Dexcent IDS

We are Industrial DataOps Practitioners!

Source Data Assessment

In asset performance management, source data assessment refers to the evaluation and validation of the data sources used to monitor, measure, and analyze the performance of assets within an organization. It involves scrutinizing the quality, reliability, accuracy, and completeness of the data obtained from various sources before using it for asset performance analysis or decision-making processes.

Data Quality Check : Assesses the quality of data obtained from various sources, including sensors, monitoring systems, maintenance records, IoT devices, and other data collection methods. This involves evaluating factors such as accuracy, consistency, timeliness, and relevance of the data.

Data Completeness and Consistency : Ensures that the data collected from different sources is complete and consistent. This involves verifying that there are no missing values or discrepancies that could affect the reliability of analysis or decision-making.

Data Accuracy Verification: Validation of the accuracy of the data by comparing it with established standards, benchmarks, or known values. This verification process helps in identifying errors, outliers, or inconsistencies within the data that will cause issues later.

Data Relevance and Timeliness Evaluation : Data Relevance and Timeliness Evaluation: Evaluation of whether the data being collected is relevant to the specific asset performance metrics being analyzed. It also involves ensuring that the data is up-to-date and timely for effective decision-making.

Data Security and Privacy Assessment : Evaluation of data security measures to ensure that sensitive asset performance data is adequately protected from unauthorized access, breaches, or manipulation.

Data Integration and Compatibility Validation : Assess the compatibility and integration of data from different sources. This involves ensuring that data collected from various systems or sources can be effectively integrated and used together for comprehensive asset performance analysis.

Data Governance Assessment : Evaluation of the data governance practices and documentation protocols of the organization. This includes examination of procedures for data collection, storage, and validation. The assessment ensure that there is documentation of data sources and processes for future reference, compliance, or auditing purposes.


Accuracy Assurance : Validating source data ensures that the information used for analyses is accurate and free from errors. This accuracy is fundamental in making informed decisions about digital readiness and asset performance initiatives.

Reliable Insights : Valid data leads to reliable insights. By validating source data, organizations can trust the findings and conclusions drawn from their assessments, enabling them to make informed decisions about their asset management strategies.

Improved Decision-Making : Accurate data enables better decision-making. When conducting operational digital readiness assessments, validated source data provides a solid foundation for identifying areas needing improvement and making decisions on resource allocation and implementation strategies.

Risk Mitigation : Validation helps in identifying and mitigating risks associated with inaccurate or unreliable data. By ensuring the data's accuracy, organizations reduce the chances of making decisions based on faulty information, minimizing potential risks.

Enhanced Efficiency and Effectiveness: Validated data streamlines the assessment process, saving time and resources that might otherwise be spent rectifying errors or dealing with inconsistencies and allowing for more efficient and effective evaluation of digital readiness levels.

Benchmarking and Progress Tracking : Validated data provides a reliable benchmark for measuring digital readiness over time. It allows organizations to track progress accurately, identify trends, and assess the impact of interventions or changes made based on the assessments.

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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