Enabling low /no code engineering
experience,building & operationalizing
Information Models
Adi Dev Katiyar
We are Yokogawa…
➔ Utilizing our ability to measure and connect, we are fulfilling our
responsibility for the future of our planet for 108 years… and going
strong !
➔ As a leading automation major, we serve the world’s who’s who in
the fields of Energy, Materials, Life Innovation etc.; We are digitally
transforming Industrial Automation to Industrial Autonomy
➔ Our vision is a Carbon neutral future, anchored in a circular
economy for overall well being
Characteristics of our Customer
➔ Bulk manufacturing enterprises, national & global
➔ Risk averse, HSE is of paramount concern
➔ Operational Technology (OT) & IT data islands
➔ Dominant OT Resource pool (automation etc...)
➔ Intricate & global value chains,
Our key Challenges
➔ Customer aspirations of digitally transforming their Automation
systems to Autonomous systems with a need to sweat assets
➔ Disparate data sources distributed across multiple databases, tech
& infrastructure debt
➔ Breaking data silos between Domain (not so IT savvy) and
Software (IT savvy) Engineers, challenges …
➔ Enable concurrent & progressive building of models with multiple
perspectives, promote their reuse & sharing
1.Domain /Application Engineering Enablement
How can we enable domain /application engineers to visualize an information
/domain model
★ Intuitively or graphically build
★ Templatize, reuse and extend models
★ Efficient consumption via performant APIs
Why …
➔ Need for Speed & Agility
◆ Concurrent evolution of Models
➔ Bridge the information GAP
◆ Across siloed data, across multiple databases (Business, IT, Operational
Technology (OT), Relational, NoSQL, Time Series, Hierarchical etc.)
◆ B/w Software (IT savvy) and Domain /OT (not so IT savvy) Engineers
2.Ease of Engineering Applications
How can we enable OT engineers to readily instantiate model templates & bind
them to the real-time variable during provisioning for end users
Why …
➔ provide an easy low /no code engineering mechanism for the customer
facing domain experts (not so IT savvy)
➔ Ease of Engineering & Deployment
➔ Implementing change along with evolving business circumstances and
processes
Questions continue…
Q. Readily consume /import models drawn by experts into the tech stack of
choice without the loss of context and specifics ??
Q. How to design performant models feeding into Analytics /AI pipelines ??
Q. How to derive insights from these information models stored in the db ??
Q. How to build on top of the existing investments and infrastructures ??
Q. How to support Intuitive & Generic tech stack along the model lifecycle ??
Q. What should be the appropriate tech stack enabling building and storing
models /templates, intuitive /performant retrieval, derive insights, breaking
info silos, concurrent evolution … ??
Explore Graph Dbs & Tooling
Items for evaluation Graph Technology Stack Description
Building Information
Models /templates
Arrows, Data Importer Free hand drawing easy for our
domain experts
Easily export & import the
models into the database
Storing Information
Models /Templates
GraphDb, Browser Intuitive /Performant for Storage
& Sync with other Dbs
Retrieval Information
Models
Cyphers, GraphQL Intuitive /Performant model
Retrieval
Derive Insights Bloom, GDS Graphical analytics
Concept
Thank you
Adi Dev Katiyar
adidev.katiyar@yokogawa.com
https://in.linkedin.com/in/adidk
Architect | Product Owner | Cloud
Technologies | AI/ML | Industrial
Automation | Smart manufacturing |
Enjoying being part of Industry 4.0
revolution

Enabling low /no code engineering experience, building & operationalizing Information Models

  • 1.
    Enabling low /nocode engineering experience,building & operationalizing Information Models Adi Dev Katiyar
  • 2.
    We are Yokogawa… ➔Utilizing our ability to measure and connect, we are fulfilling our responsibility for the future of our planet for 108 years… and going strong ! ➔ As a leading automation major, we serve the world’s who’s who in the fields of Energy, Materials, Life Innovation etc.; We are digitally transforming Industrial Automation to Industrial Autonomy ➔ Our vision is a Carbon neutral future, anchored in a circular economy for overall well being
  • 3.
    Characteristics of ourCustomer ➔ Bulk manufacturing enterprises, national & global ➔ Risk averse, HSE is of paramount concern ➔ Operational Technology (OT) & IT data islands ➔ Dominant OT Resource pool (automation etc...) ➔ Intricate & global value chains,
  • 4.
    Our key Challenges ➔Customer aspirations of digitally transforming their Automation systems to Autonomous systems with a need to sweat assets ➔ Disparate data sources distributed across multiple databases, tech & infrastructure debt ➔ Breaking data silos between Domain (not so IT savvy) and Software (IT savvy) Engineers, challenges … ➔ Enable concurrent & progressive building of models with multiple perspectives, promote their reuse & sharing
  • 5.
    1.Domain /Application EngineeringEnablement How can we enable domain /application engineers to visualize an information /domain model ★ Intuitively or graphically build ★ Templatize, reuse and extend models ★ Efficient consumption via performant APIs Why … ➔ Need for Speed & Agility ◆ Concurrent evolution of Models ➔ Bridge the information GAP ◆ Across siloed data, across multiple databases (Business, IT, Operational Technology (OT), Relational, NoSQL, Time Series, Hierarchical etc.) ◆ B/w Software (IT savvy) and Domain /OT (not so IT savvy) Engineers
  • 6.
    2.Ease of EngineeringApplications How can we enable OT engineers to readily instantiate model templates & bind them to the real-time variable during provisioning for end users Why … ➔ provide an easy low /no code engineering mechanism for the customer facing domain experts (not so IT savvy) ➔ Ease of Engineering & Deployment ➔ Implementing change along with evolving business circumstances and processes
  • 7.
    Questions continue… Q. Readilyconsume /import models drawn by experts into the tech stack of choice without the loss of context and specifics ?? Q. How to design performant models feeding into Analytics /AI pipelines ?? Q. How to derive insights from these information models stored in the db ?? Q. How to build on top of the existing investments and infrastructures ?? Q. How to support Intuitive & Generic tech stack along the model lifecycle ?? Q. What should be the appropriate tech stack enabling building and storing models /templates, intuitive /performant retrieval, derive insights, breaking info silos, concurrent evolution … ??
  • 8.
    Explore Graph Dbs& Tooling Items for evaluation Graph Technology Stack Description Building Information Models /templates Arrows, Data Importer Free hand drawing easy for our domain experts Easily export & import the models into the database Storing Information Models /Templates GraphDb, Browser Intuitive /Performant for Storage & Sync with other Dbs Retrieval Information Models Cyphers, GraphQL Intuitive /Performant model Retrieval Derive Insights Bloom, GDS Graphical analytics
  • 9.
  • 10.
    Thank you Adi DevKatiyar adidev.katiyar@yokogawa.com https://in.linkedin.com/in/adidk Architect | Product Owner | Cloud Technologies | AI/ML | Industrial Automation | Smart manufacturing | Enjoying being part of Industry 4.0 revolution