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We use Gartner’s definition: "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." At Cognite, we specialize in DataOps for industrial data, creating and leading a new category of Software called Industrial Data Operations.

Industrial DataOps provides simple access to complex IT, OT, and ET data though a real-time industrial digital twin that any domain expert can leverage to build, deploy, and scale 100s of digital solutions.

Learn more here.

Not quite. The focus for DevOps  is software and application development. Automation efforts center on the development cycle, software delivery processes, and waste elimination. The alignment of developers, operations, and the business is a major aim of DevOps.

The focus for DataOps, on the other hand, is the delivery of business-ready, trusted, actionable, high-quality data, available to all data consumers or domain experts throughout the organization. One goal is automation efforts, centered on data governance and integration. Plus, DataOps requires alignment between IT system support, operations, and the business.

  • Improved data accessibility: DataOps technology uses AI to enable rapid ingestion and contextualization of large amounts of data. And by improving data accessibility, DataOps brings a paradigm shift in how the organization accesses business-critical information, improving decision-making quality, reducing risk, and lowering the barriers to (and skills for) data innovation.
  • Efficient data management and rapid application development at scale: Maximizes the productive time of data workers with automated data provisioning, management tools, and analytic workspaces to work with and use data safely and independently within pecified governance boundaries. The approach can be augmented with AI-based automation for various aspects of data management—including metadata management, unstructured data management, and data integration—enabling data workers to spend more time on use case development. As a result, organizations accelerate the time to design and develop POCs, offering tools to operationalize and scale them.
  • Enterprise data governance as a by-product: DataOps enables companies to set and enforce the basic principles for managing data. If the basic principles for managing data are implemented successfully, organizations receive consistency and ROI in technology, processes, and organizational structures, with better operations data quality, integration and accessibility, and stewardship. Plus, an Industrial dataOps platform helps enhances data security, privacy, and compliance with tracking, auditing, masking, and sanitation tools.

Cognite Data Fusion® is the market-leading Industrial DataOps SaaS solution. With its comprehensive suite of Industrial generative AI capabilities, Cognite AI, Cognite Data Fusion® makes it easy for decision-makers to access and understand complex industrial data. Cognite Data Fusion® is a user-friendly, secure, and scalable platform that enables industrial data and domain users to collaborate quickly and safely to develop, deploy, and scale industrial generative AI solutions that deliver both profitability and sustainability.

Learn more here.

Cognite AI is a comprehensive suite of generative AI capabilities within Cognite’s core Industrial DataOps platform, Cognite Data Fusion®. It uniquely augments the ability of generic-purpose LLMs to retrieve data from private sources to generate deterministic responses based on a customer’s own private industrial data, within the customer’s secure and protected SaaS tenant. Cognite AI’s comprehensive capabilities improve operations by rapidly accelerating cloud adoption and increasing the efficiency of industrial workflows by 10x.

Learn more here.

A single, collaborative workspace that makes all OT, IT, engineering, and robotics data (time series, events, P&IDs, documents, work orders, asset hierarchies, images, simulation, 3D, and more) available through a native AI Copilot functionality to answer operational questions, compile and develop no-code applications, and analyze complex scenarios up to 90% faster than before.

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We’ve invested significantly in being able to integrate and manage the complete portfolio of industrial data as well as automating contextualization. Because of this, Cognite Data Fusion® can connect data from multiple sources using algorithms, not domain expert man hours, to enable a real-time industrial digital twin of your entire operation. Additionally, Cognite Data Fusion® is a completely open (but governed) platform so stakeholders can use whichever data, tools, models, or partners are best for the job at hand without vendor lock-in.

Congite Data Fusion has prebuilt extractors for common industrial data sources and protocols such as OPC-UA, OSI PI and MQTT. Additionally, Cognite Data Fusion® has an SDK that is used to connect to ERPs, MES, document storage systems, and IoT platforms. Extractor pipelines can be created for all connectors to enable your teams to build reliability into all of the OT, IT, and ET data sources onboarded into Cognite Data Fusion®.

Cognite Data Fusion’s AI-powered contextualization services enable industrial digital twins by aggregating all possible data types/sets, both real-time and historical, directly or indirectly related to a particular given physical assets or assets, into a single unified location. In other words, the collected data is cleaned, contextualized, and mapped to how things would be linked in the real world.

Automating the contextualization process makes the digital twin dynamic and scalable, allowing different data consumers to view and navigate the data in a way that best meets their needs, and quickly move POCs to scale using cases template use cases for specific data models. This flexibility enables teams to solve complex use cases and improve their operational efficiency via predictive maintenance, production optimization, adjusting toindustry demands, etc.

While there can be many contributing factors, scaling is often limited by the inefficiencies of working with industrial data. If data is difficult to access, understand, or trust, scaling POCs often require a significant amount of people to overcome. These data challenges are magnified as more solutions are built, which is why Cognite believes a trusted, contextualized data foundation is needed to power your operational use cases. One that enables you to create use-case-specific data models and reuse those models across many use cases.

Learn more here.

The purpose of data operations is to manage the flow of data from source to value, with the goal of speeding up the process of deriving value from data.  We target 3 workflows:

  • Data owners: Cognite Data Fusion® reduce the time your data engineers spend managing infrastructure and enable them to deliver trusted, contextualized operational data to those that can create value (advanced contextualization, extractor pipelines, governance)
  • Solution builders: these are the SMEs that want to develop new insights or perform on-the-fly analysis (Grafana and PowerBI integrations, Charts)  and professional developers or data scientists that need access to clean data to quickly build, operationalize, and scale use cases (Cognite functions, templates)
  • Solution consumers:  these are the functional groups that are going to consume the tailored insights.  Cognite Solutions portal is the entry point for these users to access solutions through a  persona-based window in whichever application the insights are being provided (Cognite apps, Grafana, PowerBI).

The hardest part about building this yourself with publicly available cloud services is the tailoring needed to capture industrial domain knowledge into these services.  For example, Cognite Data Fusion’s highly optimized time-series database can support millions of data points without any degradation in performance.  Cognite Data Fusion® also use a multi-modal data store approach so not only can we store all data types (time series, events, images, video, 3D, etc)  across the industrial data spectrum, but we can also quickly create relationships across all these data types with our advanced contextualization services.

What this means is that while a DIY platform approach can often take 6 months to start generating value, using Cognite Data Fusion® can reduce the data management efforts and start delivering value in 8-12 weeks.  The DIY path often require teams to grow rapidly to maintain the infrastructure and to scale solutions.  With Cognite Data Fusion®, infrastructure management is taken care of so your teams can manage more solutions without needing more people.

Many organization start with DIY but then start to realize that there’s significant costs and responsibilities tied to maintaining solutions over time. Working with a SaaS vendor like Cognite, not only do you get to leverage innovation from a pure-play software provider with significantly less risk.

Read more about DIY myths on our blog


Cognite has a growing, open partnership ecosystem with a wide variety of partners to help drive the full-scale digital transformation of asset-heavy industries around the world.

Cognite Data Fusion® is a completely open (but governed) platform that makes data easy to use for any data and domain expert, including any partner that wants to leverage Cognite Data Fusion® to develop, operationalize, and scale industrial AI solutions and applications.

See our partner page for a full list of our partners.


Cognite operates in asset-heavy industries where operational technology security is paramount. We continuously invest in security awareness and training to support integrated DevSecOps practices. Cognite aligns with industry technical standards and our customer's standards and regulations. Cognite Data Fusion® adheres to Service Organization Control (SOC) 2 Type II compliance, as well as a number of other industry standards. Visit our security page for more information.

Cognite Data Fusion® is cloud-native with availability today on Azure, GCP, and AWS. We offer a set of high-performance edge deployable data extractors for common source systems such as OSISoft PI and industry protocols such as OPC-UA, as well as offering pre-built integrations to partner edge services such Azure IoT Hub and Kepware.

We also partner with Siemens and Litmus to incorporate their edge solutions on top of Cognite Data Fusion®.

Our customers are trying to reimagine the future of operations and maintenance with autonomous or semi-autonomous workflows- this is a big data challenge under the hood. 
While robots - an enabler towards a safer and more autonomous operations and can be an extension of a human when conducting an on-site inspection of a pump, detecting gas leak, maintain the accuracy of the digital twin, collecting the data from a potentially high-risk pump location and more….to do so robots must be context aware. Robot needs to be part of of integrated data system and be a part of the data workflow, otherwise, they just become another roadblock in already complex operations. Once the robot gather data - it needs to be contextualized and make data available and understandable to data consumers to make better decisions, improve safety and efficiency of the asset.

Cognite’s role with any robotics deployment is to be the data layer where new data (images, sensors, lidar) can seamlessly be stored and used for analysis or other post-processing and development of insights. Without the data layer (and clear goal for what your robots solving) - robots are just another data silo organizations have to deal with. We’ve helped deploy robots across industry in Oil and gas (AkerBP/Hess), Manufacturing (Celanese), and Power & utilities (Statnett R&D project for autonomous substation monitoring).

There is no short answer to this question, but here are the fundamental steps:

  • Evaluate: First, you need to understand the state of your organization's digital maturity, learn and follow DataOps principles and practices, and assess your current data management and operational models.
  • Align: Industrial DataOps requires stronger cohesion among data stakeholders. Data science and IT must collaborate well beyond data access and resource allocation, while business should be involved in data projects well beyond the typical demand and validation stages
  • Rebuild: Move from a conventional centralized data architecture into a domain data architecture (or data mesh). This solves many challenges associated with centralized, monolithic data lakes and data warehouses. The goal becomes domain-based data as a service, not providing rows and columns of data.

To learn more about industrial DataOps, read The Definitive Guide to Industrial DataOps.