Data Retention and Data Governance Project Readiness Kit (Publication Date: 2024/02)


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

  • What should your organization do with the data used for testing when it completes the upgrade?
  • How do you convey requirements for data retention, destruction, and encryption to your suppliers?
  • How long do you retain each item of your data for and the justification for the retention period?
  • Key Features:

    • Comprehensive set of 1531 prioritized Data Retention requirements.
    • Extensive coverage of 211 Data Retention topic scopes.
    • In-depth analysis of 211 Data Retention step-by-step solutions, benefits, BHAGs.
    • Detailed examination of 211 Data Retention 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 Privacy, Service Disruptions, Data Consistency, Master Data Management, Global Supply Chain Governance, Resource Discovery, Sustainability Impact, Continuous Improvement Mindset, Data Governance Framework Principles, Data classification standards, KPIs Development, Data Disposition, MDM Processes, Data Ownership, Data Governance Transformation, Supplier Governance, Information Lifecycle Management, Data Governance Transparency, Data Integration, Data Governance Controls, Data Governance Model, Data Retention, File System, Data Governance Framework, Data Governance Governance, Data Standards, Data Governance Education, Data Governance Automation, Data Governance Organization, Access To Capital, Sustainable Processes, Physical Assets, Policy Development, Data Governance Metrics, Extract Interface, Data Governance Tools And Techniques, Responsible Automation, Data generation, Data Governance Structure, Data Governance Principles, Governance risk data, Data Protection, Data Governance Infrastructure, Data Governance Flexibility, Data Governance Processes, Data Architecture, Data Security, Look At, Supplier Relationships, Data Governance Evaluation, Data Governance Operating Model, Future Applications, Data Governance Culture, Request Automation, Governance issues, Data Governance Improvement, Data Governance Framework Design, MDM Framework, Data Governance Monitoring, Data Governance Maturity Model, Data Legislation, Data Governance Risks, Change Governance, Data Governance Frameworks, Data Stewardship Framework, Responsible Use, Data Governance Resources, Data Governance, Data Governance Alignment, Decision Support, Data Management, Data Governance Collaboration, Big Data, Data Governance Resource Management, Data Governance Enforcement, Data Governance Efficiency, Data Governance Assessment, Governance risk policies and procedures, Privacy Protection, Identity And Access Governance, Cloud Assets, Data Processing Agreements, Process Automation, Data Governance Program, Data Governance Decision Making, Data Governance Ethics, Data Governance Plan, Data Breaches, Migration Governance, Data Stewardship, Data Governance Technology, Data Governance Policies, Data Governance Definitions, Data Governance Measurement, Management Team, Legal Framework, Governance Structure, Governance risk factors, Electronic Checks, IT Staffing, Leadership Competence, Data Governance Office, User Authorization, Inclusive Marketing, Rule Exceptions, Data Governance Leadership, Data Governance Models, AI Development, Benchmarking Standards, Data Governance Roles, Data Governance Responsibility, Data Governance Accountability, Defect Analysis, Data Governance Committee, Risk Assessment, Data Governance Framework Requirements, Data Governance Coordination, Compliance Measures, Release Governance, Data Governance Communication, Website Governance, Personal Data, Enterprise Architecture Data Governance, MDM Data Quality, Data Governance Reviews, Metadata Management, Golden Record, Deployment Governance, IT Systems, Data Governance Goals, Discovery Reporting, Data Governance Steering Committee, Timely Updates, Digital Twins, Security Measures, Data Governance Best Practices, Product Demos, Data Governance Data Flow, Taxation Practices, Source Code, MDM Master Data Management, Configuration Discovery, Data Governance Architecture, AI Governance, Data Governance Enhancement, Scalability Strategies, Data Analytics, Fairness Policies, Data Sharing, Data Governance Continuity, Data Governance Compliance, Data Integrations, Standardized Processes, Data Governance Policy, Data Regulation, Customer-Centric Focus, Data Governance Oversight, And Governance ESG, Data Governance Methodology, Data Audit, Strategic Initiatives, Feedback Exchange, Data Governance Maturity, Community Engagement, Data Exchange, Data Governance Standards, Governance Strategies, Data Governance Processes And Procedures, MDM Business Processes, Hold It, Data Governance Performance, Data Governance Auditing, Data Governance Audits, Profit Analysis, Data Ethics, Data Quality, MDM Data Stewardship, Secure Data Processing, EA Governance Policies, Data Governance Implementation, Operational Governance, Technology Strategies, Policy Guidelines, Rule Granularity, Cloud Governance, MDM Data Integration, Cultural Excellence, Accessibility Design, Social Impact, Continuous Improvement, Regulatory Governance, Data Access, Data Governance Benefits, Data Governance Roadmap, Data Governance Success, Data Governance Procedures, Information Requirements, Risk Management, Out And, Data Lifecycle Management, Data Governance Challenges, Data Governance Change Management, Data Governance Maturity Assessment, Data Governance Implementation Plan, Building Accountability, Innovative Approaches, Data Responsibility Framework, Data Governance Trends, Data Governance Effectiveness, Data Governance Regulations, Data Governance Innovation

    Data Retention Assessment Project Readiness Kit – Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):

    Data Retention

    The organization should determine the appropriate length of time to retain the test data and safely dispose of it afterwards.

    1. Implement a data retention policy to specify how long data should be kept, based on regulations and business needs.
    Benefits: Ensures compliance, saves storage space and reduces risk of exposure in case of a data breach.

    2. Use data masking or tokenization techniques to anonymize or replace sensitive data in test environments.
    Benefits: Protects sensitive data from being exposed during testing, while still allowing for accurate testing results.

    3. Archive or backup the data used for testing and move it to a secure storage location.
    Benefits: Reduces strain on production systems and allows for easy access to historical data if needed.

    4. Purge or delete unnecessary data from test environments after testing is complete.
    Benefits: Reduces storage costs and minimizes the risk of unauthorized access to sensitive data.

    5. Conduct regular reviews to ensure compliance with the data retention policy and identify opportunities for data reduction.
    Benefits: Helps keep data retention practices up-to-date and minimizes the risk of keeping unnecessary data.

    6. Utilize data management tools to automate the process of identifying and removing outdated or irrelevant data.
    Benefits: Saves time and resources, and improves accuracy of data retention practices.

    CONTROL QUESTION: What should the organization do with the data used for testing when it completes the upgrade?

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

    In 10 years, our data retention goal for our organization′s testing data is to have a comprehensive and efficient system in place that allows us to securely store and access all data used for testing purposes. This will include not only the raw data collected during testing, but also any analysis and results generated from the data.

    Our ultimate goal is to create a data repository that can be easily searched and utilized by various teams within the organization for future research, development, and decision-making processes. This repository should incorporate advanced data management tools and technologies, including data encryption and backups, to ensure the security and integrity of the data.

    Furthermore, we aim to establish a data retention policy that outlines the specific guidelines and protocols for the storage, usage, and disposal of testing data. This policy will also address any legal or regulatory requirements for data retention and ensure compliance with industry standards.

    Ultimately, our big, hairy, audacious goal is for our organization to become a leader in effective and responsible data retention practices. We want to maximize the value of our testing data for future use while maintaining strict data privacy and security measures. By achieving this goal, we can continue to drive innovation, make informed decisions, and stay ahead in the highly competitive landscape of our industry.

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    Data Retention Case Study/Use Case example – How to use:

    Case Study: Data Retention Strategy for an Upgrade Project

    Synopsis of Client Situation

    Our client, a medium-sized retail company, was planning to upgrade their existing point-of-sale (POS) system to a more advanced and efficient system. This upgrade was essential to keep up with their growing business and customer demands. The new system would provide them with better data analysis, inventory management, and user-friendly interface.

    As our client was preparing for the upgrade, they faced a critical question regarding the data used for testing during the upgrade process. They had accumulated a significant amount of data during the testing phase, including confidential customer information, sales data, and inventory records. Our client was unsure about the fate of this data after the completion of the upgrade. They were concerned about potential data breaches, regulatory compliance, and overall data governance.

    Consulting Methodology

    To address our client′s concerns, our consulting team followed a four-phase methodology:

    1. Assessment and Analysis: We started by assessing our client′s current data retention policies and practices. This step also involved identifying the types of data that were generated during the testing phase and their associated risks.

    2. Regulatory Compliance: The next step was to evaluate the legal requirements concerning data privacy and retention. We referred to the General Data Protection Regulation (GDPR), the Payment Card Industry Data Security Standard (PCI DSS), and other relevant regulations to ensure our client′s compliance.

    3. Stakeholder Engagement: In this phase, we engaged with key stakeholders, including IT, legal, and data governance teams, to understand their concerns and expectations regarding data retention. We collaborated with these teams to develop a data retention strategy that aligned with their business objectives.

    4. Implementation: The final phase involved implementing the data retention strategy, which included updating policies and procedures, developing a data retention schedule, and setting up data retention controls.

    Deliverables and Implementation Challenges

    The deliverables provided to our client included a complete assessment report, a data retention policy document, a data retention schedule, and a data retention control framework.

    The main challenge faced during the implementation phase was identifying the relevant regulations and their associated data retention requirements. Our team had to review and interpret complex legal texts to ensure our client′s compliance.

    KPIs and Management Considerations

    The success of our engagement was measured using the following key performance indicators (KPIs):

    1. Compliance: The primary KPI for this engagement was our client′s compliance with relevant regulations. We evaluated this through regular compliance audits and reviews.

    2. Cost Savings: We also tracked the cost savings achieved through the efficient management of data. By implementing a data retention schedule, our client could reduce storage costs, avoid unnecessary data backups, and optimize server resources.

    3. Risk Reduction: Another KPI used to measure the success of this engagement was the reduction in data breach risks. With an updated data retention strategy, our client could minimize the likelihood of sensitive data being compromised.

    Management considerations for our client include regularly reviewing and updating their data retention policies and procedures. This will help them stay compliant with changing regulations and manage their data efficiently. It is also essential for our client to regularly train their employees on data retention best practices and foster a culture of data responsibility within the organization.


    Our client′s data retention strategy was successfully developed and implemented, allowing them to comply with relevant regulations, optimize costs, and reduce data risk. By engaging stakeholders and following a rigorous methodology, our consulting team was able to provide our client with a comprehensive data retention strategy that aligned with their business objectives. With this strategy in place, our client can confidently complete their upgrade project knowing that their data is being managed effectively and securely.

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