“In our 2012 study of data analysts, we characterized the process of data science as five high-level tasks: discovery, wrangling, profiling, modeling, and reporting. Most analytic and visualization tools focus on the last two phases of this workflow. Unfortunately, most of a data scientist’s time is spent on the first three stages.”
Sean Kandel on HBR’s blog in response to another HBR Blog about Data Scientists having the sexiest job of the 21st Century.
In my opinion, every OSS implementation should be accompanied by data scientists before and after handover. You need tripods that are able to provide links between objects and then derive insights or valuable connections from the enormous stores of data that your OSS collects.
As suggested in the quote above, you also need assistants to help with the discovery, wrangling and profiling stages to get the data to a point that the high-value modelling and reporting phases can commence.
How many data scientists do you have on your OSS team?
Have you ever had an OSS or BSS project that’s impacted by poor data quality? The problem of data integrity gets raised on almost every
“One of my favorite things about designing software is designing the defaults. The defaults define the experience for everyone out of the box. And, therefore,
Have you ever been tasked with mapping out the processes for the telco you work for? Either as a way of documenting current state, or
In the last week alone, I’ve had two potent reminders that the world of OSS is simply (pun intended) too complex. This manifests in many
Here at Passionate About OSS, we are exactly that – Passionate About OSS (Operational Support Systems). We’re also passionate about BSS, NMS, or any other names you call the tools that help to operationalise your network.
