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 industrial data to ignite your digital roadmaps.

These posts are those of you who are new to using Cognite Data Fusion and wanting to understand how we approach the challenges of working with industrial data without losing focus on delivering business impact. The topics I will be discussing are:
 

• What is Cognite Data Fusion and why did we build it? (This post)

• Data modeling grounded in business impact

• The opportunity cost of custom building your industrial data platform (DIY)

With that said, let’s get started! I always appreciate feedback and discussion, so I hope you’ll share your thoughts at the bottom of this article. 

What is Cognite Data Fusion and why did we build it?

Cognite Data Fusion is an Industrial Data Operations (DataOps) platform specialized in ingesting, curating, and making industrial data useful. Essentially, the purpose of industrial data operations is:

• Making siloed IT, operations (OT), engineering (ET), and visual data across all production facilities available to onsite and remote subject matter experts (SMEs), data scientists, and application developers. 

• Connecting individual data together (contextualization), so data becomes easy to discover in its business context, hence catalyzing its application for business analytics and optimization.

• Enabling rapid development and scaling of data-driven solutions - across all production facilities

Cognite Data Fusion at a glance

To quote Forrester: "Data has no value unless the business trusts and uses it." This is important, as merely accessing data does not provide value, but success is linked with your ability to understand and operationalize the data - at scale. Cognite Data Fusion rapidly turns data into a trusted product to be efficiently used to build solutions. Below we have highlighted what it means to create a data product and how your data product can be used to generate business impact.

Cognite Data Fusion creates consumable data products and provides tools to efficiently utilize the data at scale.

Cognite Data Fusion is built specifically for industrial companies, and only serves the industrial market. Our targeted approach means Cognite Data Fusion provides both horizontal DataOps capabilities (data lineage overview, data quality monitoring, ingestion pipeline orchestration, access control, development tools, etc.) and industry-specific vertical capabilities (native industrial data type support, live data access, data contextualization, and much more). The industry specific vertical tools is how we build domain expertise into Cognite Data Fusion.

Horizontal and vertical DataOps capabilities of Cognite Data Fusion

Cognite Data Fusion provides a rich set of composable cloud services to deliver Industrial DataOps capabilities. While the illustration below may appear monolithic, it is essential to note that these composable services fully integrate with other services (e.g. Azure or GCP). For example, edge data extraction can be supported by Cognite Data Fusion or from a 3rd party IIoT platform.

Example of the composable Data Operation services of Cognite Data Fusion

The composability and 100% openness of Cognite Data Fusion provides agility for data owners to adapt and meet the ever-changing business needs. This is crucial, as the only certainty is increasing complexity of data velocity, variety, and variability where openness and flexibility are requirements for a more agile future. 

Cognite Data Fusion secures a flexible data architecture

Cognite Data Fusion was built to address the complexity and ever changing reality of industrial companies (we grew out of this ourselves) and is committed towards a modern, open architecture that enables our customers to: 

• Select preferred data ingestion patterns - Cognite offers pre-built extractors to common industrial sources (OSI PI) and protocols (OPC-UA) that push data directly into Cognite Data Fusion. In addition, it supports 3rd party ETL tools (e.g. Azure Data Factory for batch data and Informatica for streaming data) and the ability to route data via message broker (e.g., Azure IOT Hub,  Event Hub, or Kafka).

• Model and scale desired data ontology - and iterate over time. Cognite Data Fusion supports data models for your source systems, your industrial domain (i.e., master data model), and all data-driven solutions (i.e., an ecosystem of twins). Because Cognite Data Fusion can model data at both a foundational and solution-specific model, it can serve as the data backbone of all your digital twins (This will be covered in the next part of this series).

• Utilize preferred tools - internal or 3rd party - Cognite Data Fusion provides powerful out-of-the-box tools to monitor (AIR), analyze, and visualize data (Charts) while also enabling you to use the tools and applications your teams already know (e.g. Grafana, PowerBI, Plotly Dash, Power Apps, and Logic Apps). Also, Cognite Data Fusion provides built-in tools that support and automate the data curation process (e.g., entity matching and dynamic APIs).

• Foster cross-discipline collaboration. Data Operations (i.e. the continuous process of ingesting, curating, and utilizing data) are not done in a silo. Successful companies acknowledge that cross-discipline collaboration is the key (e.g., between domain experts and data scientists, between data engineers and solution architects, and between local and global IT). Thus, Cognite Data Fusion strives to offer the set of capabilities needed in the end-to-end process and make the workflows as convenient as possible to perform. Also, to provide tools and interfaces that foster collaboration. This is different from most functionality in Azure which targets IT knowledge personas foremost. 

The goal of Industrial DataOps is to deliver business impact by providing simple access to complex industrial data. Below are a few examples of use cases we have delivered across asset utilization, production, sustainability, and maintenance.  The reusability of Cognite’s domain data model (to be covered next) makes it easy for our customers to solve many of these use cases using Cognite Data Fusion as their industrial data foundation.

Key solution areas Cognite Data Fusion addresses

If you found this helpful, or if some questions are left unanswered, please comment below. The goal is to create content valuable for you so all feedback is valuable.  I also owe a big thanks to the co-author of this post, @Morten, who will be sharing part 2 of our series. Next week: Data modeling grounded in business impact.