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Anatomy of a contextualization engine for AI use case scaling in industry If there is one thing we at Cognite get a lot of questions on, it’s contextualization. Not so much what is contextualization (luckily we are getting past that phase now), 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 also: The data liberation paradox: drowning in data, starving for context

John Markus Lervik in Cognite has been contacted by over 100 VC investors, but he had long warmed up one of the very, very hottest. This article was originally published in Norwegian in Shifter. Read the article here.  It is early morning Silicon Valley, and late afternoon at Fornebu. One of the real seniors in the investor community "over there" has got up at six o'clock to attend a video conference he does not want to miss.TCV top Jake Reynolds has previously led investments in Splunk, We

Cognite announced it has raised $150 million in an equity funding round led by TCV at a $1.6 billion post-money valuation. Cognite says this investment marks one of the largest funding rounds for a SaaS company in Europe and will be used to expand its platform and support hiring efforts. Read the full article here. 

If there is one technology trend aside “AI” that is set to define the 2020s, it is “Ops”. From DevOps to DataOps to MLOps, focus is rightfully put on end-to-end operationalization rather than initial code, data or algorithm development alone. Why does your organization need DataOps?According to Gartner, “DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization”. Forrester defines DataOps as “the ability to enable solutions, develop data products, and activate data for business value across all technology tiers from infrastructure to experi

When John Markus Lervik co-founded the industrial artificial intelligence company Cognite with Norwegian conglomerate Aker ASA in 2016, the companies set out to solve what they believed was an industrial data problem. Now the Cognite chief executive believes AI is thesolution to the larger issues of industrial transformation and a profitable energy transition. As Lervik told an online audience at the CeraWeek by IHS Markit conference in March: “Digital can make sustainability profitable by enabling cost cuts, improving efficiency and reducing environmental footprint — all at the same time — and in most cases, it comes hand-in-hand.” The energy industry is on the precipice of great change. The digital transformation that was under way is experiencing a Covid-19-induced acceleration and a growing push to transition towards sustainable fuel sources like wind, solar, and hydrogen. As a technology entrepreneur, Lervik naturally sees

Cognite was featured in TechCrunch as Oslo VC's discuss 2021 trends. The article talks about the incredible opportunity in the Nordic region, with Cognite cited as a strong company to watch. Excerpt from the article:Which industries in your city and region seem well-positioned to thrive, or not, long term? What are companies you are excited about (your portfolio or not), and which founders?We see a good variety of exciting companies from Oslo and Norway. Kahoot, Spacemaker, Cognite, and Pexip have been leading the way lately, with new ones like Memory, Tibber, PortalOne, reMarkable, and many others following right behind. We also believe that Norway’s strong roots with industrial companies now seem to move into tech, for example with a highly skilled workforce moving over from the oil and gas industry, as well as really exciting companies coming out of this area — Cognite being a

Shifter has spoken with the two managers who form the backbone of Aker's new software department. They say that acquiring startups is high up on the agenda.  Kjell Inge Røkke recently announced a large-scale investment in software. He outlined a future with dozens of software companies under a separate Aker division called Axis. He told E24 that he has worked on this plan for five years. That is about as long as John Markus Lervik has been in the Aker Group and developed ​Cognite. The company, which collects and analyzes industry data, ​was recently valued at NOK 5 billion​ by Silicon Valley investor Accel. Lervik has also been instrumental in the establishment of Axis.“A lot has been created along the way, but this has existed as a conceptual idea since before the start in 2016,” Lervik said about the new initiative.“The idea behind Axis is to see how people in Norway can build

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 compa

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.

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

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.

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.