Impact 2024: The Industrial Data and AI Conference for and by Users | Nominate Speakers Now for a Ch...
Digitalization PoCs are commonplace. Real return on investment (ROI) isn’t.So how do investments in digital transformation efforts translate into real value for your business (or company)?We, at Cognite, commissioned a study from Forrester Consulting to examine the potential ROI and business benefits asset-heavy industrial organizations can expect from deploying Cognite Data Fusion. Forrester interviewed six representative customers across our customer base in Oil and Gas, Manufacturing, and Power & Cleantech with experience using Cognite Data Fusion, and found $21.6 million in added net present value at a 400% ROI. Key results of the ROI study include:* $9 million gained through the optimization of heavy machinery and industrial processes* $5.1 million saved through optimized energy use and reduced operational costs* $4.8 million added value through reduced shutdown time* $4.3 million cost reduction by optimizing planned maintenance Do you want to know what stands behind these nu
Don’t say. Show.If there ever was one crisp line to capture the essence of what’s in store in 2023 for both buyers and sellers of Industry 4.0 solutions, it could well be this.Staying true to our New Year Predictions format, two disclaimers hold: For those looking for more general technology predictions, there is no shortage of well-researched examples published around this time of the year by the likes of Gartner, Forrester, and Verdantix — we encourage you to seek these out directly; and We are equally not venturing into the macro market factors, as these alike are abundantly covered by financial and even popular media. Instead, let us offer you 4 predictions that are focused on digital transformation of heavy asset industries. As always, let us know what you think by dropping us a line below. New buzzwords die before taking offThis is sobering to see! The last thing needed is one more nonsense buzzword to fill conference stages and drive keyword bidding in vendor SEM programs.We’
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
“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
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
Only a fool believes in different outcomes by doing the same as before.Executive summary: 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 (simply referred to as 'platforms' from hereon) 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
While many manufacturers have successfully fostered lighthouse sites, very few have been able to replicate this success across their other production sites.The purpose of these lighthouse sites are to act as the guiding model for other production sites, providing a wave of innovation to address use cases that will increase productivity, improve quality, reduce energy and water consumption, and much more. The problem is that lighthouse facilities on average only account for 10-15% of production volume, and manufacturers are struggling to replicate this success to the significantly outstanding 85-90% of their production.Read in our latest blog why aren’t lighthouses scaling. And how to ensure that your lighthouse success can be carried forward to other production sites. Did this article cause some reflections? We’d love to hear from you and your experiences.
Herein lies the challenge, while many manufacturers have successfully fostered lighthouse sites, very few have been able to replicate this success across their other production sites.To define a lighthouse site, these are the ones that are first to install the newest technologies, often have teams with unique technological expertise, and are likely the most productive and agile of all your production sites. This lighthouse concept is best recognized by the World Economic Forum, which started the Global Lighthouse Network in 2018 and currently recognizes 90 manufacturing sites worldwide for “applying Fourth Industrial Revolution technologies to increase efficiency and productivity, along with environmental stewardship.” The purpose of these lighthouse sites are to act as the guiding model for other production sites, providing a wave of innovation to address use cases that will increase productivity, improve quality, reduce energy and water consumption, and much more. The problem is that
A technical introduction to parallel write-optimized and read-optimized Cognite Data Fusion TSDB. Read the full article here:
The digital twin is the foundation for industrial digitalization efforts, delivering real-time insights, accurate forecasting, and intelligent decision-making. In the almost two decades since the term was invented, industry - and the world - have changed dramatically. So what’s next for digital twin technology? Johan Krebber, IT Strategist at Cognite, summarized his perspective on the evolution of the digital twin concept during a panel at Ignite Talks, 2021’s big industrial digitalization conference.Read here an extended expert interview between Johan and Petteri Vainikka, our Vice President of Product Marketing, on the future of digital twins: Did this interview cause some reflections? We’d love to hear from you and your experiences.
Design Performance Architecture Backups Access control Roadmap Design Time series databases typically come in two flavors: write-optimized and read-optimized. Cognite Data Fusion Time Series Database (CDF TSDB) strikes a balance between the two, ensuring that tens of millions of data points per second can be ingested and read in response to queries simultaneously, reliably, and with ultra-low latency both for input/indexing and querying.Write-optimized time series databases are useful as historians, constantly ingesting data from industrial equipment. But they are of limited use for large-scale analytics and are a poor choice to power interactive applications, as the stress from unevenly distributed user traffic may interfere with the reliable operation of time series ingestion. Examples include most industrial historians, as well as InfluxDB.Read-optimized time series databases on the other hand are an excellent choice for analytical query loads, but struggle with streaming ingestion.
The digital twin is the foundation for industrial digitalization efforts, delivering real-time insights, accurate forecasting, and intelligent decision-making. In the almost two decades since the term was invented, industry - and the world - have changed dramatically. So what’s next for digital twin technology? Johan Krebber, IT Strategist at Cognite, summarized his perspective on the evolution of the digital twin concept during a panel at Ignite Talks, 2021’s big industrial digitalization conference. Read below an extended expert interview between Johan and Petteri Vainikka, our Vice President of Product Marketing, on the future of digital twins.Hello, Johan! Thank you, for taking part in our panel at Ignite Talks and especially for taking the time to do a deep-dive interview to expand on your contributions to the panel! Let’s start with a lightning round question. All I need is a simple yes or no. You’ll get to elaborate in a second. Should we sunset talking about digital twins and
Did you know that only one in four industrial organizations extract value from their data? The lack of tools and processes to connect, contextualize, and govern the data often stand in the way of industrial digitalization. Industrial DataOps is a powerful new way of deploying data and technology to transform an industrial organization. It makes sense of, manages, and extracts value from complex industrial data. And it is is already becoming a driving force in industrial transformations, helping accelerate digital maturity, enabling data teams to deliver more digital products, and realizing more operational value at scale. In a 2020 survey of global companies, McKinsey found organizations that embedded DataOps could see the volume of new features increase by 50 percent because data automation enables quicker development iterations. At Cognite, we’ve released the first-of-its-kind Industrial DataOps book - a guide packed with insights, industry expertise and practical advice on how you
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?).Read also: DataOps: A transformative new approach to data ROITo avoid boiling the ocean, we will focus on what is perhaps the most fundamental question all fellow Chief Data Officers and other digitalisation executives need to consider as their Northstar — and in doing so, we will find ourselves on
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. Dive in!
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 How does Cognite Data Fusion contextualization engine work?First, it is paramount to set some foundations:There is no such thing as the ideal universal data model. Having some pre-defined reference data model (can be based on industry-standard where applicable, or only usi
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