Archival processes in Data management Dataset (Publication Date: 2024/02)

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



  • Is there a significant savings in archival space when Master Data Management processes are implemented?


  • Key Features:


    • Comprehensive set of 1625 prioritized Archival processes requirements.
    • Extensive coverage of 313 Archival processes topic scopes.
    • In-depth analysis of 313 Archival processes step-by-step solutions, benefits, BHAGs.
    • Detailed examination of 313 Archival processes 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 Control Language, Smart Sensors, Physical Assets, Incident Volume, Inconsistent Data, Transition Management, Data Lifecycle, Actionable Insights, Wireless Solutions, Scope Definition, End Of Life Management, Data Privacy Audit, Search Engine Ranking, Data Ownership, GIS Data Analysis, Data Classification Policy, Test AI, Data Management Consulting, Data Archiving, Quality Objectives, Data Classification Policies, Systematic Methodology, Print Management, Data Governance Roadmap, Data Recovery Solutions, Golden Record, Data Privacy Policies, Data Management System Implementation, Document Processing Document Management, Master Data Management, Repository Management, Tag Management Platform, Financial Verification, Change Management, Data Retention, Data Backup Solutions, Data Innovation, MDM Data Quality, Data Migration Tools, Data Strategy, Data Standards, Device Alerting, Payroll Management, Data Management Platform, Regulatory Technology, Social Impact, Data Integrations, Response Coordinator, Chief Investment Officer, Data Ethics, Metadata Management, Reporting Procedures, Data Analytics Tools, Meta Data Management, Customer Service Automation, Big Data, Agile User Stories, Edge Analytics, Change management in digital transformation, Capacity Management Strategies, Custom Properties, Scheduling Options, Server Maintenance, Data Governance Challenges, Enterprise Architecture Risk Management, Continuous Improvement Strategy, Discount Management, Business Management, Data Governance Training, Data Management Performance, Change And Release Management, Metadata Repositories, Data Transparency, Data Modelling, Smart City Privacy, In-Memory Database, Data Protection, Data Privacy, Data Management Policies, Audience Targeting, Privacy Laws, Archival processes, Project management professional organizations, Why She, Operational Flexibility, Data Governance, AI Risk Management, Risk Practices, Data Breach Incident Incident Response Team, Continuous Improvement, Different Channels, Flexible Licensing, Data Sharing, Event Streaming, Data Management Framework Assessment, Trend Awareness, IT Environment, Knowledge Representation, Data Breaches, Data Access, Thin Provisioning, Hyperconverged Infrastructure, ERP System Management, Data Disaster Recovery Plan, Innovative Thinking, Data Protection Standards, Software Investment, Change Timeline, Data Disposition, Data Management Tools, Decision Support, Rapid Adaptation, Data Disaster Recovery, Data Protection Solutions, Project Cost Management, Metadata Maintenance, Data Scanner, Centralized Data Management, Privacy Compliance, User Access Management, Data Management Implementation Plan, Backup Management, Big Data Ethics, Non-Financial Data, Data Architecture, Secure Data Storage, Data Management Framework Development, Data Quality Monitoring, Data Management Governance Model, Custom Plugins, Data Accuracy, Data Management Governance Framework, Data Lineage Analysis, Test Automation Frameworks, Data Subject Restriction, Data Management Certification, Risk Assessment, Performance Test Data Management, MDM Data Integration, Data Management Optimization, Rule Granularity, Workforce Continuity, Supply Chain, Software maintenance, Data Governance Model, Cloud Center of Excellence, Data Governance Guidelines, Data Governance Alignment, Data Storage, Customer Experience Metrics, Data Management Strategy, Data Configuration Management, Future AI, Resource Conservation, Cluster Management, Data Warehousing, ERP Provide Data, Pain Management, Data Governance Maturity Model, Data Management Consultation, Data Management Plan, Content Prototyping, Build Profiles, Data Breach Incident Incident Risk Management, Proprietary Data, Big Data Integration, Data Management Process, Business Process Redesign, Change Management Workflow, Secure Communication Protocols, Project Management Software, Data Security, DER Aggregation, Authentication Process, Data Management Standards, Technology Strategies, Data consent forms, Supplier Data Management, Agile Processes, Process Deficiencies, Agile Approaches, Efficient Processes, Dynamic Content, Service Disruption, Data Management Database, Data ethics culture, ERP Project Management, Data Governance Audit, Data Protection Laws, Data Relationship Management, Process Inefficiencies, Secure Data Processing, Data Management Principles, Data Audit Policy, Network optimization, Data Management Systems, Enterprise Architecture Data Governance, Compliance Management, Functional Testing, Customer Contracts, Infrastructure Cost Management, Analytics And Reporting Tools, Risk Systems, Customer Assets, Data generation, Benchmark Comparison, Data Management Roles, Data Privacy Compliance, Data Governance Team, Change Tracking, Previous Release, Data Management Outsourcing, Data Inventory, Remote File Access, Data Management Framework, Data Governance Maturity, Continually Improving, Year Period, Lead Times, Control Management, Asset Management Strategy, File Naming Conventions, Data Center Revenue, Data Lifecycle Management, Customer Demographics, Data Subject Portability, MDM Security, Database Restore, Management Systems, Real Time Alerts, Data Regulation, AI Policy, Data Compliance Software, Data Management Techniques, ESG, Digital Change Management, Supplier Quality, Hybrid Cloud Disaster Recovery, Data Privacy Laws, Master Data, Supplier Governance, Smart Data Management, Data Warehouse Design, Infrastructure Insights, Data Management Training, Procurement Process, Performance Indices, Data Integration, Data Protection Policies, Quarterly Targets, Data Governance Policy, Data Analysis, Data Encryption, Data Security Regulations, Data management, Trend Analysis, Resource Management, Distribution Strategies, Data Privacy Assessments, MDM Reference Data, KPIs Development, Legal Research, Information Technology, Data Management Architecture, Processes Regulatory, Asset Approach, Data Governance Procedures, Meta Tags, Data Security Best Practices, AI Development, Leadership Strategies, Utilization Management, Data Federation, Data Warehouse Optimization, Data Backup Management, Data Warehouse, Data Protection Training, Security Enhancement, Data Governance Data Management, Research Activities, Code Set, Data Retrieval, Strategic Roadmap, Data Security Compliance, Data Processing Agreements, IT Investments Analysis, Lean Management, Six Sigma, Continuous improvement Introduction, Sustainable Land Use, MDM Processes, Customer Retention, Data Governance Framework, Master Plan, Efficient Resource Allocation, Data Management Assessment, Metadata Values, Data Stewardship Tools, Data Compliance, Data Management Governance, First Party Data, Integration with Legacy Systems, Positive Reinforcement, Data Management Risks, Grouping Data, Regulatory Compliance, Deployed Environment Management, Data Storage Solutions, Data Loss Prevention, Backup Media Management, Machine Learning 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    Archival processes Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):


    Archival processes

    Yes, implementing Master Data Management processes can lead to significant savings in archival space due to improved data organization and removal of duplicate or irrelevant data.

    1. Data compression techniques: Reduce the size of data files without compromising quality, making it easier and more cost-effective to store and manage data.

    2. Cloud storage: Store data in remote servers, reducing physical storage space and costs related to maintenance and equipment.

    3. Data lifecycle management: Define retention policies for different types of data, storing only necessary data and deleting old or obsolete data to free up storage space.

    4. Database optimization: Optimize database structures and configurations to reduce storage space and improve data retrieval speed.

    5. Data deduplication: Identify and remove duplicate data, reducing the amount of storage space needed and improving data integrity.

    6. Hierarchical storage management: Automatically transfer older or infrequently used data to less expensive storage, freeing up space on primary storage systems.

    7. Virtualization: Use virtual machines to consolidate and optimize hardware resources, reducing the need for physical storage space.

    8. Data archiving: Create a separate storage system specifically for archived data, keeping it separate from active data and freeing up space on primary storage.

    9. Tiered storage: Use a combination of high-performance and lower-cost storage solutions to efficiently manage data based on its level of importance.

    10. Compression and decompression tools: Use specialized software to compress data into smaller sizes for archival purposes, reducing storage space requirements.

    CONTROL QUESTION: Is there a significant savings in archival space when Master Data Management processes are implemented?


    Big Hairy Audacious Goal (BHAG) for 10 years from now:
    The big hairy audacious goal for 10 years from now for Archival processes is to achieve a 75% reduction in archival space when Master Data Management processes are implemented. This would be a significant improvement compared to the current state, where only a 25% reduction is typically seen with MDM implementation.

    By implementing robust and efficient Master Data Management processes, organizations will have better control and understanding of their data, leading to more effective and targeted archiving practices. This will result in significant cost savings in terms of physical storage space, as well as better utilization of IT resources and improved overall operational efficiency.

    This goal will require constant innovation and adaptation to new technologies and data management strategies. It will also require a cultural shift within organizations, encouraging a proactive approach to data governance and archival processes.

    Achieving this goal would not only benefit individual organizations, but also contribute to a more sustainable and environmentally conscious approach to data storage and management. It would also pave the way for future advancements in archival processes, setting a benchmark for efficiency and effectiveness in data management.

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    Archival processes Case Study/Use Case example - How to use:



    Client Situation:
    The client, a large retail chain with over 100 stores nationwide, was facing a significant challenge with their growing amount of data and the associated storage costs. As they expanded their product offerings and customer base, their data volume increased exponentially. The use of multiple systems and databases led to duplicated and inconsistent data, making it difficult to extract accurate insights and make data-driven business decisions. This issue was further compounded by the lack of proper data governance and management processes.

    The retail chain realized the urgency to implement an effective Master Data Management (MDM) solution to address their data challenges and reduce storage costs. They approached our consulting firm, looking for an efficient and cost-effective way to manage their constantly growing data while maintaining data quality and consistency.

    Consulting Methodology:
    Our consulting firm deployed a three-phase approach to address the client′s concerns. The first phase involved conducting an initial assessment of the client′s current data management practices and identifying areas for improvement. The second phase focused on designing and implementing a customized Master Data Management system, considering the client′s specific business needs. The third and final phase involved rolling out the MDM system across all business units and providing training to relevant stakeholders on how to effectively use the new system.

    Deliverables:
    The primary deliverable of this project was the implementation of a robust Master Data Management system that consolidated all the client′s data into a single, centralized platform. Our customized MDM system also provided data governance capabilities, including data standardization, data cleansing, and data quality monitoring. Additionally, we provided the client with an MDM strategy document outlining best practices for ongoing data management and governance.

    Implementation Challenges:
    The biggest challenge faced during the implementation of this project was the integration of data from multiple sources, including legacy systems, ERP systems, and third-party applications. This required extensive data mapping and cleansing to ensure data accuracy and consistency.

    KPIs:
    To measure the success of this project, we tracked several key performance indicators (KPIs) related to data management and storage costs. These included:
    1. Data Accuracy: Percentage of accurate data in the system.
    2. Data Duplication: Percentage of duplicate records eliminated through MDM processes.
    3. Storage Space Savings: Percentage of cost savings in archival space.
    4. Data Retrieval Time: Average time taken to retrieve required data.
    5. Data Storage Costs: Comparison of storage costs before and after MDM implementation.

    Management Considerations:
    In addition to the technical aspects of the project, our consulting firm also worked closely with the client′s management team to ensure successful adoption of the MDM solution. We provided training and support to key stakeholders, highlighting the importance of data governance and the need for ongoing data management practices.

    Citation:
    According to a whitepaper published by Informatica, a leading data management solutions provider, organizations with proper Master Data Management processes can save up to 15-20% in data storage costs. This is achieved through the elimination of data duplication, improved data quality, and enhanced data governance practices.

    Furthermore, a study published in the IMD International Business School′s Journal for General Management found that successful implementation of MDM processes not only leads to cost savings but also enables organizations to make more informed business decisions, resulting in an increase in overall profitability.

    Market research reports also highlight the growing importance of MDM in the retail industry, where the volume and complexity of data are continuously increasing. A report by MarketsandMarkets estimates that the global MDM market size in the retail sector will grow from USD 6.8 billion in 2019 to USD 19.6 billion by 2024, driven by the need for efficient data management and increased ROI.

    Conclusion:
    In conclusion, the implementation of Master Data Management processes proved to be highly beneficial for the retail chain in terms of reducing storage costs. The use of a centralized data management system improved data quality, eliminated data duplication, and provided better data governance capabilities. The successful implementation of MDM processes also resulted in faster data retrieval times and improved accuracy, enabling the client to make more informed business decisions. Our consulting firm continues to work with the client to ensure the sustainability and continuous improvement of their Master Data Management practices. Overall, this case study highlights the significant cost savings that can be achieved through the implementation of effective MDM processes in organizations dealing with large volumes of data.

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