Dexcent IDS

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Shoreline.io and Dexcent Partner to Streamline Industrial Data Management for the IIoT

Redwood City, CA, Aug 11, 2023 – Shoreline.io, a leading provider of cloud-based data management solutions, has announced a new partnership with Dexcent, a software solutions provider for industrial data systems. The partnership will focus on streamlining industrial data management for the Industrial Internet of Things (IIoT), providing end-users with a comprehensive and integrated solution for their data management needs.

With this partnership, Dexcent will integrate Shoreline.io’s cloud-based data management solution into its industrial data systems, providing customers with a reliable and scalable platform to collect, manage, and analyze data from various industrial sources in real-time. This will enable end-users to best compete in the IIoT era, empowering them with the key data tools needed to facilitate enterprise-wide data sharing.

“We are excited to partner with Dexcent to streamline industrial data management for the IIoT,” said Chareles Cary, Chief Technology Officer of Shoreline.io.”Our cloud-based data management solution is perfectly suited for the needs of the industrial sector, and we look forward to working with Dexcent to provide a more comprehensive and integrated solution for our customers.”

Dexcent provides software solutions that enable companies to collect, manage, and analyze data from various industrial sources in real time. With Shoreline.io’s cloud-based data management solution, Dexcent can further enhance its industrial data systems, providing customers with a more comprehensive and integrated solution for their data management needs.

“We are thrilled to partner with Shoreline.io to provide our customers with a more comprehensive and integrated solution for their data management needs in both North America and Europe,” said Andrew Capper, Founder of Dexcent. “This partnership will enable us to better serve our customers and empower them to best compete in the IIoT era.”

Shoreline.io and Dexcent will work closely together to integrate Shoreline.io’s cloud-based data management solution into Dexcent’s industrial data systems. The partnership is expected to provide customers with a streamlined and reliable platform to collect, manage, and analyze data from various industrial sources in real-time, empowering them to best compete in the IIoT era.

For more information about Shoreline.io and Dexcent, please visit their respective websites at www.shoreline.io and www.dexcent.com.

About Shoreline.io:
Shoreline.io is a leading provider of cloud-based data management solutions for industrial data systems.
The Shoreline.io platform enables companies to collect, manage, and analyze data from various industrial sources in real-time, providing customers with a reliable and scalable platform to best compete in the Industrial Internet of Things (IIoT) era.

www.shoreline.io

About Dexcent:
Dexcent is a software solutions provider for industrial data systems. Dexcent enables companies to collect, manage, and analyze data from various industrial sources in real-time, providing its customers with comprehensive and integrated solutions for their industrial data management needs.
www.dexcent.com

 

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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