Dexcent IDS

We are Industrial DataOps Practitioners!
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Harnessing the Power of Data in Industrial Operations

An Industrial DataOps Practitioner is a data-savvy expert who understands the unique challenges and opportunities that exist in the industrial sector. They possess a deep understanding of industrial processes, systems, and technologies, enabling them to bridge the gap between data analytics and operational excellence.

In the fast-paced world of industrial operations, maximizing the value of data has become a critical factor for success. Enter the Industrial DataOps Practitioner—a skilled professional who specializes in harnessing the power of data to drive efficiency, optimize processes, and unlock valuable insights within industrial organizations.

Dexcent IDS as a vendor agnostic extension to your organization’s OT Digital Transformation continuous improvement journey

Identify and address optimization opportunities where technology can assist in automating some functions.

Provide assessments and recommendations on best tools for the job, execution, and Project Management.

Assist with building out new standards / processes.

Building KPIs analytics and associated reports.

Documenting as is and to be states.

Help develop or augment digital transformation plans / road maps.

Help develop or augment digital transformation plans / road maps.

Consolidate and optimize disparate applications with similar functions.

Assist with technology assessments and recommendations for data stack consolidation work.

Business requirements gathering, interviews, documentation.

Documenting as is and to be states.

Integrating data models.

Coaching, help with change management, training.

Digital tools that our Industrial DataOps consultants use to unlock industrial data and eliminate data related headaches:

An Industrial DataOps Practitioner leverages cutting-edge technologies, such as cloud computing, artificial intelligence, machine learning, and advanced analytics, to extract actionable insights from vast amounts of industrial data. They possess the technical expertise to implement and manage data platforms, develop customized algorithms, and deploy predictive models that optimize operational processes and enable predictive maintenance.

Continuous learning is at the core of an Industrial DataOps Practitioner's skill set. They stay up-to-date with the latest advancements in data technologies and industrial best practices. By acquiring new digital skills, they adapt to evolving industry trends and identify innovative opportunities to drive operational excellence, reduce costs, and enhance overall performance.

Continuous learning is at the core of an Industrial DataOps Practitioner's skill set. They stay up-to-date with the latest advancements in data technologies and industrial best practices. By acquiring new digital skills, they adapt to evolving industry trends and identify innovative opportunities to drive operational excellence, reduce costs, and enhance overall performance.

An Industrial DataOps Practitioner combines their expertise in data analytics, industrial processes, and advanced technologies to unlock the true potential of data in industrial operations. With their specialized skill set, they drive optimization, efficiency, and innovation, empowering industrial organizations to stay ahead in today's data-driven world.

We often get asked about some of the digital tools used by Industrial DataOps consultants to unlock the value in industrial data. At Dexcent IDS, we specialize in helping businesses leverage the power of Industrial DataOps consulting services and the latest digital tools to optimize their data operations and drive better business outcomes.

Data Integration Platforms

Data integration platforms are designed to streamline the process of accessing and integrating data from multiple sources. These platforms can help businesses overcome data silos and unlock the full potential of their data.

Advanced Analytics and Visualization Tools

Advanced analytics and visualization tools provide businesses with the ability to analyze complex data sets and gain valuable insights. These tools can help businesses identify trends, patterns, and outliers that may not be immediately apparent in raw data.

Cloud Computing Services

Cloud computing services provide businesses with the ability to store, process, and analyze data in a highly scalable and cost-effective manner. By leveraging cloud computing services, businesses can unlock the full potential of their data without having to make significant investments in on-premises infrastructure.

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning technologies can help businesses automate and streamline their data operations. These technologies can help businesses identify patterns in data, automate repetitive tasks, and improve the accuracy and speed of data processing.

Are you ready to harness the power of data in your industrial operations? Contact us today to learn more about how our Industrial DataOps Practitioners can help transform your organization, optimize processes, and unlock untapped value through data-driven insights. Together, let's unlock the full potential of your industrial operations.

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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
6.Discovery
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