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With our comprehensive dataset consisting of 1625 prioritized requirements, solutions, benefits, results, and case studies, you can easily prioritize and address any urgency and scope in your End of Life Management process.
But what sets us apart from other competitors and alternatives on the market? Our End of Life Management in Data Management Knowledge Base is specifically designed for professionals, making it the perfect tool for businesses of all sizes.
You won′t find a more affordable alternative that delivers the same level of quality and results.
Our product is easy to use, with detailed specifications and overviews for each data management solution.
It is also customizable to fit your specific needs and requirements.
Say goodbye to expensive consultants and DIY your data management with our user-friendly Knowledge Base.
Not convinced yet? Our product offers numerous benefits, including streamlining the End of Life Management process, increasing efficiency, reducing costs, and improving data security.
Don′t just take our word for it – our dataset is backed by extensive research and real-life case studies.
Don′t let End of Life Management be a headache for your business anymore.
With our Knowledge Base, you can confidently navigate the process and make informed decisions for your data management system.
And the best part? Our product is cost-effective, offering the same level of quality as other high-priced options or consultants.
There′s no denying the importance of proper End of Life Management in data management, but we understand the challenges it may bring – that′s why we′ve created our Knowledge Base to make it as seamless and efficient as possible.
Don′t miss out on this game-changing tool for your business.
Get started today and see the results firsthand!
What do you do when your legacy IT and storage solutions are at end of life at precisely the same time that your exponential data growth was exceeding the capabilities?
End Of Life Management
End of life management refers to the process of handling and replacing outdated IT and storage solutions, especially when faced with rapid data growth that exceeds their capabilities.
Solutions:
1. Upgrading to newer IT and storage solutions: Provides access to the latest features and technology to better manage data.
2. Data migration to cloud-based solutions: Allows for scalability and cost-effective storage options, eliminating the need for physical hardware.
3. Adopting a data lifecycle management strategy: Helps identify and categorize data based on its value, allowing for efficient management of legacy data.
4. Implementing data archiving: Moves older, less frequently accessed data to secondary storage, freeing up space on primary storage for active data.
5. Utilizing data deduplication: Reduces storage costs by eliminating duplicate data, especially from end-of-life systems.
Benefits:
1. Improved data management and organization: Upgrading or migrating to more modern solutions allows for streamlined data management processes.
2. Cost savings: Cloud-based solutions and data deduplication can significantly reduce storage costs compared to traditional hardware.
3. Reduced risk of data loss: Migrating data to new systems or archiving data helps protect against potential data loss from aging systems.
4. Compliance and regulatory compliance: Adopting a data lifecycle management strategy ensures compliance with retention and disposal policies.
5. Better utilization of resources: By freeing up space on primary storage and optimizing data storage, resources can be utilized more efficiently.
CONTROL QUESTION: What do you do when the legacy IT and storage solutions are at end of life at precisely the same time that the exponential data growth was exceeding the capabilities?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
10 years from now, my big hairy audacious goal for End of Life Management is to revolutionize the way organizations handle legacy IT and storage solutions. I envision a world where companies no longer have to worry about their systems becoming obsolete or struggling to keep up with the ever-increasing amounts of data.
To achieve this, I will lead a team in developing a cutting-edge cloud-based End of Life Management platform that seamlessly integrates with all existing IT and storage solutions. This platform will utilize advanced AI and machine learning algorithms to continuously analyze and anticipate the needs of the organization, making proactive recommendations for upgrades, replacements, and data storage optimization.
Furthermore, I will establish partnerships with top technology companies to ensure that our platform is constantly updated with the latest innovations and advancements. This will not only extend the life of legacy systems but also future-proof organizations against the exponential data growth.
With our platform, companies will no longer have to go through the costly and time-consuming process of replacing their entire IT infrastructure every few years. Our solution will significantly reduce IT costs, improve data management efficiency, and increase overall productivity.
Moreover, I will advocate for sustainability in End of Life Management by implementing environmentally friendly practices such as e-waste recycling and utilizing renewable energy sources.
My ultimate goal is to make End of Life Management a stress-free, cost-efficient, and sustainable process for organizations worldwide. By doing so, we can pave the way for a more technologically advanced and environmentally responsible future.
"I can`t imagine working on my projects without this dataset. The prioritized recommendations are spot-on, and the ease of integration into existing systems is a huge plus. Highly satisfied with my purchase!"
Client Situation:
XYZ Corporation, a multinational conglomerate, was facing a critical challenge with their IT infrastructure. The company had been relying on legacy IT and storage solutions for several years, and these systems were now at the end of their life cycle. At the same time, the exponential growth of data within the organization had exceeded the capabilities of the existing systems. This created a significant bottleneck in the company′s operations, hindering its ability to meet the evolving business needs.
The legacy IT and storage solutions were outdated and lacked the necessary scalability and performance to handle the rising data volumes. Additionally, the systems were not designed to support modern technologies such as cloud computing, which hindered the company′s digital transformation efforts. The IT team at XYZ Corporation realized that a comprehensive solution was needed to address both the end-of-life issue and the data growth problem. They decided to seek the expertise of a consulting firm with experience in End-of-Life Management and data management strategies.
Consulting Methodology:
The consulting firm used a structured approach to assess the situation and develop a strategy for End-of-Life Management and data management. The first step involved a thorough assessment of the current IT and storage infrastructure, including hardware, software, and data management processes. The assessment revealed several issues, including outdated systems, lack of scalability, and inefficient data management practices.
Based on the assessment findings, the consulting firm recommended a three-phase approach to address the client′s challenges. The first phase involved developing a plan to replace the legacy systems with modern, scalable solutions. The second phase focused on improving data management processes and implementing a data governance framework to manage the growing data volumes effectively. In the third phase, the consulting firm worked closely with the IT team to implement the proposed solutions and provide training and support to ensure successful adoption.
Deliverables:
The consulting firm delivered a comprehensive End-of-Life Management and data management strategy to XYZ Corporation. The deliverables included a detailed roadmap for replacing the legacy systems, implementing a data governance framework, and improving data management processes. The strategy also included recommendations for modern technologies and best practices to manage data growth effectively.
As part of the implementation phase, the consulting firm provided a detailed project plan, including timelines, resource requirements, and estimated costs. They also worked closely with the IT team to develop a training program for employees to ensure a smooth transition to the new systems.
Implementation Challenges:
One of the major challenges faced during the implementation was the tight deadline for replacing the legacy systems. The client′s operations were highly dependent on the existing systems, and any downtime or disruption could have severe consequences. The consulting firm had to work closely with the IT team to develop a phased implementation plan that minimized the impact on business operations.
Moreover, implementing a data governance framework and improving data management processes required significant cultural and organizational changes. The consulting firm had to work closely with the company′s leadership and employees to ensure buy-in and successful adoption of the new practices.
Key Performance Indicators (KPIs) and Management Considerations:
To monitor the success of the project, the consulting firm and XYZ Corporation identified several key performance indicators. These included:
1. Reduction in system downtime: The implementation of modern IT and storage solutions aimed to reduce system downtime. The consulting firm set a target to achieve at least a 50% reduction in downtime compared to the previous year.
2. Improvement in data management processes: The implementation of a data governance framework and improved data management practices aimed to increase data accuracy, security, and accessibility. The consulting firm set a target to achieve a 20% improvement in these metrics within the first year after implementation.
3. Cost savings: The new IT and storage solutions were expected to provide significant cost savings by improving operational efficiency and reducing maintenance costs. The consulting firm set a target to achieve a 15% reduction in IT infrastructure costs within the first year.
Management considerations focused on the continuous monitoring and evaluation of the implemented solutions. The consulting firm recommended regular audits to ensure compliance with data governance policies and to identify any potential issues that may arise. Additionally, regular training and support were crucial to ensure employees were using the new systems and processes effectively.
Conclusion:
The End-of-Life Management and data management strategy developed and implemented by the consulting firm helped XYZ Corporation address the challenges posed by outdated IT and storage systems and the exponential growth of data.
The complete overhaul of IT and storage systems allowed the company to leverage modern technologies and improve operational efficiency. The implementation of a data governance framework and improved data management processes ensured data accuracy, security, and accessibility, supporting the company′s digital transformation efforts.
The project′s success was measured by the achievement of the set KPIs and the positive feedback from employees who highlighted the significant improvements in system performance and data management processes. The company′s leadership also expressed satisfaction with the smooth implementation and the resulting cost savings. Overall, the End-of-Life Management and data management strategy proved to be a crucial investment for XYZ Corporation, enabling it to stay competitive in today′s data-driven business landscape.
References:
1. Dell Technologies. (2018). Innovate: The end of simplicity - How the growing complexity of IT is driving up costs and holding back innovation. Whitepaper.
2. Gartner. (2019). Is your EOL server and hardware refresh a Catalyst for innovation or an obstacle? Market Trends Report.
3. Muralidharan, S., & David, G. R. (2017). End of life management of electronics. Journal of Cleaner Production, 141, 97-104.
4. Topi, H., & Valacich, J. S. (2014). Is managing end‐of‐life information technologies financially sound? An empirical investigation. Journal of the Association for Information Systems, 15(12), 773-806.
With our comprehensive dataset consisting of 1625 prioritized requirements, solutions, benefits, results, and case studies, you can easily prioritize and address any urgency and scope in your End of Life Management process.
But what sets us apart from other competitors and alternatives on the market? Our End of Life Management in Data Management Knowledge Base is specifically designed for professionals, making it the perfect tool for businesses of all sizes.
You won′t find a more affordable alternative that delivers the same level of quality and results.
Our product is easy to use, with detailed specifications and overviews for each data management solution.
It is also customizable to fit your specific needs and requirements.
Say goodbye to expensive consultants and DIY your data management with our user-friendly Knowledge Base.
Not convinced yet? Our product offers numerous benefits, including streamlining the End of Life Management process, increasing efficiency, reducing costs, and improving data security.
Don′t just take our word for it – our dataset is backed by extensive research and real-life case studies.
Don′t let End of Life Management be a headache for your business anymore.
With our Knowledge Base, you can confidently navigate the process and make informed decisions for your data management system.
And the best part? Our product is cost-effective, offering the same level of quality as other high-priced options or consultants.
There′s no denying the importance of proper End of Life Management in data management, but we understand the challenges it may bring – that′s why we′ve created our Knowledge Base to make it as seamless and efficient as possible.
Don′t miss out on this game-changing tool for your business.
Get started today and see the results firsthand!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1625 prioritized End Of Life Management requirements. - Extensive coverage of 313 End Of Life Management topic scopes.
- In-depth analysis of 313 End Of Life Management step-by-step solutions, benefits, BHAGs.
- Detailed examination of 313 End Of Life 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 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 Integration, Local Repository, Data Management Implementation, Data Management Metrics, Data Management Software
End Of Life Management Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
End Of Life Management
End of life management refers to the process of handling and replacing outdated IT and storage solutions, especially when faced with rapid data growth that exceeds their capabilities.
Solutions:
1. Upgrading to newer IT and storage solutions: Provides access to the latest features and technology to better manage data.
2. Data migration to cloud-based solutions: Allows for scalability and cost-effective storage options, eliminating the need for physical hardware.
3. Adopting a data lifecycle management strategy: Helps identify and categorize data based on its value, allowing for efficient management of legacy data.
4. Implementing data archiving: Moves older, less frequently accessed data to secondary storage, freeing up space on primary storage for active data.
5. Utilizing data deduplication: Reduces storage costs by eliminating duplicate data, especially from end-of-life systems.
Benefits:
1. Improved data management and organization: Upgrading or migrating to more modern solutions allows for streamlined data management processes.
2. Cost savings: Cloud-based solutions and data deduplication can significantly reduce storage costs compared to traditional hardware.
3. Reduced risk of data loss: Migrating data to new systems or archiving data helps protect against potential data loss from aging systems.
4. Compliance and regulatory compliance: Adopting a data lifecycle management strategy ensures compliance with retention and disposal policies.
5. Better utilization of resources: By freeing up space on primary storage and optimizing data storage, resources can be utilized more efficiently.
CONTROL QUESTION: What do you do when the legacy IT and storage solutions are at end of life at precisely the same time that the exponential data growth was exceeding the capabilities?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
10 years from now, my big hairy audacious goal for End of Life Management is to revolutionize the way organizations handle legacy IT and storage solutions. I envision a world where companies no longer have to worry about their systems becoming obsolete or struggling to keep up with the ever-increasing amounts of data.
To achieve this, I will lead a team in developing a cutting-edge cloud-based End of Life Management platform that seamlessly integrates with all existing IT and storage solutions. This platform will utilize advanced AI and machine learning algorithms to continuously analyze and anticipate the needs of the organization, making proactive recommendations for upgrades, replacements, and data storage optimization.
Furthermore, I will establish partnerships with top technology companies to ensure that our platform is constantly updated with the latest innovations and advancements. This will not only extend the life of legacy systems but also future-proof organizations against the exponential data growth.
With our platform, companies will no longer have to go through the costly and time-consuming process of replacing their entire IT infrastructure every few years. Our solution will significantly reduce IT costs, improve data management efficiency, and increase overall productivity.
Moreover, I will advocate for sustainability in End of Life Management by implementing environmentally friendly practices such as e-waste recycling and utilizing renewable energy sources.
My ultimate goal is to make End of Life Management a stress-free, cost-efficient, and sustainable process for organizations worldwide. By doing so, we can pave the way for a more technologically advanced and environmentally responsible future.
Customer Testimonials:
"I can`t imagine working on my projects without this dataset. The prioritized recommendations are spot-on, and the ease of integration into existing systems is a huge plus. Highly satisfied with my purchase!"
"As a researcher, having access to this dataset has been a game-changer. The prioritized recommendations have streamlined my analysis, allowing me to focus on the most impactful strategies."
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End Of Life Management Case Study/Use Case example - How to use:
Client Situation:
XYZ Corporation, a multinational conglomerate, was facing a critical challenge with their IT infrastructure. The company had been relying on legacy IT and storage solutions for several years, and these systems were now at the end of their life cycle. At the same time, the exponential growth of data within the organization had exceeded the capabilities of the existing systems. This created a significant bottleneck in the company′s operations, hindering its ability to meet the evolving business needs.
The legacy IT and storage solutions were outdated and lacked the necessary scalability and performance to handle the rising data volumes. Additionally, the systems were not designed to support modern technologies such as cloud computing, which hindered the company′s digital transformation efforts. The IT team at XYZ Corporation realized that a comprehensive solution was needed to address both the end-of-life issue and the data growth problem. They decided to seek the expertise of a consulting firm with experience in End-of-Life Management and data management strategies.
Consulting Methodology:
The consulting firm used a structured approach to assess the situation and develop a strategy for End-of-Life Management and data management. The first step involved a thorough assessment of the current IT and storage infrastructure, including hardware, software, and data management processes. The assessment revealed several issues, including outdated systems, lack of scalability, and inefficient data management practices.
Based on the assessment findings, the consulting firm recommended a three-phase approach to address the client′s challenges. The first phase involved developing a plan to replace the legacy systems with modern, scalable solutions. The second phase focused on improving data management processes and implementing a data governance framework to manage the growing data volumes effectively. In the third phase, the consulting firm worked closely with the IT team to implement the proposed solutions and provide training and support to ensure successful adoption.
Deliverables:
The consulting firm delivered a comprehensive End-of-Life Management and data management strategy to XYZ Corporation. The deliverables included a detailed roadmap for replacing the legacy systems, implementing a data governance framework, and improving data management processes. The strategy also included recommendations for modern technologies and best practices to manage data growth effectively.
As part of the implementation phase, the consulting firm provided a detailed project plan, including timelines, resource requirements, and estimated costs. They also worked closely with the IT team to develop a training program for employees to ensure a smooth transition to the new systems.
Implementation Challenges:
One of the major challenges faced during the implementation was the tight deadline for replacing the legacy systems. The client′s operations were highly dependent on the existing systems, and any downtime or disruption could have severe consequences. The consulting firm had to work closely with the IT team to develop a phased implementation plan that minimized the impact on business operations.
Moreover, implementing a data governance framework and improving data management processes required significant cultural and organizational changes. The consulting firm had to work closely with the company′s leadership and employees to ensure buy-in and successful adoption of the new practices.
Key Performance Indicators (KPIs) and Management Considerations:
To monitor the success of the project, the consulting firm and XYZ Corporation identified several key performance indicators. These included:
1. Reduction in system downtime: The implementation of modern IT and storage solutions aimed to reduce system downtime. The consulting firm set a target to achieve at least a 50% reduction in downtime compared to the previous year.
2. Improvement in data management processes: The implementation of a data governance framework and improved data management practices aimed to increase data accuracy, security, and accessibility. The consulting firm set a target to achieve a 20% improvement in these metrics within the first year after implementation.
3. Cost savings: The new IT and storage solutions were expected to provide significant cost savings by improving operational efficiency and reducing maintenance costs. The consulting firm set a target to achieve a 15% reduction in IT infrastructure costs within the first year.
Management considerations focused on the continuous monitoring and evaluation of the implemented solutions. The consulting firm recommended regular audits to ensure compliance with data governance policies and to identify any potential issues that may arise. Additionally, regular training and support were crucial to ensure employees were using the new systems and processes effectively.
Conclusion:
The End-of-Life Management and data management strategy developed and implemented by the consulting firm helped XYZ Corporation address the challenges posed by outdated IT and storage systems and the exponential growth of data.
The complete overhaul of IT and storage systems allowed the company to leverage modern technologies and improve operational efficiency. The implementation of a data governance framework and improved data management processes ensured data accuracy, security, and accessibility, supporting the company′s digital transformation efforts.
The project′s success was measured by the achievement of the set KPIs and the positive feedback from employees who highlighted the significant improvements in system performance and data management processes. The company′s leadership also expressed satisfaction with the smooth implementation and the resulting cost savings. Overall, the End-of-Life Management and data management strategy proved to be a crucial investment for XYZ Corporation, enabling it to stay competitive in today′s data-driven business landscape.
References:
1. Dell Technologies. (2018). Innovate: The end of simplicity - How the growing complexity of IT is driving up costs and holding back innovation. Whitepaper.
2. Gartner. (2019). Is your EOL server and hardware refresh a Catalyst for innovation or an obstacle? Market Trends Report.
3. Muralidharan, S., & David, G. R. (2017). End of life management of electronics. Journal of Cleaner Production, 141, 97-104.
4. Topi, H., & Valacich, J. S. (2014). Is managing end‐of‐life information technologies financially sound? An empirical investigation. Journal of the Association for Information Systems, 15(12), 773-806.
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