Enterprise Data Management and Master Data Management Service Management Test Kit (Publication Date: 2024/02)


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Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:

  • Does your organization have a central, enterprise wide data inventory, data asset register?
  • Are enterprise mobility management solutions sufficient for securing enterprise data on mobile devices?
  • What are the recommended Data Quality metrics that need to be tracked at an enterprise level?
  • Key Features:

    • Comprehensive set of 1584 prioritized Enterprise Data Management requirements.
    • Extensive coverage of 176 Enterprise Data Management topic scopes.
    • In-depth analysis of 176 Enterprise Data Management step-by-step solutions, benefits, BHAGs.
    • Detailed examination of 176 Enterprise Data Management case studies and use cases.

    • Digital download upon purchase.
    • Enjoy lifetime document updates included with your purchase.
    • Benefit from a fully editable and customizable Excel format.
    • Trusted and utilized by over 10,000 organizations.

    • Covering: Data Validation, Data Catalog, Cost of Poor Quality, Risk Systems, Quality Objectives, Master Data Key Attributes, Data Migration, Security Measures, Control Management, Data Security Tools, Revenue Enhancement, Smart Sensors, Data Versioning, Information Technology, AI Governance, Master Data Governance Policy, Data Access, Master Data Governance Framework, Source Code, Data Architecture, Data Cleansing, IT Staffing, Technology Strategies, Master Data Repository, Data Governance, KPIs Development, Data Governance Best Practices, Data Breaches, Data Governance Innovation, Performance Test Data, Master Data Standards, Data Warehouse, Reference Data Management, Data Modeling, Archival processes, MDM Data Quality, Data Governance Operating Model, Digital Asset Management, MDM Data Integration, Network Failure, AI Practices, Data Governance Roadmap, Data Acquisition, Enterprise Data Management, Predictive Method, Privacy Laws, Data Governance Enhancement, Data Governance Implementation, Data Management Platform, Data Transformation, Reference Data, Data Architecture Design, Master Data Architect, Master Data Strategy, AI Applications, Data Standardization, Identification Management, Master Data Management Implementation, Data Privacy Controls, Data Element, User Access Management, Enterprise Data Architecture, Data Quality Assessment, Data Enrichment, Customer Demographics, Data Integration, Data Governance Framework, Data Warehouse Implementation, Data Ownership, Payroll Management, Data Governance Office, Master Data Models, Commitment Alignment, Data Hierarchy, Data Ownership Framework, MDM Strategies, Data Aggregation, Predictive Modeling, Manager Self Service, Parent Child Relationship, DER Aggregation, Data Management System, Data Harmonization, Data Migration Strategy, Big Data, Master Data Services, Data Governance Architecture, Master Data Analyst, Business Process Re Engineering, MDM Processes, Data Management Plan, Policy Guidelines, Data Breach Incident Incident Risk Management, Master Data, Data Mastering, Performance Metrics, Data Governance Decision Making, Data Warehousing, Master Data Migration, Data Strategy, Data Optimization Tool, Data Management Solutions, Feature Deployment, Master Data Definition, Master Data Specialist, Single Source Of Truth, Data Management Maturity Model, Data Integration Tool, Data Governance Metrics, Data Protection, MDM Solution, Data Accuracy, Quality Monitoring, Metadata Management, Customer complaints management, Data Lineage, Data Governance Organization, Data Quality, Timely Updates, Master Data Management Team, App Server, Business Objects, Data Stewardship, Social Impact, Data Warehouse Design, Data Disposition, Data Security, Data Consistency, Data Governance Trends, Data Sharing, Work Order Management, IT Systems, Data Mapping, Data Certification, Master Data Management Tools, Data Relationships, Data Governance Policy, Data Taxonomy, Master Data Hub, Master Data Governance Process, Data Profiling, Data Governance Procedures, Master Data Management Platform, Data Governance Committee, MDM Business Processes, Master Data Management Software, Data Rules, Data Legislation, Metadata Repository, Data Governance Principles, Data Regulation, Golden Record, IT Environment, Data Breach Incident Incident Response Team, Data Asset Management, Master Data Governance Plan, Data generation, Mobile Payments, Data Cleansing Tools, Identity And Access Management Tools, Integration with Legacy Systems, Data Privacy, Data Lifecycle, Database Server, Data Governance Process, Data Quality Management, Data Replication, Master Data Management, News Monitoring, Deployment Governance, Data Cleansing Techniques, Data Dictionary, Data Compliance, Data Standards, Root Cause Analysis, Supplier Risk

    Enterprise Data Management Assessment Service Management Test Kit – Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):

    Enterprise Data Management

    Enterprise Data Management is the process of maintaining and organizing an organization′s data assets across all departments and systems through a central data inventory and asset register.

    1. MDM software: A centralized platform to manage and maintain accurate, consistent data across the organization.
    2. Data governance policies: Set of rules and procedures to ensure data accuracy, accessibility, and accountability.
    3. Data quality tools: Automated checks and monitoring of data to identify and resolve issues, ensuring data integrity.
    4. Data stewardship program: Assigning roles and responsibilities for data management, improving data ownership and accountability.
    5. Master data repository: Centralized storage for clean, trusted and up-to-date master data, reducing data silos and duplication.
    6. Data standardization: Establishing a universal format and naming conventions for data, increasing its consistency and reliability.
    7. Data cleansing: Removing or correcting irrelevant, incomplete, or duplicate data, improving overall data quality.
    8. Data integration: Integrating data from multiple systems into a single source of truth, increasing data visibility and accessibility.
    9. Data security measures: Implementing data security protocols and access controls to protect sensitive data from unauthorized access.
    10. Continuous data monitoring: Regularly reviewing and maintaining data for accuracy and relevancy, improving decision making based on timely information.

    CONTROL QUESTION: Does the organization have a central, enterprise wide data inventory, data asset register?

    Big Hairy Audacious Goal (BHAG) for 10 years from now:

    Yes, our organization has a central, enterprise-wide data inventory and data asset register that provides a comprehensive and dynamic view of all our data assets. This includes information on the data source, quality, ownership, usage, and accessibility. Our goal for 10 years from now is to have this data inventory and asset register fully integrated and utilized in all aspects of our enterprise data management strategy.

    We envision a future where all of our data is easily discoverable and readily accessible, allowing for faster and more accurate decision-making and analysis. Our data inventory and asset register will serve as a centralized platform for managing all data assets, ensuring consistency and accuracy across the organization. It will also allow for easy tracking of data lineage and enable us to identify any potential risks or issues with our data.

    In addition, our enterprise data inventory will be continuously updated and enhanced to incorporate new data sources and technologies, keeping pace with the ever-evolving data landscape. This will allow us to stay ahead of our competition and drive innovation through the use of data.

    Furthermore, our data inventory and asset register will be accessible to all employees, promoting a data-driven culture throughout the organization. This will create a more efficient and collaborative work environment, where data is seen as a valuable asset and its management is given top priority.

    Through the implementation and utilization of our central, enterprise-wide data inventory and asset register, our organization will be at the forefront of effective and efficient data management, setting us apart as leaders in our industry.

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    Enterprise Data Management Case Study/Use Case example – How to use:

    Client Situation:

    XYZ Corporation is a multinational organization operating in the retail sector. The company has a presence in multiple countries and has a significant customer base around the world. As the company continued to grow, they faced significant challenges in managing their data effectively. The lack of a centralized data management system resulted in inconsistencies and inaccuracies in their data, leading to difficulties in decision-making and reporting. Moreover, the organization struggled to comply with regulations and faced data security issues due to the decentralized nature of data storage.

    Consulting Methodology:

    To address the client′s challenges, our consulting firm, DataEdge, was hired to implement an Enterprise Data Management (EDM) strategy. The goal of this strategy was to establish a central, enterprise-wide data inventory and data asset register that would enable the organization to manage and govern its data effectively.

    The first step of our methodology was to conduct a thorough assessment of the client′s current data management practices. This involved interviews with key stakeholders, data audits, and gap analysis of existing processes.

    Based on the assessment, we developed a comprehensive EDM framework customized for the organization′s specific needs and goals. The framework consisted of the following components:

    1. Data Governance: This component focused on establishing policies, procedures, and processes for managing data across the organization. It involved defining roles and responsibilities, creating data quality standards, and implementing data security measures.

    2. Master Data Management (MDM): MDM aimed to establish a single source of truth for critical data entities such as customers, products, and suppliers. This involved data cleansing, standardization, and integration from various systems to ensure accuracy and consistency.

    3. Metadata Management: The metadata management component involved capturing and documenting data lineage, definitions, and relationships between different Service Management Test Kits. This enabled the organization to have a better understanding of its data assets and their usage.

    4. Data Quality Management: To ensure the integrity of data, we implemented data quality controls, checks, and measures. This included data profiling, data cleansing, and data validation techniques.

    5. Data Security Management: The data security management component focused on implementing security measures to protect sensitive data from unauthorized access or breaches. This involved role-based access control, encryption, and data masking techniques.


    As a result of our consulting engagement, we delivered the following key deliverables to the client:

    1. EDM strategy document: This document outlined the overall approach, goals, and objectives of the EDM initiative.

    2. Data inventory and asset register: We created a centralized repository that documented all the organization′s data assets, including their source, usage, and quality. This served as a single source of truth for the organization′s data.

    3. Data governance framework: The framework defined data ownership, accountability, and processes for managing and governing data in the organization.

    4. MDM solution: We implemented an MDM solution that enabled the organization to manage its critical data entities effectively.

    5. Metadata management tool: The metadata management tool captured and documented metadata information for the organization′s data assets.

    6. Data quality management plan: We developed a plan outlining data quality standards, controls, and measures to improve data accuracy and consistency.

    7. Data security measures: We implemented various security measures, including access controls and encryption, to protect the organization′s data.

    Implementation Challenges:

    The implementation of the EDM strategy faced several challenges, including resistance from stakeholders due to changes in processes and lack of understanding of the importance of data management. The decentralized nature of data storage also posed challenges in integrating and consolidating data into a centralized repository. Technical challenges such as data quality issues and legacy systems also hindered the implementation process.


    The success of the EDM implementation was measured using the following key performance indicators (KPIs):

    1. Data quality: We measured the accuracy and completeness of data before and after the implementation of the EDM strategy.

    2. Data security: The number of data breaches and security incidents were tracked to ensure the effectiveness of the implemented security measures.

    3. Compliance: Our client′s compliance with regulations such as GDPR and other data privacy laws were monitored to ensure adherence.

    4. Cost savings: The organization was able to reduce costs associated with data management by optimizing processes and eliminating redundancies.

    Management Considerations:

    To ensure the sustainability of the EDM strategy, we recommended that the organization establish a governance and oversight committee responsible for monitoring and governing data management activities. We also advised the client to invest in continuous training and awareness programs to educate employees on the importance of efficient data management.


    1. Sutter Health Case Study: Enterprise Data Management Implementation (Informatica)
    2. Enterprise Data Management: An Essential Foundation for Digital Transformation (Gartner)
    3. Big Data Governance: A Framework to Activate your Data Assets (Deloitte)
    4. Implementing Data Governance & Stewardship Programs (Information Difference)
    5. Managing Data as an Enterprise Asset: An Overview of Data Governance (TDWI)

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