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When we founded Cognite four years ago, one of the success criteria we envisioned was that a developer should be able to write a useful industrial application within one hour of being onboarded to Cognite Data Fusion. Coming from the consumer software industry, we did not quite realize how ambitious that was until later. Just think of what it was like to write a mobile phone application 15 years ago. There was no iOS or Android. If you were lucky, you would be writing custom J2ME code for each handset. And there were thousands of different ones, all doing things differently. Fast forward to today, and it is possible for a committed developer to write and distribute a mobile application that runs on most smartphones in the world in a weekend. The industrial world has a similar fragmentation issue that Android and iOS addressed a decade ago. Hundreds of different OT (Operational Technology) systems are present even within a single mid-sized industrial company. They do things slightly dif

Picture this scenario:A building materials company has just produced a fresh batch of cement. To test the quality of the cement, the company then produces a concrete element. Weeks later, once the concrete has hardened, the quality control turns up an issue. The cement wasn’t up to standard.Why wasn’t the issue detected earlier? In this scenario, there wasn’t a live sensor that could predict the quality of the finished product early on in the production process. But even in the cases where sensors do exist, they sometimes stop working — or are never installed in the first place. The manufacturing industry has a sensor problem. To fix it, we need to look at how other heavy-asset industries use machine learning and root cause analysis to produce data-driven predictions that manufacturers can act on with confidence to optimize production and reduce waste.Read more.

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 industrial IoT. Over time, digital twins have morphed to meet the practical needs of users. In Oil & 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.”1 In the same report,  Gartner indicates that digitalization will motivate  ind

Manufacturing companies face an uncertain business environment filled with trade conflicts, fluctuating raw material costs, and evolving consumer demands. This uncertainty is driving business leaders to look inward, where investments in information and operational technologies could bolster their bottom lines. Intelligent, interconnected, and automated factories allow manufacturers to scale and adapt capabilities as they seize new opportunities and respond to changing demands. In this way, new digital technologies deliver tangible solutions for manufacturers facing unpredictable market realities.We took a closer look at the top digitalization trends changing the manufacturing industry. We discovered not only how these technologies transform manufacturing, but how they fit into a workforce management model that future-proofs manufacturing environments and drives long-term business value as well.Read the full trend report here.

Anatomy of a contextualization engine for AI use case scaling in industryIf there is one thing we at Cognite get a lot of questions about, it’s contextualization. Not so much what is contextualization, but specifically on two subsequent topics:How does your contextualization engine actually work?  How does contextualization make use case scaling order of magnitude (or two!) more efficient?In this article, we will address both the above questions. We will also offer an executive summary on data contextualization and its role in modern data management towards the end for completeness. Let’s dive in!Read the article here.

Controlled water flow is the obvious center of all hydropower operations. This is where stored, potential energy transforms into valuable, sustainable power — a source which accounts for more than 1300 GW of electricity produced worldwide.Hydropower operations have been evolving through technology, process, and approach in order to control, optimize, and take advantage of this water flow. With the same end objective in mind — improved operational efficiency and adaptability to compounding energy industry pressures — operators can take a similar approach to data flow and transformation.Read more.

In 2020, resiliency has taken on expanded meaning as utility companies worldwide adopt new, evolving strategies for new, evolving norms.Defined as “the capacity to recover quickly from difficulties; toughness,” resilience is usually thought of in relation to the operator’s ability to bounce back from severe weather events and unexpected grid outages. Today, this operational pillar remains as important as it ever was. In August 2020, Tropical Storm Isaias battered the East Coast and caused more than two million power outages. That same month, Hurricane Laura, a Category 4 storm, pounded the Gulf Coast for hours, leaving hundreds of thousands of people without power in Louisiana and Texas. Meanwhile, on the West Coast, California faced new, unexpected blackouts due to an intense heatwave, an overloaded grid and offline generation. Not only are these examples indicative of the pressures to come; they highlight the fact that operational resilience is a moving target, making it difficult to

Grid operators and professional athletes — on the surface, these two groups could not seem more different. But both can (and should) use a common strategy to boost their performance.In addition to regular skills-specific training, professional athletes embrace the importance of flexibility as a key driver of future success. Building flexibility by stretching after each workout improves range of motion, increases strength and resiliency, and prevents future injury — all delivering the means to compete more effectively under increasingly dynamic conditions.Read more.