, risks as well as internal or external skill sets

Coordinating other corporate projects and controlling mutual influences

efine Scenario,



This is followed by planning, a further part of the analysis stage in which the team will define special dimensions of MDM such as data/metadata profiling, solution architecture, processes and business rules for the selected projects. At this stage, it is important to define measures (KPIs) and their rela-tionships. This ensures that the success or failure of each individual activity is clearly visible and this insight can be used as part of an “act – plan – do check” approach to making decisions. The “act – plan” analysis step is then followed by the “do – check” improvement step. “Do“ refers to the execu-tion of the activities. The subsequent “check” monitors the operations in order to appraise the pro-jects and deliver feedback to evaluate the next planned MDM projects. This initiates a new cycle of analysis and implementation.

Master Data Management leads to Data Governance

The first MDM project should be viewed as an opportunity to establish program management in place of project management. The goal is to treat change as a permanent challenge and to define a Lifecycle Management for Data Governance.

The main objective of Data Governance and Master Data Management is to make best use of enter-prise data. The goal is to make steady improvements on the way to delivering the right data at the right place at the right time.

–



Maturity levels in companies

The main factors influencing MDM initiatives are people, policies and technology. We asked the sur-

vey participants to use a maturity level model to describe the current state in their company. First,

however, the survey tested the model for suitability.

Factors influencing data management

Figure 7 displays the relevance of the three main factors influencing data management. The weight

of each factor can vary from 1 (no influence) to 4 (strong influence).

People (n = 144) Policies (n = 143) Technology (n = 144)

Scale from 1 (no influence) to 4 (strong influence)

Figure 7: Factors influencing data management

The sample group ranked all three factors highly. This confirms the validity of the maturity level

model by stating that all three factors have either strong or significant effects on data management.

Only a marginal share of the responses indicated that any one of the three factors had no bearing at

all.

One can, however, recognize clear differences in the influence of each factor. The order of impor-

tance is people, policies and then technology. The majority (63 percent) of participants feel that peo-

ple have a strong impact on data management initiatives, while another 27 percent said their influ-

ence was above average. According to the survey results, policies are closely tied to the people fac-

tor. Although the test group assesses this influence lower than that of people, it is still relatively high.

Finally, the impact of technology as the third factor in the maturity level model ranked much lower

than the soft factors of people and corporate policies. Figure 8 illustrates these relationships more

clearly using the same data used in Figure 7.

123

4

12

3

41

2

3

4

3% 8%

27%

63%

6%

21%

36%

38%

4%

33%

47%

16%



4 = Strong influence 1 = No influence

3 = Significant influence 2 = Moderate influence

People Corporate policies Technology

Figure 8: The influence of the factors on maturity

Technology ranked highly for having a moderate (33 percent) as well as a significant (47 percent)

impact on MDM initiatives. Only 16 percent of the test group, however, felt that it has a strong influ-

ence.

The rationale behind this ranking lies in the nature of the influencing factors. The technological foun-

dation is important in implementing MDM initiatives. This enables the company to administer the

massive volumes of data and use them for various decision-making tasks. Technology alone, how-

ever, cannot deliver a significantly more value if the company does not have a common understand-

ing of its relevance or data semantics. Companies need to coordinate the processes associated with

enterprise data management and establish the concept of Master Data Management in their corpo-

rate culture.

Maturity level of data management

Companies should assess the maturity level of its people, policies and technology in projects belong-

ing to MDM initiatives. In this way that they can undertake targeted actions to balance the quality of

Master Data Management. Each category and influencing factor brings its own challenges, which

must be addressed one step at a time. The companies surveyed represent the entire scale from 1

(immature) to 4 (very mature) as shown in Figure 9.

63%

38%

16%3% 6% 4%

27%36% 47%

8%21%

33%



People (n = 145) Policies (n = 144) Technology (n = 145)

Scale from 1 (immature) to 4 (very mature)

Figure 9: Maturity level of data management

Considering the fact that all companies in the test group have MDM initiatives in progress, one can quickly observe that a remarkable percentage is not ready for these initiatives. Almost 60 percent of the companies assess themselves as immature or moderately mature in all three factors. Companies, therefore, often deploy MDM initiatives without a clear understanding of which factors are crucial to their success.

By comparing the breakdowns of the different charts, one can also see that the companies have a similar maturity level for the three factors. Studies show correlations among these levels. Companies that are mature in one area are also mature in the others. This provides empirical proof that the fac-tors are mutually dependent and that it is not enough to focus on a single factor. Simply purchasing software, for example, is not enough. To implement MDM successfully, companies must keep all three components in mind. The following pages will explore this from various points of view.

In order to determine the extent of the three influential factors, the survey also asked several indi-rect questions about the company. This makes it possible to identify possible reasons for observed maturity levels. This empirical study substantiates that the indicators discussed below influence the factors of the maturity level model. These indicators also reveal correlations among the factors. Many indicators, therefore, share similarities with more than one factor in the maturity level model.

Factors indicating the maturity level for the people factor

Figure 9 shows that half of the companies ranked the people factor as slightly mature and 12 percent as immature. The main reason is the low employee awareness for the topics of Metadata Manag e-ment and Data Governance. The people factor of MDM initiatives is mature when employees have fully grasped the value of accurate data and take on group responsibility for high-quality data. Before companies can achieve this goal, however, they will need to make steady improvements over time. The following analysis, therefore, will observe the causal connections of factors that build on each other.

The majority of the companies surveyed already understand the value of accurate data. Two thirds of those surveyed feel either strongly or relatively strongly that their companies as a whole understand this value. Only six percent of the participants felt that their companies have not yet identified the value of accurate data. (Figure 10).

1

23

4 1

2

34 1

23

4

12%

50%

33%

4% 22%

39%

34%

6% 15%

43%

34%

6%



Value of accurate data (n = 146)

Management support (n = 146)

Internal collaboration (n = 145)

Scale from 1 (not strong) to 4 (strong)

Figure 10: The relative strength of the indicators for the people factor

Executives generally support MDM initiatives when they understand the value of accurate data. As expected the overall value of accurate data was stronger than the overall support from management. Generally, there are two main trends concerning management support. While a slight majority (54 percent) feels they get little to no support from senior management, the slight minority (46 percent) says they get noticeable support or better. Every tenth company surveyed received strong support. When management has increased awareness for the value of accurate data, it logi-cally places more pressure on MDM initiatives. This step normally occurs before employees begin to see eye to eye on this topic.

The internal collaboration with data management, therefore, will increase proportionally to man-agement support. Many employees often ignore internal approval processes and do not cooperate enough to support enterprise Master Data Management initiatives. The majority (64 percent) felt that internal collaboration in their companies was either somewhat strong or not strong at all. A cor-relation analysis substantiates this relationship empirically without making a clear statement as to the cause and effects. The extent of the correlation, however, suggests this given causality. Under-standing the value of accurate data has a strong correlation with receiving management support. Management support, in turn, has a similar correlation to internal collaboration. Another noticeable, albeit weaker trend is the relationship between the value of accurate data the extent of internal col-laboration. Management must understand the value of accurate data and act accordingly, before there is any significant improvement in internal collaboration.

The maturity level of management support for MDM initiatives and internal collaboration is closely tied to that of policies. The central organization of data management is another indicator for the development level of company policies (Figure 11).

12

3

4 1

23

4 1

23

4

6%

27%

49%

17% 16%

34% 40%

10% 9%

55%

32%

3%



Centrally organized team (n = 146)

Rule based quality control (n = 145)

Scale of 1 (not strong) to 4 (strong)

Figure 11: The relative strength of the indicators for the policy factor

More than two thirds of the companies surveyed do not have an adequate or any centralized team that organizes data management. A competence team comprised of people from different depart-ments creates a common view of the data and define business rules for handling data. Centrally or-ganizing MDM initiatives has its advantages, which explains the relationship of the perceived matur-ity level for policies. One benefit is that rules are subject to more intense maintenance in order to ensure the data quality. Regular maintenance, in turn, has a high correlation with the existence of a centralized organization team. Generally, the tendency to perform rule maintenance for data quality purposes is not strong (54 percent). Nineteen percent of the companies apparently do not performany maintenance on business rules. The existence of a centrally organized team is not the only factor influencing the prevalence of rule based quality control. There are also ties to indicators from both people and technology factors.

Indicators for the maturity level of the technology factor

The indicators for determining the maturity level of technology show how important it is to view the factors for MDM initiatives holistically. There is substantial evidence of a correlation between the intensity of the technical indicators with the maturity level of people and policies. These indicators have not quite reached the ranking already achieved by the status quo for technology (Figure 12).

1

2

34 1

2

34

29%

40%

25%

6%

19%

54%

19%

8%



Quality of the process definition

(n = 146)

Security through automatization

(n = 146)

Scale of 1 (not strong) to 4 (strong)

Figure 12: The relative strength of the indicators for the technology factor

More than two thirds of the companies felt that the quality of the process definition and the system

security was either weak or only moderately strong. Only 59 percent, however, felt that way about

technology in general. As a whole, one can assume that companies often do not utilize the full poten-

tial of their technology investments.

The maturity level study clearly shows that a balanced approach is the key to the successful imple-

mentation of a complex, ongoing initiative such as MDM. Companies that overlook people and poli-

cies factors will not be able to make significant improvements to the technology and vice versa. Only

companies who develop all three success factors in balance will vastly improve their current state of

Data Governance and, at its core, Master Data Management.

1

2

34 1

2

34

25%

45%

25%

5% 16%

55%

24%

5%



The maturity level of MDM products

The MDM solutions currently available on the market can be categorized by implementation scenar-ios (Figure 4, page 10) as well as by complete and specialized solutions. Complete solutions encom-pass a suite of modules designed to cover a very wide range of applications. Specialized solutions are tools that focus on specific application areas. In order to extend their functionality, however, compa-nies must combine them with other products.

Companies can only determine the maturity level of a specific solution if they carefully compare the software’s functionality to their unique requirements. Mere classifications – for example, in the sense of the application layer in Figure 4 on page 8 – are hardly evidence that a product is suitable for a particular company. They can, however, serve as a first filter for narrowing the short list of pos-sible solution vendors.

In general, software vendors quickly integrate the knowledge gained in customer projects back into the further development of their tools. This makes the market very dynamic and prone to vendor buyouts or the integration of specific solutions as OEM components. This trend is apparent in the fields of data integration – where vendors added MDM modules to tried-and-true solutions – and data quality, which is often part of MDM systems.

The market for data relations, the most modern implementation scenario for MDM, is even more dynamic. This is complicated by the fact that experts with a business – and not an IT – background use these solutions and expect a high usability and comfort despite the complexity of the subject matter. As a result, highly specialized, local niche players will often emerge as global players almost overnight.

MDM solutions reach maximum complexity in combination with Business Process Management (BPM) components. In this scenario, the solution must meet requirements with regards to auto-mated and manual process execution. Very few BPM products can meet the combined demands. These two disciplines have emerged from the two historical approaches to BPM. Tools that provide advanced functionalities to visualize processes have already been on the market for some time. Due to the new technological possibilities in the field of process management, however, more and more vendors are integrating process automation functionality into their products. These relatively young tools are still struggling with the lack of technical standards and approaches, a problem which should disappear as the market matures. Master Data Management demands that BPM solutions can handle parallel processes as well as generate new ones ad hoc.

As a whole, vendors of MDM products will have to face a variety of challenges. The maturity level of MDM solutions, however, will increase with each additional challenge that they master.



BARC stands for neutrality, competence and quality

Neutrality

The Business Application Research Center (BARC) was founded in 1994 as a spin-off from the Univer-

sity of Würzburg’s Chair for Information Science. BARC is an independent institute and treats all

software vendors with the same objectivity. It does not accept fees for coverage in software evalua-

tions or commission for software recommendations. BARC also does not offer software implementa-

tion services to prevent internal conflicts of interest.

Competencies

BARC employees have assessed Business Intelligence and Data Management products and consulted

companies in this field since 1994. BARC analysts have vast market, product and implementation

insight. Their comprehensive, yet detailed knowledge of the latest market developments and the

offerings of all relevant software vendors is backed by years of dedicated market research and

hands-on product evaluations.

Quality

BARC consulting projects are highly efficient and offer the highest level of security for software selec-

tions. BARC reports provide competent market overviews for all applied areas of information man-

agement. BARC conferences and seminars offer a concentrated overview of key players in a specific

segment of the software market.

Conference

Master Data Management Conference

Held every autumn in Wurzburg, Germany

"Master Data Management – Developments, Trends and Best Practices" hosts an interesting mix of

German and international experts in the fields of Master Data Management, Data Quality Manage-

ment and Data Governance. Participants will have the opportunity to discover the latest market

trends and developments as well as learn from the first-hand challenges and experiences of special-

ists who have implemented and use MDM solutions.

www.barc.de/en

http://www.barc.de/

MDM Assessment

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