Transforming Requirements Management
Three Strategic Considerations for Toolchain Modernization
Why do legacy tools fall short for requirements management?
Manual Errors
Lack of Traceability
Costly Maintenance
Limited collaboration
Spreadsheets and documents stored in shared folders or emails hinder real-time collaboration and introduce version control issues.
Limited Collaboration
Manual Errors
Frequent manual updates increase the risk of human error, affecting the accuracy of requirements and slowing down the development process.
Lack of Traceability
Outdated toolsets, often stitched together manually, lack the integration needed for seamless traceability across different stages of the product lifecycle.
Costly Maintenance
Keeping legacy systems operational involves high maintenance costs, often without delivering the efficiency required in modern development environments.
Limited Collaboration
Spreadsheets and documents stored in shared folders or emails hinder real-time collaboration and introduce version control issues.
Manual Errors
Frequent manual updates increase the risk of human error, affecting the accuracy of requirements and slowing down the development process.
Lack of Traceability
Outdated toolsets, often stitched together manually, lack the integration needed for seamless traceability across different stages of the product lifecycle.
Costly Maintenance
Keeping legacy systems operational involves high maintenance costs, often without delivering the efficiency required in modern development environments.
1
tooling
strategy
2
adoption
strategy
3
data migration
strategy
Commit to the future state of your toolchain
Consider factors such as the data volume, system architecture, dependencies, budget, business continuity risks, and operational expertise. Organizations typically opt for one of these three approaches:
Best-of-breed systems
Best-of-Breed systems are specialized tools designed to excel in a specific function, such as requirements management, testing, or quality assurance. These systems are chosen for their superior functionality in a particular area.
Integrated toolchains refer to platforms designed to cover a wide range of functions under one system, providing an end-to-end solution. These platforms unify tools for requirements management, testing, version control, and more into one seamless experience.
Some organizations build homegrown solutions-customized systems developed in-house to meet specific requirements. These are tailored to the company's needs, often using a mix of proprietary and open-source technologies.
Integrated toolchains
Homegrown solutions
Integrated toolchains refer to platforms designed to cover a wide range of functions under one system, providing an end-to-end solution. These platforms unify tools for requirements management, testing, version control, and more into one seamless experience.
Homegrown solutions
Integrated toolchains
Best-of-breed systems
Some organizations build homegrown solutions-customized systems developed in-house to meet specific requirements. These are tailored to the company's needs, often using a mix of proprietary and open-source technologies.
Homegrown solutions
Integrated toolchains
Best-of-breed systems
Tooling Strategy
Commit to the future state of your toolchain
Consider factors such as the data volume, system architecture, dependencies, budget, business continuity risks, and operational expertise. Organizations typically opt for one of these three approaches:
Best-of-Breed systems are specialized tools designed to excel in a specific function, such as requirements management, testing, or quality assurance. These systems are chosen for their superior functionality in a particular area.
Integrated toolchains refer to platforms designed to cover a wide range of functions under one system, providing an end-to-end solution. These platforms unify tools for requirements management, testing, version control, and more into one seamless experience.
Some organizations build homegrown solutions-customized systems developed in-house to meet specific requirements. These are tailored to the company's needs, often using a mix of proprietary and open-source technologies.
1
tooling
strategy
2
adoption
strategy
3
data migration
strategy
Pick a strategy for making the transition
Consider factors like opportunity costs, system downtime, historic data preservation, audit and compliance purposes. There are two primary tool adoption strategies:
Rip and replace
This approach involves completely replacing a legacy system with a modern tool in one decisive move.
Co-existence
In this approach, the legacy system and the new tool operate side-by-side for a transitional period. The legacy tool remains in use for specific functions, such as audit or compliance, while the new system is gradually adopted.
Co-existence
Rip and replace
Adoption Strategy
Pick a strategy for making the transition
Consider factors like opportunity costs, system downtime, historic data preservation, audit and compliance purposes. There are two primary tool adoption strategies:
Rip and replace
This approach involves completely replacing a legacy system with a modern tool in one decisive move.
Co-existence
In this approach, the legacy system and the new tool operate side-by-side for a transitional period. The legacy tool remains in use for specific functions, such as audit or compliance, while the new system is gradually adopted.
1
tooling
strategy
2
adoption
strategy
3
data migration
strategy
Choose an approach for migrating data
Based on your tooling and adoption strategies, consider factors like data complexity, volume, interlinkages, and compliance needs. There are multiple ways to migrate (or leave behind) data:
Cap and Grow
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Big Bang
Agile, incremental, non-disruptive
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
The big bang approach involves migrating all data from the legacy system to the new one in a single, large-scale operation. This is the most straightforward, but also the riskiest strategy. Despite the risks, big bang can be effective for smaller, less complex systems or when rapid transition is essential.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
The agile incremental approach is a phased, iterative method for migrating data. It offers flexibility and minimizes disruption. It is ideal for larger organizations or projects with complex data structures, as it provides better control over the migration process and reduces overall risk.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
The big bang approach involves migrating all data from the legacy system to the new one in a single, large-scale operation. This is the most straightforward, but also the riskiest strategy. Despite the risks, big bang can be effective for smaller, less complex systems or when rapid transition is essential.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
The agile incremental approach is a phased, iterative method for migrating data. It offers flexibility and minimizes disruption. It is ideal for larger organizations or projects with complex data structures, as it provides better control over the migration process and reduces overall risk.
Agile, incremental, non-disruptive
Big Bang
Cap and Grow
data migration Strategy
Choose an approach for migrating data
Based on your tooling and adoption strategies, consider factors like data complexity, volume, interlinkages, and compliance needs. There are multiple ways to migrate (or leave behind) data:
Cap and Grow
This method involves limiting the use of the legacy system (capping) while gradually adopting the new tools (growing) for broader team use. This strategy works best for organizations that don't require all historical data to be moved and can afford to maintain the legacy tool for specific use cases.
Big Bang
The big bang approach involves migrating all data from the legacy system to the new one in a single, large-scale operation. This is the most straightforward, but also the riskiest strategy. Despite the risks, big bang can be effective for smaller, less complex systems or when rapid transition is essential.
Agile, incremental, non-disruptive
The agile incremental approach is a phased, iterative method for migrating data. It offers flexibility and minimizes disruption. It is ideal for larger organizations or projects with complex data structures, as it provides better control over the migration process and reduces overall risk.
Learn more about making a smooth transition
Common myths and misconceptions to debunk before embarking on your modernization journey
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Actionable best practices for product teams aiming to update their toolchain
Benefits teams experience by making a well-planned transition to modern tools
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Benefits of data-enabled intelligent manufacturing
Using a solution with these four key components, manufacturers can:
Monitor production
Improve efficiency
Make data-driven decisions
Reduce downtime
Optimize operations
Quickly react to changes
How PTC Kepware can help
Kepware, a software solution developed by PTC, simplifies the process of collecting, monitoring, and controlling data from multiple sources. This allows manufacturers to optimize their operations, improve efficiency, and make data-driven decisions. To that end, PTC Kepware:
Offers a reliable communication platform.
Enables seamless data exchange and integration.
Includes a portfolio of industrial connectivity solutions.
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