- Introduction
- Cognite Data Fusion Pros
- Cognite Data Fusion Cons
- How Order Group Uses Cognite Data Fusion
- How Cognite Data Fusion Can Be Augmented to Offer More
- Conclusion
Special thanks to Patryk Kot, who helped with writing this article.
Introduction
Cognite Data Fusion is a powerful platform for managing and contextualizing industrial data. We have been using Cognite Data Fusion (CDF) for some time now, and we can honestly say that the system is a great one. While the service does have some drawbacks, we would say only one is a major one, the rest can be remedied with proper extensions.
Cognite Data Fusion Pros
Easy to Use
Ease of use is always appreciated, and it’s no different here.
Oftentimes, developers of professional tools and systems deprioritize the user experience, and only focus on the offered functionality. That’s when function goes over form, when they both are supposed to be on par with each other.
At Order Group, we put emphasis on a positive user experience, and we like to polish our interfaces. Iza Mrozowska, our Head of Design, is there to make sure everything looks up to par.
On the other hand, using Cognite Data Fusion is quite easy, and technical documentation is there to help you dive into the world of CDF.
Extensive Documentation and Training
Having extensive technical documentation surely helps whenever you’re either starting out or whenever you have an issue. CDF’s documentation gives you information on:
- How to Search for Data
- How to Analyze Data
- CDF’s Canvas
- CDF’s Charts
- Integrations with Grafana, Excel, and Power BI
That’s not it. There is also a whole section dedicated to data engineering, as well as CDF Admin. The depth of articles within ensures users get answers to all the most common questions.
Furthermore, there is also a link to the documentation on Cognite’s Python Industrial Data Science Library.
Flexible Data Extractor Deployment
Data extractors push data in the original format to Cognite Data Fusion. It’s the second step in the pipeline, after data sources, and before the staging area.
There are several pre-built ones that are super easy to deploy. They are often available as a Docker container or even as a Windows .exe file. Pre-built extractors include:
- Cognite DB extractor
- Cognite PI extractor
- Cognite PI AF extractor
- Cognite SAP Extractor
- and more
Publishing docker containers makes deploying an extractor effortless. You may then simply deploy it to Azure, AWS, or Google Cloud in a matter of minutes. It’s also a breeze to deploy it on your infrastructure, should the need arise.
If prebuilt extractors don’t cut it, you may easily build custom ones. Cognite maintains two SDKs: one for Python, and another for .NET. For more information, go here.
More Pros
- Strong Technical Support
- Robust API
Cognite Data Fusion Cons
Advanced Technical Support Is Required for the Initial Setup
There are relatively few cons, though one of the biggest issues with CDF is the time it takes to get started. Unfortunately, as the case of one of our partners showed, it may even take you 3 months to do so.
It’s not the worst, since you may purchase additional support, although that’s not the best consolation. You’re paying for the product while it’s still getting set up, and on top of that, you (must) pay for the advanced support. At least when you do get it up and running, the system pays for itself multiple times over.
How Order Group Uses Cognite Data Fusion
We use Cognite Data Fusion to collect, process, and contextualize data from Kyoto’s battery storage systems. This data includes:
- Temperature
- Pressure
- State of charge
- Power output
We use this data to monitor the performance of our battery storage systems and to identify potential problems. Thanks to the data contextualization (on top of analysis or any other operations), we can not only extract insights, but we can also display them on top of specific parts.
As they say, a picture is worth a thousand words.
How Cognite Data Fusion Can Be Augmented to Offer More
While Cognite Data Fusion excels at data collection and contextualization, Order Group, a leading software development company, sees opportunities to further empower users, particularly those in operational and management roles.
Here's how Cognite Data Fusion can be augmented to unlock its full potential. It’s worth noting that while for some bare functionality of CDF will be enough, for others it most definitely won’t.
Enhanced Operator Tools for Real-Time Decision Making:
Operators require clear, customizable dashboards that present critical data points in a user-friendly format. Real-time anomaly detection and actionable alerts can empower operators to identify and address potential issues before they escalate.
Improved Search Functionality for Faster Information Retrieval:
The current search functionality can be improved to understand the context of user queries. This means recognizing not only keywords but also the relationships between different data points. Imagine searching for "dropping battery pressure" and getting results that include related data like temperature readings and recent maintenance logs.
To do that, one may integrate Natural Language Processing (NLP). Allowing users to search using natural language can significantly improve search efficiency. Operators should be able to ask questions like "What was the average battery temperature last week?" and receive relevant results quickly.
Reducing cognitive load will let your employees work more efficiently than before. They have a finite amount of energy per day, which they have to use as wisely as possible.
Creating an All-Encompassing Experience for BMS
Finally, our ultimate goal is to develop a comprehensive system that handles all aspects of battery management, from initial order placement to smooth delivery, installation, and battery usage monitoring.
We envision this process seamlessly integrating into a larger journey, providing customers with a hassle-free experience from start to finish. Our goal is to streamline the entire battery management process, eliminating any potential stumbling blocks or complications along the way and ensuring that our customers can rely on a dependable and efficient system that meets their requirements.
Conclusion
Cognite Data Fusion is a powerful platform for managing and contextualizing industrial data. Order Group has been using Cognite Data Fusion for quite some time now to manage the performance of Kyoto’s battery storage systems.
We believe that Cognite Data Fusion has the potential to be an even more powerful tool if it is augmented to offer more features and functionality. If you would like to talk further about adding more functionality, do not hesitate to contact us now.
