The DataMiner Teams bot comes with a set of out-of-the-box commands that you can use to interact with your DataMiner System(s). However, these commands only scratch the surface of what is possible. By creating custom commands tailored to your exact needs, you can get maximum value from this functionality.
Do you have a use case in mind for the DataMiner chatbot, but unsure how to create the custom command? Let us know in the answers to this question what you would like to achieve and we'll implement the 5 highest voted commands for you!
While there are general use cases, in my opinion it can be very individual and depending on the business and target users. Of course our custom commands allow almost anything to be implemented quite easily and efficiently. I would therefore say that it starts by thinking about empowering people in your organization. And as a DataMiner DevOps Engineer, I would be looking at empowering the people in my organization that typically would never access DataMiner through the classic UIs.
For example. I run a VSAT operation, and I want to empower my sales team with up to date data on some of the links provided to our customers (utilization, uptime, traffic volumes, etc. or other KPIs like billing status, credits, etc.). This way, my sales team can easily pull up to date information, easily and conveniently, in a very efficient manner without needing any training.
Or I could be looking at field engineers that don't have access to the core management platform, rather than calling a NOC to check on something, this can be translated into a Teams interaction. In fact, any e-mail or phone conversations going on today between teams are good candidate use cases to be evaluated. After all this is Digital Transformation, and it is about looking for opportunities to digitize processes as much as possible, increasing efficiency, hardening the security posture (Teams allows me to make data accessible wherever it comes from, easily and efficiently, but also in a secure standard manner).
But it depends on the environment (MediaOps, SatOps, CableOps, IoTOps, etc.), and the way the operation and organization is structured. Give me the upcoming bookings for the next hour, give me the RX power on the other end of the optical line, give me the number of bookings this month for this contract, etc.
Look at empowering all the teams, across the organization
Look at e-mail & phone calls asking for information
This place may not be suitable to post this comment, but better than forgetting to comment.
It was like I was dreaming that the Dataminer responded to me, just like ChatGPT, when I saw the ChatOps demo for the first time.
But the reality was that the ChatOps integration was simply triggering a specific script to create a specific response.
Because we are living in Generative AI age, I believe our ultimate goal of the ChatOps application is,
to make it for more generic purpose AI engine integration, rather than a script per purpose.
ChatOps team developper doesn’t have to create scripts for purposes, but instead Generative AI engine generates responses by itself.
I’m not talking about AI prediction, but generating responses based on the already gethered information.
I know it is not immediately possible, and I don’t have enough knowledge about how much difficult to achieve it.
It might be very CPU or GPU intensive, and don’t know what kind of technologies are needed, Natulal Language processing, AIOps, LLM, etc.
But it should be worth for AI team to consider.