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Data Maturity Assessment for Microsoft Dynamics 365
Focusing on artificial intelligence (AI) or analytics without getting your data maturity right is like trying to run before you learn to walk. That is where organizations fail. Each data process is a building block for your artificial intelligence program. If you don’t have processes for data quality and you use garbage data to train your AI program, it is no surprise that the output you receive will be incorrect too. Before you put in any time and money, building an AI program or implementing sophisticated AI solutions, take our Data Maturity Assessment.
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Data Integration
1. How seamlessly does your organization connect data across systems?
Data is siloed with minimal integration.
We have basic integration between a few key systems.
Most systems are integrated with some real-time capabilities
All systems are fully integrated with real-time data flow.
2. What tools or platforms are used for data integration?
We rely on manual processes or spreadsheets.
We use basic ETL tools for some integrations.
We use advanced ETL tools and platforms.
We use a comprehensive integration platform with real-time capabilities.
3. Are there any real-time data integration requirements?
No, we do not have real-time requirements.
Some processes require near real-time data.
Many processes benefit from real-time data, but it’s not fully implemented.
Real-time data integration is essential and fully implemented.
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Data Migration
4. How do you handle data migration during system upgrades or transitions?
We rely on manual processes with significant downtime.
We have some automated scripts but still experience challenges.
We use migration tools with minimal downtime.
We have fully automated migration processes with seamless transitions.
5. What strategies are in place to ensure data consistency during migration?
We have no formal strategies in place.
We perform manual checks post-migration.
We have automated consistency checks during migration.
We have comprehensive strategies that ensure consistency without manual intervention.
6. Have you automated data migration processes?
No, we rely on manual processes.
We have partially automated some processes.
Most of our migration processes are automated.
All migration processes are fully automated.
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Data Governance
7. How well-defined are your data policies and standards?
We have no formal data policies or standards.
We have some policies, but they are not well enforced.
We have well-defined policies that are somewhat enforced.
Our policies are comprehensive and strictly enforced.
8. Is there a data governance team responsible for enforcing policies?
No, we do not have a dedicated data governance team.
We have a team, but they are not fully empowered.
We have an established team that enforces policies regularly.
We have a dedicated and empowered team that ensures full compliance.
9. How do you manage data quality and compliance?
We have no formal data quality management.
We rely on manual checks for data quality.
We have automated tools for data quality management.
We have comprehensive systems ensuring high data quality and full compliance.
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Data Security
10. What measures are taken to protect sensitive data?
We have minimal security measures in place.
We have basic security controls like passwords and access levels.
We have advanced security measures including encryption and role-based access.
We have a robust security framework with regular audits and real-time monitoring.
11. Are there access controls in place?
No, all data is generally accessible.
We have basic access controls.
We have role-based access controls with regular reviews.
We have strict access controls with multi-factor authentication and regular updates.
12. How often is data security audited?
Rarely or never.
Occasionally, without a set schedule.
Annually, with some corrective actions.
Regularly, with continuous monitoring and proactive security enhancements.
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Data Analytics
13. How mature is your organization’s analytics capability?
We primarily use basic reporting.
We use basic descriptive analytics and dashboards.
We use advanced analytics including predictive models.
We have fully integrated AI-driven analytics for strategic decision-making.
14. What tools or platforms are used for data analysis?
We rely on basic spreadsheets or manual analysis.
We use basic BI tools.
We use advanced BI platforms and tools.
We use cutting-edge AI and machine learning platforms.
15. Are insights from analytics integrated into decision-making processes?
Insights are used informally and inconsistently.
Insights are used regularly in operational decisions.
Insights are a key part of tactical decisions across departments.
Insights are integral to strategic decision-making and are embedded in workflows.
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Strategy
16. Is there a clear vision for data and analytics within your organization?
No, data and analytics are not a priority.
There is some focus, but no clear vision.
We have a vision, but it’s not fully aligned across the organization.
We have a clear, organization-wide vision for data and analytics.
17. Are data and analytics initiatives aligned with business goals?
No, they operate independently.
They are somewhat aligned, but with gaps.
They are mostly aligned, with some areas needing improvement.
They are fully aligned and integrated into the business strategy.
18. How well-defined is your data and analytics strategy?
We do not have a formal strategy.
We have a basic strategy that’s not well documented.
We have a well-documented strategy that is partially implemented.
We have a comprehensive and fully implemented strategy.
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AI (Artificial Intelligence)
19. To what extent do you leverage AI and machine learning?
We do not use AI or machine learning.
We have some experimental projects using AI/ML.
AI/ML is used in some critical areas.
AI/ML is integral to our operations and decision-making.
20. Are AI models integrated into decision-making processes?
No, AI models are not used for decision-making.
AI models are occasionally used for decision-making.
AI models are regularly used and provide significant input.
AI models are deeply integrated into all decision-making processes.
21. How mature is your organization’s AI adoption?
We have not adopted AI.
AI adoption is in early stages with limited use cases.
AI is moderately adopted with growing use cases.
AI is fully adopted and a critical part of our operations.
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Talent
22. Do you have skilled data and analytics professionals?
We have limited skills in data and analytics.
We have some skilled professionals, but they are not fully utilized.
We have a skilled team with defined roles.
We have a highly skilled and collaborative team with continuous development.
23. Is there a talent development plan in place?
No, we do not have a formal talent development plan.
We have a basic plan, but it’s not well implemented.
We have a structured plan that is partially implemented.
We have a comprehensive talent development plan that is fully implemented.
24. How well does your team collaborate across functions?
Collaboration is minimal or non-existent.
There is some collaboration, but it’s inconsistent.
Teams collaborate regularly but could improve.
There is strong collaboration across all functions.
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Data Management
25. How robust are your data management practices?
Data management practices are minimal or ad-hoc.
We have basic data management practices, but they are not standardized.
We have well-established data management practices.
Our data management practices are robust and fully standardized.
26. Is data quality prioritized?
Data quality is not a priority.
We focus on data quality, but it’s inconsistent.
Data quality is a priority, and we have processes in place to maintain it.
Data quality is a top priority, with comprehensive processes ensuring high standards.
27. Do you have a data catalog and lineage tracking?
No, we do not have a data catalog or lineage tracking.
We have some basic documentation, but it’s incomplete.
We have a data catalog, but lineage tracking is limited.
We have a comprehensive data catalog with full lineage tracking.
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Data Maturity Assessment Score
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