Dexcent IDS

We are Industrial DataOps Practitioners!

Identifying the Key Challenges Industries Face on the Road to Digital Transformation

As Industrial DataOps Practitioners, we can identify several key challenges that industries face in their digital transformation efforts. We took some time to gather common issues faced by many of our clients. Here are the top five problems that they identified:

1. Data Silos:

Many industries have accumulated vast amounts of data over the years, but this data is often stored in isolated silos within different departments or systems. Breaking down these data silos and integrating data from disparate sources is a significant challenge. It requires a cohesive data strategy, standardized formats, and efficient data governance practices. The other thing that breaking these data silos down are skilled people who have the time to invest in implementing a data strategy.

2. Legacy Systems:

Industrial sectors often rely on legacy systems that were not designed to handle modern data volumes and processing requirements. These systems can be difficult (as we all know) to integrate with new digital solutions, leading to data inconsistencies, operational inefficiencies, and limited scalability. Transforming and modernizing these legacy systems while ensuring minimal disruption is a complex task. Particularly the part about minimal disruption to scheduling and production.

3. Data Quality and Integrity:

Data is the foundation of digital transformation, but ensuring data quality and integrity is a constant challenge. Inaccurate or incomplete data can lead to flawed insights and poor decisions, impacting overall business performance. Implementing robust data governance frameworks, data cleansing processes, and data quality checks is essential for maintaining data reliability.

4. Security and Privacy:

With the increasing digitization of industrial processes, cybersecurity threats have become a significant concern in today’s world. Protecting sensitive data, intellectual property, and operational systems from cyberattacks is critical. Implementing robust security measures, conducting regular audits, and staying updated with the latest security practices are crucial in maintaining a secure digital environment.

Lastly is the problem that we consider most important to be addressed:

5. Talent Gap:

The successful implementation of digital transformation initiatives requires skilled professionals who understand both the industrial domain and modern data technologies. However, there is a shortage of talent with expertise in areas such as data analytics, dataops practitioners, machine learning, and data engineering. Bridging the talent gap by upskilling existing employees and attracting new talent is vital for driving successful digital transformation.

Addressing these challenges requires a holistic approach, involving the collaboration of various stakeholders, including business leaders, IT teams, data scientists, and domain experts. It also necessitates a clear vision, adequate resources, and a commitment to adapt and evolve in the face of ongoing technological advancements. It is like the old saying goes “Anything worth while takes effort”.

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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