That's effectively how we organise projects in my corporate insurance setting now. We're pretty much all-in on an agile type approach and doing things in chunks of a few weeks or a month or so at a time even if we have the bigger picture of what we think we will do (across the quarter, year, etc). We do that through a combination of milestones that are revisable and then having the shorter more detailed 2 week forecast.
I flat-out struggle to not get stuck into things with no relevance to the project at hand.
We have the same basic split of comms (teams), to-do lists and tasks (planner / asana), and then use microsoft products for everything else (sharepoint for information management / storage, excel etc).
My team itself (Data Science) do have some difficulties doing everything cleanly in those two week chunks because our work can be really variable in our firm. We might have some questions we are investigating and it might be the case the data / information my insurance company has is flat-out unreliable, we might not be able to directly answer their question, or we see evidence that it might be the wrong question to ask in the first place.We are often tasked with exploring the data in context of a general problem, and our project managers want longer term planning, but we don't even know what we'll find yet.
Hey Alex! Thanks for your reply. It’s really reassuring that a big company is taking a similar approach and that I’m on the right track.
I was thinking of doing a monthly post of ‘what I have done - what are my aims for the next month’ so people can see how much work I’ve covered (note to self, find the scrunched up to do lists in the bin).
It’s interesting that you should mention your challenges as a data scientist because I am currently crafting the pre-screening survey for consultations with sex workers and I am aware that if you don’t have the right data questions, it can really muck up the analysis later on. For me it’ll be about stratifying the population to ensure I am consulting with a representative sample - and I really have to make sure the parameters are spot on i.e age, sex work type, gender etc.
That's effectively how we organise projects in my corporate insurance setting now. We're pretty much all-in on an agile type approach and doing things in chunks of a few weeks or a month or so at a time even if we have the bigger picture of what we think we will do (across the quarter, year, etc). We do that through a combination of milestones that are revisable and then having the shorter more detailed 2 week forecast.
I flat-out struggle to not get stuck into things with no relevance to the project at hand.
We have the same basic split of comms (teams), to-do lists and tasks (planner / asana), and then use microsoft products for everything else (sharepoint for information management / storage, excel etc).
My team itself (Data Science) do have some difficulties doing everything cleanly in those two week chunks because our work can be really variable in our firm. We might have some questions we are investigating and it might be the case the data / information my insurance company has is flat-out unreliable, we might not be able to directly answer their question, or we see evidence that it might be the wrong question to ask in the first place.We are often tasked with exploring the data in context of a general problem, and our project managers want longer term planning, but we don't even know what we'll find yet.
Hey Alex! Thanks for your reply. It’s really reassuring that a big company is taking a similar approach and that I’m on the right track.
I was thinking of doing a monthly post of ‘what I have done - what are my aims for the next month’ so people can see how much work I’ve covered (note to self, find the scrunched up to do lists in the bin).
It’s interesting that you should mention your challenges as a data scientist because I am currently crafting the pre-screening survey for consultations with sex workers and I am aware that if you don’t have the right data questions, it can really muck up the analysis later on. For me it’ll be about stratifying the population to ensure I am consulting with a representative sample - and I really have to make sure the parameters are spot on i.e age, sex work type, gender etc.
Anyway, thanks for sharing your experience <3