SPDM maturity survey
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Optimize Your Simulation Data Management!
Uncover the risks with this quiz and optimize your simulation data management for success. Think your simulation data in under control and its management is cutting-edge? This quiz will take you on a journey through your simulation data management practices, exposing the cracks and practices that might be hindering your progress. Take the test and discover if your simulation processes are fit for the modern world and how to unlock their full potential!
For each question, choose the answer that best reflects your company current situation:
1.
Data Accessibility:
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Data is readily available in a centralized system and easily searchable by authorized users across teams.
Some data is siloed, requiring specific permissions or technical knowledge.
Accessing data often involves manual processes, delays, or limited permissions.
Key data is missing, inaccessible, or requires significant effort to retrieve.
2.
Model Change Tracking:
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A comprehensive Simulation Data Management system tracks and documents model changes with rationale and versioning.
Changes are manually tracked or inconsistently documented across teams.
Tracing changes is difficult or impossible, limiting transparency and accountability.
Changes are rarely tracked, making it hard to understand model evolution.
3.
Data Inconsistencies:
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Inconsistencies are rare and quickly resolved with minimal impact on decisions.
Occasional inconsistencies arise but have procedures for identification and correction.
Inconsistencies are frequent, leading to conflicting results and bad decisions.
Lack of tools or processes to detect and address inconsistencies, impacting data reliability.
4.
Input Data Preparation Time:
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Data collection and verification take less than 5% of simulation time.
Time spent on Input data preparation is manageable, typically under 15%.
Data preparation consumes 15-30% of simulation time, hindering efficiency and agility.
Over 30% of time is spent on data, creating a significant bottleneck and delaying progress.
5.
Data Flow Management:
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Data seamlessly flows across departments and systems, readily available for analysis.
Some data silos exist, but mechanisms for sharing and collaboration are effective.
Data flow is inefficient, requiring manual intervention for sharing and integration.
Data is fragmented and isolated, making cross-functional analysis impossible.
6.
New Requirements Adaptability:
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Actively seek out and embrace new requirements, easily to adjust simulations and data processes to accommodate them.
Open to new requirements but extra time and effort is required to adapt existing systems and practices.
Struggle to adapt to new requirements due to limitations in our data infrastructure or resistance to change.
Resistant new requirements, preferring to stick with tried-and-tested methods and data sets.
7.
Model Tracking and Evolution:
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Can easily trace the origin, development, and changes of any model and its results.
Tracking model evolution is possible, but requires manual effort or specialized tools.
Lack a clear understanding of how models have evolved over time, impacting knowledge transfer.
Older models and their results are often lost or inaccessible, hindering learning and improvement.
8.
Data Reuse:
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Regularly reuse simulation data for new projects and analyses, maximizing its value.
Reuse data sometimes, but it requires significant cleaning and adaptation effort.
Data reuse is limited due to inconsistencies, lack of documentation, or technical barriers.
Rarely reuse data, treating each simulation as a standalone project, failing to optimize resources.
9.
Data Visualization and Interpretation:
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Data is readily visualized and easily understood by all stakeholders across departments.
Decent visualization tools exist, but some data is challenging to interpret for non-experts.
Data visualization is limited or complex, hindering informed decision-making and stakeholder engagement.
Lack the tools or skills to effectively visualize and interpret data, leading to confusion and misinterpretations.
10.
Simulation Collaboration:
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Seamless collaboration on simulation projects with effective knowledge sharing and insights exchange.
Have collaboration tools, but communication and knowledge sharing can be improved.
Collaboration on simulations is siloed and often inefficient, hindering progress and innovation.
Cross-team collaboration on simulations is non-existent or discouraged, limiting potential synergy.
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