Dexcent IDS

We are Industrial DataOps Practitioners!

People Readiness Assessment

By conducting a people readiness assessment, organizations can identify strengths, weaknesses, and areas for improvement in their workforce. This assessment helps in planning targeted training programs, change management strategies, and creating a culture that supports effective asset management practices, ultimately contributing to improved asset performance and organizational success.

Skills and Competency Assessment : Evaluating the skills and competencies of personnel involved in asset management, maintenance, and operations. This includes assessing technical skills, knowledge of asset management practices, and proficiency in using relevant technologies or systems.

Identification of Training Needs Evaluation : Identifying gaps in skills or knowledge and determining the training needs of employees. Determines the need for training programs related to asset management practices, use of specific tools or software, safety protocols, or new technologies.

Cultural Assessment and Change Management Assessment : Examines the organizational culture and readiness for change related to asset performance initiatives. Evaluates the attitudes, perceptions, and willingness among employees to adapt to new processes or technologies.

Stakeholder and Leadership Engagement Readiness : Assessing the leadership's support and engagement in promoting asset performance initiatives to ensure that leadership communicates the importance of asset management and encourages employee participation and support.

Adaptability and Resilience Assessment : Assesses the workforce's adaptability and resilience to changes in asset management practices or technologies. Identifying individuals or teams that are more receptive to change and can act as corporate champions for new initiatives.

Performance Metrics Alignment Review : Ensures that employees understand key performance indicators (KPIs) related to asset performance management. Aligning individual or team goals with organizational objectives for improved performance.


Efficiency and Streamlined Operations : Process alignment ensures that workflows are optimized for digital tools and technologies. It eliminates redundancies, streamlines processes, and removes bottlenecks, leading to increased operational efficiency and smoother interactions between systems and teams.

Enhanced Agility and Adaptability : Aligned processes are more agile and adaptable to changes brought about by digital transformation. They can flexibly accommodate new technologies, shifting market demands, or evolving customer needs without significant disruptions, ensuring the organization remains competitive and responsive.

Improved Customer Experience: Streamlined and aligned processes often translate to improved customer experiences. When workflows are optimized, customer interactions become more seamless, quicker, and more tailored to individual needs, enhancing overall satisfaction and loyalty.

Data Integration and Insights : Aligned processes facilitate better integration of data across systems and departments. This integration allows for a holistic view of operations, enabling organizations to gather insights from data analytics more effectively. These insights can drive informed decision-making and strategic planning.

Risk Mitigation and Compliance : Aligned processes often include built-in risk management measures and compliance standards. By ensuring that processes adhere to industry regulations and internal policies, organizations mitigate operational risks and reduce the likelihood of compliance-related issues.

Resource Optimization : Process alignment enables better resource allocation and utilization. By eliminating unnecessary steps or inefficiencies, organizations can optimize resources—whether it's human capital, time, or technology—to focus on high-priority tasks that contribute more directly to the digital transformation goals.

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Table of Content

1. Purpose
1.1. Purpose and Goals
1.2. Why The Industrial DataOps Process Is Needed?
1.3. Industrial DataOps Practitioner Engagement
1.3.1. Oversee An Existing Industrial DataOps Program
1.3.2. High Data Secrecy Organizations
1.3.3. Full Engagement
1.4. Principles
1.4.1. Know Your Data
1.4.2. Curate Your Data
1.4.3. Unify Your Data
1.4.4. Analyze Your Data
1.4.5. Hardware, Software, and People Working Together
1.5. Lifecycle
2. Intention
2.1. Scope
2.2. Assumptions
3. Terminology & References
3.1. Definitions
3.2. Acronyms and Abbreviations
3.3. Industry References, Standards, Regulations and Guidelines
3.4. Site Related References, Standards, Regulations and Guidelines
4. Expectations and Responsibilities
4.1. Roles
4.2. Role Job Description
4.3. Role Assignment
5. Opportunity Identification
5.1. Need Initiated
5.2. Improvement Initiated
7. Baselining
7.1. Data Rationalization
7.2. Data Justification
7.3. Data Impact
7.4. Data Flow
7.4.1. Data Producer
7.4.2. Data Path
7.4.3. Data Consumer
7.5. Data Good State
7.5.1. Failure Conditions
7.5.2. Warning Conditions
7.5.3. Abnormal Conditions
7.6. Data Processing Team
8. Target Confidence Factors
9. Critical Success Factors
10. Risk Analysis / Mitigation Plan
10.1. Risk Analysis
10.2. Mitigation Plan
11. Technology Selection
11.1. Hardware
11.2. Software
11.3. People
12. Project Execution
12.1. Project Synergy
12.2. Project Synergy
12.3. Resource Acquisition
12.4. Scheduling
12.5. Implementation
12.6. Training
12.7. Maintenance
12.8. Contingency
13. Evaluation Vs Baseline
14. Calibration & Sustainment
14.1. Training
14.2. Maintenance
14.3. Obsolescence
15. Continuous Improvement Process
15.1. Continuous Process Documentation
15.2. Audit
16. Management Of Change (MOC)
16.1. Applicability
16.2. Methodology