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Multi-Enterprise Master Data Management

Discuss about the Enterprise Information Management.

Recently, organizations are dealing with a huge explosion of information. Organizations are required to staunchly manage and save these large amounts of data, daily. Enterprise Information Management, also called EIM is used to term a business discipline which unifies various theories of content management, business intelligence technique, and enterprise integration so as to reorganize the information for better management, storage, and handling (Lam & Taylor, 2016). 

Master Data denotes the basic entities for a specific enterprise like accounts, suppliers, consumers, etc. Master Data Management (MDM) is the theory which incorporates business with IT so that continuity, uniformity, and accuracy of master data which is associated with the organization can be maintained (Gartner, 2012). Master Data Management consists of processes, tools, standards, policies, and governance which uniformly explains and manages an organization’s core data for providing an individual reference point. The data comprises of both analytical data and reference data. Multi-enterprise Master Data Management provides support for master data management via deletion of duplicates, mass maintenance, and implementing rules for elimination of improper data being provided to the system. This creates an authentic master data source. The main objective of MDM is to provide processes for collection, aggregation, consolidation, quality-assurance, persistence, and distribution of data all throughout an organization so that control and consistency is ensured. It is an exclusive process of helping an enterprise link all the critical data into one single file which provides a reference point. MDM simplifies data sharing between departments and personnel. Additionally, MDM streamlines computing across various system applications, platforms, and architectures. MDM can be defined as the theory of specialized quality improvement via policies and procedures which have been implemented by the organization. The main goal of MDM is to provide the user with a single true file on which the management will base their decisions. MDM ensures that the organization doesn’t utilize various versions of one file in various operations. MDM can also be defined to be a technology-centric discipline which combines IT and business so that accountability, semantic consistency, stewardship, accuracy, and uniformity can be ensured for an enterprise’s data asset. Master Data is uniform and consistent all throughout. It is an uniform and consistent identifier set and enhanced attributes which explains the basic components of an enterprise such as accounts, hierarchies, sites, suppliers, employees, opportunities, and the consumers. 

One specific master data source consists of 3 important abilities, namely, an authorized information sources, the capability to consistently utilize the information, and the capability to be parallel with the transformation of the business requirements, in regards to MDM. An integrated compilation of services might be created in regards to the specific piece of data in regards to the analytical applications and business processes. Additionally, effective business operations are enabled via the 3 abilities which have been explained. The authenticated and authoritative data sources make sure the data is redundancy and duplicity free and it follows a standardized structure (Dreibelbes, 2016).

Gartner has described numerous theories and technologies which can be used for EIM with the help of Hype Cycle, that has been depicted below.


Hype Cycle (Ronthal and Simoni, 2015)

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

There are numerous stages of application for a multi-enterprise MDM methods and techniques. They are –

  • The first step is Identification of data sources from all exterior levels and from all throughout the enterprise. These might comprise of both unstructured and structured data.
  • The second step is Identification of data consumers and producers is performed so that the data sources can be understood properly.
  • The third step is Collection and analysis of master data’s metadata is performed via analysis of data so that all associated attributes can be understood.
  • The fourth step is Appointment of data stewards is performed so that the collected data can be analyzed and the various associated operations can be managed.
  • The fifth step is Implementation of information and data governance is performed so that authority is given at the administrative, legislative, judicial, and executive levels to the data management is performed correctly.
  • The sixth step is Development of a Master Data Model is done. It is created at a multi-enterprise level which is developed in regards to the approval of all involved parties. Additionally, it includes the analysis results, too.
  • The seventh step is Specific set of tools are used for the creation of master lists by performing various operations on them. These tools are generally divided into 2 classes –
    • Production Information Management (PIM)
    • Customer Data Integration (CDI)
  • The eight step is an infrastructure is created. Then, the master data is tested via various equipment and tools that were previously chosen.
  • The ninth step is A process of maintenance is performed afterwards so that additional activities can be maintained (Wolter and Haselden, 2016). 

MDM accomplished the 5 basics of management of enterprise level information and data that has been depicted below –

  • Data Migration – Master Data consists of data which is amassed from various diverse sources and is transferred to a single location. The data is cleaned effectively so that all kinds of inconsistencies, redundancies, and duplicities can be removed from all the sets of data.
  • Data Integration – Each organization has their own methodology and information architecture which is customizable. The Multi-enterprise MDM incorporates the whole sets of data in a single unit which is well-suited with the organization’s architecture.
  • Data Maintenance – Synchronization of data definitions and business objectives with multi-enterprise MDM makes sure that any modifications are correctly and accurately reflected and data consistency is maintained throughout.
  • Data Quality Assurance – Multi-enterprise MDM comprises of various quality measures for maintaining data quality.
  • Data Retention – Multi-enterprise MDM employs constant effort for management of data by saving essential data and deleting non-essential and unnecessary data. 
  • Master Data Edits – With the assistance of MDM and its related features, Master data modification can be performed. Sometimes, it might happen that modification made in one data copy might not be shown in other data copies. This might cause discrepancies and inconsistence which might be quite fatal. MDM ensures that these situations do not take place and updates records via master data modifications.
  • Effective Data Analysis – With the assistance of irredundant and clean data, best data analysis results can be achieved. This might not be possible if unreliable or duplicate data is present.
  • Removal of redundancy – For high volumes of data, redundancy is quite common since data is collected from various sources. A major advantage of multi-enterprise MDM is the fact that redundancy of data is fully removed and maintenance of coherence is present all throughout.
  • Various Platforms – When various businesses and enterprises are involved, there are various platforms present, too. MDM permits data access and availability across various platforms like online, physical, cloud, etc.
  • Flexibility – MDM permits immense data flexibility as operations like modifications, storage, changes, etc. Are quite easy to manage and perform.
  • Easy and timely Backup – Centralized sources of data are used for multi-enterprise MDM so that data backup is performed frequently and the data backup is saved securely in case a severe attack takes place.
  • Key Data – Users have the capability to make decision regarding the data which will be fed and the data that will be deleted. This permits accomplishment of needs of the business.
  • Authoritative Data – MDM provides an authentic information source and a reliable base for all types of business operations which are performed.
  • Access Based Roles – For multi-enterprise MDM, access privileges and rights is provided according to the individuals’ role within the master data. It permits better management of access and maintains data and information privacy and security.
  • High Usability and Business Efficiency – The 2 prime conditions for consumers and business operators are efficiency and usability. Multi-enterprise MDM provides both business efficiency and increased usability which results in satisfied consumers and employees. 

The 2nd technology and concept which is used for enterprise information management is the application and utilization of logical data warehouses (Russom, 2016). The architectural layer which is layered on top of the organization’s normal data warehouse which consists of continuous data is known as a logical data warehouse. This layer permits the business analysts and executives to observe the stored data without any requirement of data relocation and transformation, prior to observation. The logical data warehouse complements the traditional central data warehouse with capabilities which will progressively fetch and transform data. 

  • Repository Management – This is a critical part of the Logical Data Warehouse which manages and maintains the enterprise’s data repository.
  • Distributed Processes – Various shared processes are available within the cloud and these are incorporated with the assistance of logical DATA warehouses.
  • Auditing Statistics Services – Accurate data accuracy and governance is permitted and handled.
  • Management of Metadata – Metadata from various external and internal sources is managed and maintained.
  • Data Virtualization – It permits virtual data integration for all the enterprise components. 

EIM has numerous demands which can be accomplished via the implementation of a logical data warehouse within the organization’s structure and architecture. These demands are as follows –

  • Agility of enterprise – an organization’s capability to acquire and sense modifications which are required to incorporated and successfully attempt is known as enterprise agility. Logical data warehouse permits the enterprise to manage an excellent agility level since it offers a logical data view which is present at the organization’s levels and allocations.
  • Single view – Corporations want a consistent and individual information view which is related to it in regards to information, suppliers, consumers, etc. Logical data warehouses offer this with extreme excellence and ease (Hensley, 2016).
  • Satisfaction of customer– an organization primary goal is maintenance and achievement of enhanced consumer satisfaction. This permitted via the utilization of logical data warehouses via effective data analysis, storage, and management.
  • Real time – Integration of real-time data is given across all interfaces and boundaries by permitting on-demand analysis via logical data warehouses. 
  • Agility on all enterprise level – Most of the new modifications and developments in regards to data attributes can be effectively managed via Logical DATA warehouses. Real-time analytics and queries is permitted via Logical DATA warehouses since it offers enhanced flexibility.
  • Comprehensive Critical Data Assets View – Organizations find it easier to have a combined data view, at all times. This is permitted via the logical DATA warehouses. Additionally, it permits multi-structured and unstructured data management and handling.
  • Budget Effectiveness – Logical DATA warehouses provides features which are cost-effective. Also, it permits optimized data administration 


EIM or Enterprise Information Management is a critical element which is required by every organization. There are various concepts and technologies which have been initiated for achieving various tasks. Multi-enterprise MDM and Logical DATA warehouses are 2 methodologies which help in management of data and information at an enterprise level. There are various advantages which are provided via these methodologies in regards to cost effectiveness, agility, individual data view, cleansed data, and flexibility. Additionally, these permits the enterprise to accomplish its core business objectives in regards to customer satisfaction and competitive advantage. Additionally, various security mechanisms are present within these 2 methods. These permit the business officials to maintain data privacy and security, in addition to availability, integrity, and confidentiality. The advantages of MDM are enhanced as the organization’s departments, employees, and applications are enhanced. Due to this reason, MDM is quite worthwhile for larger enterprises. In case of mergers, MDM helps in minimization of confusion, and optimization of efficiency of the newly amalgamated organization.


Compositesw (2016). Logical Data Warehouse | Cisco. [online] Available at: [Accessed 3 Sep. 2016]. 

Dreibelbis, A. (2016). Enterprise Master Data Management. [online] Available at: [Accessed 3 Sep. 2016]. 

Increase Agility and Reduce Costs with a Logical Data Warehouse. (2014). 1st ed. [ebook] Available at: [Accessed 3 Sep. 2016]. 

Gartner, (2012). Master Data Management (MDM) - Gartner IT Glossary. [online] Gartner IT Glossary. Available at: [Accessed 3 Sep. 2016]. 

Hensley, N. (2016). The logical data warehouse: The new secret weapon for leading. [online] IBM Big Data & Analytics Hub. Available at: [Accessed 3 Sep. 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 3 Sep. 2016]. 

Russom, P. (2016). The Logical Data Warehouse: What it is and why you need it. [online] Available at: [Accessed 3 Sep. 2016]. 

Wolter, R. and Haselden, K. (2016). The What, Why, and How of Master Data Management. [online] Available at: [Accessed 3 Sep. 2016].

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