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  • 136 Topics
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136 Topics
Alexandra Cretu
Practitioner
Alexandra CretuProduct Marketing Analyst
posted in Digitalization Community
Join our live webinar: A guide to data operations for the industry

Data operations (DataOps) is essential to providing consumers with business-ready, trusted, high-quality data. But when faced with a somewhat different data source, data type, data quality, and data consumer landscape, what are the defining factors that will significantly catalyze DataOps adoption in industrial companies? Join our upcoming webinar with Forrester, and be a part of the discussion, as we unpack the technology behind the DataOps practice. Dive into the topic of data operations for industry and hear leading analysts’ take on:-What is DataOps today? How has it evolved over the years?-How does DataOps adoption affect industrial companies?-What are the challenges when adopting DataOps?-Where do DataOps and MLOps come together? Register here

0
Alexandra Cretu
Practitioner
1 year ago
K
Practitioner
Karolina LunaHead of Solutions Enablement
published in Digitalization Community
Cross-functional Industrial DataOps

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post. Different types of cross-functionalIndustrial DataOps, like all other Ops mo

920
K
Practitioner
1 year ago
Alexandra Cretu
Practitioner
Alexandra CretuProduct Marketing Analyst
published in Digitalization Community
The journey from data fabric to DataOps to data productsBlog

 “Data has no value unless the business trusts and uses it”  There is no shortage of data in any industrial company, but there is a general lack of understanding on how to extract it, bring it together, and use it in an actionable way.There are two discomforting truths within digital transformation across our key industries; energy, utilities, and manufacturing: Digitalization PoCs are commonplace. Real ROI isn’t. Billions are invested in cloud data warehouses and data lakes. Most data ends there, unused by anyone for anything. At the heart of this data-driven value dilemma lies a confluence of challenges, ranging from the technical (How can we best organize our diverse and fluid data universe?) to the operational (How can we create new information products and services?), to the financial (How can we treat data as an asset?), to the human (How can we improve data literacy and ensure digital solution adoption in the field?).With more and more of our industrial operations data readily

480
Alexandra Cretu
Practitioner
1 year ago
Alex
Practitioner
AlexPractitioner
posted in Digitalization Community
Why industrial dataOps is fundamental when building and scaling data-driven solutions to drive sustainable operations

Developing, tracking and meeting sustainability goals is becoming increasingly important for companies in the public sphere. Many of them are turning to data-driven solutions to help them monitor, report and reduce their environmental impact. Recent regulations in Europe recognises that Data-driven solutions for GHG emissions reductions are contributing to climate change mitigation (see official site from the European Commission - link) At Cognite, we have encountered many innovative solutions that promote sustainability. Examples include:  Automating the recording and reporting of greenhouse gas emissions in industrial processes. Optimizing energy efficiency at the equipment and system level to minimize scope 2 emissions. Using robotics to detect dangerous leaks. Leveraging operational data to derive the environmental footprint of discrete products. Protecting biodiversity with the automatic detection of birds close to onshore windmills  After analysing dozens of those solutio

0
Alex
Practitioner
1 year ago
Anita Hæhre
Seasoned Practitioner
Anita HæhreHead of Academy and Community
published in Digitalization Community
Book a Demo

Schedule a personalized demo to learn how Cognite Data Fusion™ generates fast, scalable value from your data, enabling better decision making about maintenance, production and safety.In the demo, we will:Understand your priorities, initiatives and challenges that you are looking to solve Introduce Cognite and share examples of use cases we deliver for our customers today Identify areas where Cognite Data Fusion can help your organizationRequest demo with one of our product experts 

200
Anita Hæhre
Seasoned Practitioner
1 year ago
K
Practitioner
Karolina LunaHead of Solutions Enablement
published in Digitalization Community
Customer & Cognite operational responsibilities

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post.Planning your solutions and Industrial DataOps with Cognite Data FusionTo be

580
K
Practitioner
1 year ago
Stig Harald Gustavsen
Seasoned
Stig Harald GustavsenSeasoned
asked in Digitalization Community
Automatic labeling of CDF resources

One thing that would make the finding of the correct data is to label the CDF resources better with labels. https://docs.cognite.com/dev/concepts/resource_types/labels In the contextualization pipeline could things be automatically labeled such as:Document type: P&ID, PDF(Process Flow Diagram), loop diagrams, datasheets, single line. that theses documents are either labeled from the metadata. document ID schema (regex will do it) or from classification algorithm the file, and then gives it a label to the file. that we can see from the label on the resource what it is.The same is for Asset: Is it a Junction box, Transmitter, Skid, Pump, Motor, valve etc etc. Time series: is it a physical measurement, is an inference / calculation / “soft-tag”, is a parameter (like the gain or integration time on PID controller) this could be determent by simple analyzing of the data-point behavior of the time series. cause now when we search for a time series often a time series of a parameter that

1
N
Practitioner
1 year ago
bskal
Practitioner
bskalPractitioner
posted in Digitalization Community
Why Cognite: The Opportunity Cost of DIY (Part 3/3)

Morten Andreas Strøm / Ben Skal                                                                              September 12, 2022 What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources?This is a 3 part series where @Morten and I answer these questions through an indepth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (First post) Data modeling grounded in business impact (Previous post) The opportunity cost of custom building your industrial data platform (This post)  In the first Why Cognite post, we discussed the data problem Cognite Data Fusion is built to address. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are struggling to effectively use data to improve production. In the

0
bskal
Practitioner
2 years ago
Morten
Practitioner
MortenPractitioner
posted in Digitalization Community
Why Cognite: Data Modeling Grounded in Business Impact (Part 2/3)

What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This is a 3 part series where @bskal  and I answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. These posts are for those of you who are new to using Cognite Data Fusion and want to understand how we approach the challenges of working with industrial data without losing focus on delivering business impact. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (Last post) Data modeling grounded in business impact (This post) The opportunity cost of custom building your industrial data platform (DIY)  In the first Why Cognite post, Ben and I shared why we built Cognite Data Fusion. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are strugglin

1
bskal
Practitioner
2 years ago
Gabe Prado
Practitioner
Gabe PradoPractitioner
published in Digitalization Community
Operational Digital Twin: How data contextualization provides a complete, actionable understanding of industrial operations

INTRODUCTION A digital twin can be one of the most useful, insightful tools to drive industrial innovation. While the digital twin concept is no longer new, the capacity of the term continues to expand based on technological advancement, particularly in the realm of the Industrial Internet of Things (IIoT). Over time, digital twins have morphed to meet the practical needs of users. In oil and gas, for example, the possibilities of condition-based monitoring and predictive maintenance have amplified the need for a digital representation of both the past and present condition of an object or system. Gartner predicts that “by 2023, 33% of owner-operators of homogeneous composite assets will create their own digital twins, up from less than 5% in 2018” while “at least 50% of OEMs’ mass-produced industrial and commercial assets will directly integrate supplier product sensor data into their own composite digital twins, up from less than 10% today.” In the same report, Gartner indicates that

1850
Gabe Prado
Practitioner
2 years ago
Pierre Pernot
Seasoned Practitioner
Pierre PernotSeasoned Practitioner
posted in Digitalization Community
Cognite Data Fusion Tour - Part 4: Scaling

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

0
Pierre Pernot
Seasoned Practitioner
2 years ago
bskal
Practitioner
bskalPractitioner
posted in Digitalization Community
Why Cognite: Cognite Data Fusion Introduction and Reflection (Part 1/3)

Morten Andreas Strøm / Ben Skal                                                                                   August 22, 2022  Hello digitalization community.  My name is Ben Skal, and this is my first time posting to our community. At Cognite, I am part of our industry team and focus on helping our customers apply Cognite Data Fusion to solving the most difficult challenges within their operations. I’ve spent my career working in industry (11 years and counting). First, for a global steel company, then at a major process automation company, and now at Cognite. I am currently living in Austin, Texas and looking forward to e-meeting and learning from this community. The purpose of this series is to answer the following questions: What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This will be a 3 part series to precisely answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use ind

0
bskal
Practitioner
2 years ago
Alex
Practitioner
AlexPractitioner
posted in Digitalization Community
Use Case and Opportunity identification for Cognite Data Fusion

In the 1800s, enterprises organised themselves to use their capital assets effectively. Beginning in the mid-1900s, they organised to take better advantage of their people. Today, “data” are increasingly important to virtually all companies. There are many ways to “put data to work,” each with its own strengths and challenges. One option is to focus on finding and exploiting both value pools for the business and deep, fundamental technical capabilities provided by CDF. This can be done by executing an onsite Use Case Discovery Workshop.  There are three main steps to executing a Use Case Discovery Workshop: Identify qualified use case ideas Prioritize the use case ideas and select the top use cases  Detail out the top use cases  1. Identify qualified use case ideas 2. Prioritize the use case ideas and select the top use cases 3. Detail out the top use cases How do you find the best opportunity to leverage data ?

3
Alex
Practitioner
2 years ago
kelvin
Seasoned Practitioner
kelvinSeasoned Practitioner
posted in Digitalization Community
Manufacturing: Unlocking product traceability using an industrial knowledge graph

We’re @Uzair Wali and @kelvin, Senior Data Scientist and Data Science Lead in Cognite’s Manufacturing delivery team. In this post we talk about the increasingly important ability to intuitively and flexibly query data from all steps of a product life cycle and across source systems, with examples we’ve implemented on Cognite Data Fusion together with our users.The need for traceabilityIn many manufacturing industries, the ability to trace a product through its manufacturing life cycle, whether internal or supply chain is extremely important. It entails the collection and management of information regarding what has been done in manufacturing processes, from the raw materials and parts used to the shipment of finished products. An industrial knowledge graph that enables this traceability has the potential to not only let users speed up or automate existing use cases, it also opens up possibilities for considerable value addition.Typical questions A customer complains about the quality o

0
kelvin
Seasoned Practitioner
2 years ago
Gabe Prado
Practitioner
Gabe PradoPractitioner
posted in Digitalization Community
5 Data Challenges in Trading - Any others we should be thinking of?

In the spirit of summer reading- here’s a pretty interesting blog post covering 5 emerging challenges in commodity trading. What similarities/differences/additional challenges apply to power trading? Anything missing here?https://www.cognite.com/en/blog/commodity-trading-data-challenges1. Rapid increases in the number and availability of new data sources are accelerating the complexities of managing data and analytics in global markets2. Reliance on legacy systems means participants in high-paced commodity markets struggle to make data relevant and actionable3. Participants in commodity markets need to accelerate their data management and digital development just to keep up with technological improvements4. Existing data solutions in commodity markets are often designed as one-stop single solutions or platforms, with little room to create a proprietary competitive edge5. Continued growth in market complexities will require that IT platforms and data architectures are designed to remain

0
Gabe Prado
Practitioner
2 years ago
Pierre Pernot
Seasoned Practitioner
Pierre PernotSeasoned Practitioner
posted in Digitalization Community
Cognite Data Fusion Tour - Part 3

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

0
Pierre Pernot
Seasoned Practitioner
2 years ago
Pierre Pernot
Seasoned Practitioner
Pierre PernotSeasoned Practitioner
posted in Digitalization Community
Cognite Data Fusion Tour - Part 2

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

0
Pierre Pernot
Seasoned Practitioner
2 years ago
Hunter Beck
Practitioner
Hunter BeckPractitioner
posted in Digitalization Community
Analytics Translators - Are they the secret to a successful digital program?

Just stumbled upon an article from McKinsey in 2019 which articulates well what I believe is a crucial role at many of the customers I’ve worked with: the Analytics Translator. The technology in industry is continuing to move forward quickly opening up many new opportunities, but the deep domain expertise in production processes, maintenance, and more is still as important as ever. To me, the Analytics Translator role seems a perfect bridge between these two worlds. I’ve seen a number of manufacturers successfully “upskill” prior machine operators, process engineers, and more to fulfill this role. It’s always very impactful, making for significantly more effective digital initiatives. From my personal experience, I see them doing things like:Running use case workshops and prioritizations Internal training of users Adoption tracking Interim project manager during new technology initiatives Liaison with external technology partners and SIs Curious how others see this role, and whether yo

0
Hunter Beck
Practitioner
2 years ago
Pierre Pernot
Seasoned Practitioner
Pierre PernotSeasoned Practitioner
posted in Digitalization Community
Cognite Data Fusion Tour - Part 1

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Congite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualized data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

0
Pierre Pernot
Seasoned Practitioner
2 years ago
Jo Øvstaas
Seasoned
Jo ØvstaasSeasoned
posted in Digitalization Community
Big spatio-temporal (ocean) data

This is how it looks like when you push geospatial to the limit and show 4+ billion essential ocean variables (temperature, salinity, pH, oxygen etc) captured from 220,000 research cruises from 1890-YTD. Such data is very important to understand the effects of climate change, for instance ocean warming, deacidification, dead-zones (lack of oxygen), biodiversity migration and more. In the Ocean Data Platform, we have implemented something called the “Ocean Data Connector” which is a cloud-based JupyterHub infrastructure where you can analyze this data in a very efficient way.Ocean Data Platform and the “Explorer” interface 

0
Jo Øvstaas
Seasoned
2 years ago
Nicklas Lind
Seasoned Practitioner
Nicklas LindSeasoned Practitioner
posted in Digitalization Community
Hello from Oslo, Celanese! 👋🏻

 Hello everyone! Nicklas from InField here 😊 It’s been about a month since we were on the grounds with the crew at Celanese, and the InField team has been hard at work analyzing, prioritizing and planning work going forward based on all the insights gained from the visit. The visit has been invaluable and we want to keep the momentum going full steam!While we’re working on delivering new functionality and fixing bugs, I just wanted to reach out and say a huge thank you to the awesome and helpful team at Acid South, that makes this possible – we learned so much from you and it was a blast to work together on the grounds and I am very hyped about our collaboration going forward! 😊 See y’all again soon, Nicklas on behalf of InField   Cc. @Crystal Connor Richards @Kylie R @ibrahim.alsyed 

1
K
Committed
2 years ago
Gabe Prado
Practitioner
Gabe PradoPractitioner
posted in Digitalization Community
The Journey from Data Fabric to Data Products

Hello! I wanted to share a good infographic on how Industrial DataOps enables data product strategies. What is Data Fabric? What is Data Operations? What are Data Products?I was just at a Utility Analytics conference the other week in the US where this was a big topic of discussion- how to embed domain expertise in governed data products through joint ownership between the data product managers and the data product consumers. Several utilities are already formalizing these teams internally into a mesh-based architecture.Power Market Operations is a great example of how a data product strategy can enable smarter pre-trade analytics. Who is already executing a data product strategy?

0
Gabe Prado
Practitioner
2 years ago
S
Practitioner
Susan PetersonPractitioner
posted in Digitalization Community
Cognite joins fellow Oil & Gas companies in making cyber resilience pledge

Proud to be a Cogniter!  Cognite is a signatory of the World Economic Forum’s Cyber Resilience Pledge.  We are among 18 global oil and gas ecosystem companies championing a unified approach to mitigating growing cyber risks.  This kind of commitment and collaboration will make mission critical industries safer from cyber threats.https://www.weforum.org/press/2022/05/global-ceos-commit-to-collective-action-on-cyber-resilience-ffa0ba5f56/ (edited)  World Economic ForumGlobal CEOs Commit to Collective Action on Cyber ResilienceSahil Raina, Public Engagement, World Economic Forum, +41-795596273, sahil.raina@weforum.org

0
S
Practitioner
2 years ago
bernhardpg
Practitioner
bernhardpgPractitioner
posted in Digitalization Community
Making Point Clouds Smarter

Through 3D scanning technology, Cognite obtains accurate point clouds of large-scale industrial plants with a high level of detail. We're currently using these point clouds for visualization and measurement purposes to provide value to our customers.However, we're still missing a fundamental understanding of the objects located in a three dimensional scene. This understanding starts with semantic segmentation, the process of assigning each a class to each point in a point cloud. In the D-MVP and 3D team, we are currently working hard on solving this with state-of-the-art Deep Learning technology. Our goal is to automatically connect assets in a point cloud directly to Cognite Data Fusion, enabling rapid development of fully contextualized as-built digital twins.In the attached video you'll see what we are aiming to achieve: A point cloud from an industrial site, where all the points are split into different classes. Let me know what you think eller I'd love to hear your thoughts  

1
Trygve Utstumo
Practitioner
2 years ago
Alexandra Cretu
Practitioner
Alexandra CretuProduct Marketing Analyst
posted in Digitalization Community
Why understanding the modern data stack is priority #1 to digitalisation executives in industry

Only a fool believes in different outcomes by doing the same as before.The modern data stack - a more nuanced view of data platforms - is quickly gaining ground, focusing on making data truly useful, not just storing it in the cloud. Modern data stack based platforms are the only means of moving beyond costly, monolithic, closed business applications that maintain business and data silos, preventing real digital transformation.Platforms themselves are equally no longer monolithic products, but equally composed of interoperable platforms services from multiple open platforms. Open platforms with composable business applications are the new technology imperative. Old technology stacks, and “lets only focus on the discrete business solution at hand” approaches don’t work for the 2020s enterprise.OT, IT, and business must work together to prevent tug of wars - and instead - collaborate to secure competitiveness in the new platforms era.Read the full article:  Did this article cause some re

0
Alexandra Cretu
Practitioner
2 years ago

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