Managing Data Science projects

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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