One of the problems managing data science or even development projects is that due to its usual complexity, they can be harder to manage.
If you're working with manual tasks, its easy to say "We need you to do 50 parts", or "We need you to process 100 requests using this process.".
However, when working with people who are responsible for more complex and dynamic tasks, one cannot communicate in a few simple words whether they are doing the right thing, or how well they are doing.
Before being able to measure how well the work is being done, many times one has to sit down with them to help define what they should be doing and why and this can be very time-consuming.
It needs to be clearly understood what is expected of people in charge of intelligent tasks and why.
Data scientists, from all hierarchies, must be focused on the results and goals of the entire organization to have any results at all. That means that time needs to be taken to direct their vision and focus from their technical work to results.
#datascience #management #datastrategy #bigdata
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Oh yes, a familiar story in that if you don't know what data you want at the end of a project or process, you will often struggle and get lost in a giant soup of numbers and data!
Knowing what you need for the final result will make it a lot easier to work back to the beginning 🙂
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True. I've developed a methodology to deal with that. When me or someone else from EAI (my company) start developing a data strategy with a client, we list all the different types of data, data sources (sometimes the same data has the same sources), and relevance for the different use cases in the organization. We do this even with data that has not yet been collected (or purchased).
This is part of our data strategy program, this way companies that work with us know from the start what data they need, what they should be collecting, what are the dependencies, and their priorities! :)
Thanks for the comment, it means a lot!
I can tell that you and I will be talking a lot on here! I'm excited to have another fan of Data Viz on here with us!