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Multi-enterprise Master Data Management for EIM

Discuss about the Enterprise Information Master Data Management.

In the past few years, there has been a massive increase in the volume of information that an organization deals with. It has become essential for the organization to dedicatedly manage and store such huge chunks of data on a daily basis. Enterprise Information Management also known as EIM is a term that refers to a business discipline that combines a number of concepts of enterprise integration, business intelligence technique and content management in order to streamline the information for better handling, storage and management of the same (Lam and Taylor, 2016). 

Gartner has explained the various technologies and concepts that can be utilized for the enterprise information management with the aid of the Hype Cycle which has been shown below.

Hype Cycle

Figure 1:Hype Cycle (Ronthal and Simoni, 2015)

Master Data refers to the core entities of a particular enterprise such as customer, suppliers, accounts and many more. Master Data Management (MDM) is a discipline that integrates business and IT to maintain the accuracy, uniformity and continuity of master data that is associated with a particular organization (Gartner, 2012). 

One particular source of master data comprises of three significant capabilities viz. an authenticated source of information, ability to make use of the information in a consistent manner and the ability to be at par with the transforming business requirements in terms of the master data management. An integrated collection of services can then be developed around the particular data piece in terms of the business processes and analytical applications. Efficient business execution is also enabled with the three capabilities that have been described. The authoritative and authenticated source of data ensures that the data is free from redundancy and duplicity of any sorts and is standardized in its structure (Dreibelbis, 2016).

There are a number of phases that are involved in the application of a multi-enterprise MDM technique and method.

  • The first step comprises of identification of the sources of data from all across the enterprise and external levels. These sources may consist of structured as well as unstructured data.
  • After the first step, the producers and consumers of the data are identified in order to understand the data sources in a better fashion.
  • Metadata about the master data is collected and analyzed by studying the data to discover all the attributes associated with the same.
  • Data stewards are then appointed to look upon the data that is collected and handle the various operations that are associated with it.
  • Data and information governance program is then implemented to make sure that the authority is granted to at the executive, judicial, legislative and administrative levels to correctly manage the data and all of its components.
  • A master data model is then developed at the multi-enterprise level that is designed on the basis of the consent of all the parties that are involved and incorporates the analysis results as well.
  • Specific toolsets are then utilized to create the master lists by performing a number of operations on the same. The tools are broadly classified in two categories as:
  • Customer Data Integration (CDI)
  • Product Information Management (PIM)
  • The infrastructure is then created followed by the testing of the master data by the usage of the tools and equipment that were selected earlier.
  • A maintenance process is then followed to maintain the additional activities that are involved (Wolter and Haselden, 2016).

Multi enterprise Master Data Management (MDM) fulfills the five essentials of managing the enterprise level data and information which are described below.

  • Data Migration

Master data comprises of data that is collected from a number of different sources and is migrated at one common location. This data is efficiently cleansed to remove all sorts of duplicities, redundancies and inconsistencies from all the data sets that have been collected.

  • Data Integration

Every organization has its own information architecture and methodology that is adapted. The Multi-enterprise MDM integrates the entire data sets in to one single unit that is compatible with the architecture of the organization as well.

  • Data Maintenance

Key Benefits

Data definitions are synchronized with the business objectives of the organization and the multi-enterprise MDM ensures that any change in the same is reflected correctly and accurately with the maintenance of consistency all throughout.

  • Data Quality Assurance

Multi enterprise MDM also consists of a number of quality measures in order to maintain the quality of the data.

  • Data Retention

Multi enterprise MDM puts a constant effort to manage the data by keeping the data that is essential and discarding the pieces that are no longer required.

  • Redundancy Elimination

Redundancy is a common phenomenon that is sure to occur in case of such huge volumes of data collected from a number of different sources. One of the key benefits of the multi-enterprise MDM is that the data redundancy is completely removed and coherence is maintained all throughout.

  • Master Data Edits

Master data edits are possible with the help of MDM and its associated features. It is often encountered that there is a change that is made in one copy of the data and the same does not reflect in other data parts and places. It leads to inconsistencies and discrepancies and can be extremely fatal. MDM makes sure that such a situation does not happen by updating the records through master edits.

With the help of clean and irredundant data, it is possible to have the best data analysis results which are otherwise not possible through the presence of duplicate and unreliable data.

  • Key Data Goes In

The users get the ability to take a decision on what data goes in to management and what data has to be discarded. This allows for fulfillment of the business needs.

  • Authoritative Data

MDM emerges as an authoritative source of information and reliable basis for all the kinds of business operations that are carried out.

  • Multiple Platforms

When there are multiple enterprises and businesses involved, there are bound to be multiple and varying platforms as well. MDM allows the data to be available and accessible from a number of different platforms such as cloud, physical, online and many others.

  • Flexibility

MDM also allows a great degree of flexibility since the operations such as changes, storage, modifications and likewise are extremely easy to perform and manage.

  • Easy Backup

Centralized data sources are utilized in case of multi-enterprise MDM to make sure that data backup is taken at frequent intervals and the same is kept secure in the event of a disaster of severe attack.

Usability and efficiency are the two prime requirements for the customers as well as for the business operators. Multi-enterprise MDM offers the best of both resulting in satisfied customers as well as happier employees.

  • Access Based On Roles

Useful and Effective Data Analysis

The access rights and privileges in case of multi-enterprise MDM is granted on the basis of the individual’s role on the master data. It allows better access management and also maintains the security and privacy of the data and information at the same time (, 2016)..

The second concept and technology that can be utilized for the management of enterprise information is through the use and application of the logical data warehouses. A logical data warehouse refers to the architectural layer that is placed on top of the regular Data Warehouse (DW) of the organization that comprises of persisted data. This layers allows the business executives and analysts to view the data in the warehouse store and at other locations in the enterprise without any need of relocating and transforming the data ahead of the view time. As it were, the logical data distribution center supplements the conventional center stockroom (and its essential capacity of data accumulation, change, and tirelessness) with capacities that get and change data, progressively (or close to it), in this way instantiating non-held on data structures, as required (Russom, 2016).

  • Repository Management – It is an essential component of logical DW by maintaining and managing the repository of data for an enterprise.
  • Data Virtualization – It allows the virtual integration of the data will all the components present in an enterprise.
  • Distributed Processes – There are a number of distributed processes that are present on the cloud and the same are integrated with the help of logical data warehouse.
  • Auditing Statistics and Performance Evaluation Services – The accurate amount of data governance and accuracy is allowed and maintained.
  • Metadata Management – Metadata from a number of internal and external sources is maintained and managed (, 2016).

There are a number of demands of EIM that are fulfilled by the incorporation of a logical data warehouse in the architecture and structure of an organization. These demands are as listed below.

  • Enterprise Agility

It is the ability of an organization to sense and acquire the changes that are necessary to be implemented and be successful in the attempt. Logical DW allow the enterprises to maintain an excellent level of agility as it provides a logical view of data that resides at allocations and levels in an organization.

  • Real Time

Real time integration is provided across all the boundaries and interfaces by enabling on-demand analytics through the logical data warehouses.

  • Single View

Organizations always look for a single and consistent view of all the information that is associated with it in terms of customers, suppliers, information itself and many more. Logical Data Warehouse provides the same with extreme ease and excellence.

  • Customer Satisfaction

The prime goal of an organization is to maintain and achieve the enhanced customer satisfaction at all times and the same is allowed through the use of logical DW by efficient management, storage and analysis of the data (Hensley, 2016).

  • Comprehensive view of all the critical data assets

It is easier for the organizations when they have a unified view of all of their data and the same is allowed with the help of the logical data warehouse. It also allows the handling and management of multi-structured and unstructured data with extreme ease.

  • Agility across all the enterprise levels

Increased Usability and Business Efficiency

All the new developments and changes in terms of data and its attributes can be easily handled with the aid of logical data warehouse. Real time querying and analytics is allowed with the help of logical DW with a flexible approach that it provides.

  • Cost Effectiveness

The features that are provided by the logical data warehouse are also cost effective in nature and optimized data administration is also enabled (, 2014).


Enterprise Information Management (EIM) is an important element that is necessary to be maintained by all the organizations. There are a number of technologies and concepts that have been introduced for achieving the task. Multi-enterprise Master Data Management (MDM) and Logical Data Warehouse (LDW) are two such disciplines that aid in the information and data management at the enterprise level. There are a number of benefits and advantages that are offered by these concepts in terms of flexibility, cleansed data, and single view of data, agility and cost effectiveness. These also allow the enterprise to achieve its basic business objectives in terms of the competitive advantage and customer satisfaction. There are also security mechanisms that are maintained in these two approaches which allow the business executives to maintain the data security and privacy along with the confidentiality, integrity and availability of the same.


Dreibelbis, A. (2016). Enterprise Master Data Management. [online] Available at: [Accessed 23 Aug. 2016].

Gartner, (2012). Master Data Management (MDM) - Gartner IT Glossary. [online] Gartner IT Glossary. Available at: [Accessed 23 Aug. 2016].

Hensley, N. (2016). The logical data warehouse: The new secret weapon for leading. [online] IBM Big Data & Analytics Hub. Available at: [Accessed 23 Aug. 2016].

Lam, V. and Taylor, J. (2016). Enterprise Information Management (EIM): The Hidden Secret to Peak Business Performance. [online] Available at: [Accessed 23 Aug. 2016].

Ronthal, A. and Simoni, G. (2015). Hype Cycle for Enterprise Information Management, 2015. [online] Available at: [Accessed 23 Aug. 2016].

Russom, P. (2016). The Logical Data Warehouse: What it is and why you need it. [online] Available at: [Accessed 23 Aug. 2016].

Wolter, R. and Haselden, K. (2016). The What, Why, and How of Master Data Management. [online] Available at: [Accessed 23 Aug. 2016]., (2016). Logical Data Warehouse | Cisco. [online] Available at: [Accessed 23 Aug. 2016]., (2016). The Benefits of Master Data Management - Flatworld Solutions. [online] Available at: [Accessed 23 Aug. 2016]., (2014). Increase Agility and Reduce Costs with a Logical Data Warehouse. [online] Available at: [Accessed 23 Aug. 2016].

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