So you can either have sort of training sessions or
sort of informational sessions about how the Data Science team works.
Those are both really useful ways to empower people to approach
the Data Science team and work with them.
Another way is to build documents or build tools or
build products that can be shared with other people.
Who don't necessarily have Data Science or Data Engineering training, but
would like to interact with the data.
So one common example is sort of a dashboard,
sort of a set of visualizations or set of plots.
That are built, so that somebody can click on some buttons and
interact with that visualization.
They can ask their own questions, have their own hypothesis and
interact with the data.
Even if it's just a subset of the data.
Another way is just one interactive visualization can be really compelling,
can be a way for people to sort of interact with the data,
ask one specific question.
And sort of see how their choices work.
So here the key is, there's a design element to it.
It's how are you going to design your interface so
that people can interact with the data.
They can ask their own questions.
But they're not intimidated by syntax or knowing how to actually do the stuff.
With the low-level programming languages that you or
your Data Science team might know about.
So in addition to training,
there's these sort of ways to directly connect people to the data themselves.
Ideally, you can automate this.
So a lot of report writing tools, whether they're in sort of high Python
notebooks or shiny documents or so forth, can be sort of auto-generated.
You can also setup a dashboard that does an auto-pull from your database,
that always has the latest and freshest data available to people.
If you can automate it in that way, the most common questions can be
answered by people other than the Data Science team.
Which leads them to innovate and to focus on new ideas and
new things that they can do well.
Or to shore up the infrastructure for this sort of Data Science processes that you
already have going on in your organization.
The other thing that can be really useful as a way to have people sort of interact
with the Data Science team is with Data Science idea evaluation.
So often people will have ideas about ways they think they might be able to
use data that solve a particular problem or another.
But it turns out that sometimes those are impossible.
We don't have the right data or we don't have the imaginary infrastructure,
we don't have the right scale to be able to solve that problem.
But it's still really nice if you can provide a way or a format or
a forum where people can propose ideas that they'd like to do with the data and
get critical, but positive feedback on those ideas.
You don't wanna immediately crush any idea that somebody brings up.
You'll never hear another idea again.
But it is a good idea to sort of, that helps tune expectations.
What can we actually do with the datasets that we have?
Especially, if someone else is proposing the ideas and
you are sort of letting them know.
Yes, we can do that.
No, we can't do that.
Yes, we can do that, but
it would take a whole new infrastructure on the server side.
Yes, we can do that, but
it would require us to develop a whole new machine learning infrastructure here.
That gives people an idea about what's going on with the Data Science team.
What can you actually do, what are the parameters that makes it useful for you.
And so you can do peer review of these ideas from the data team and
from yourself as another way to interact with the organization.
But in general, the idea is as often as possible drawing
other people into the Data Science process.
Often that requires a little bit of education and that education can feel like
wasted time if you're not thinking about it in the right way.
But if you think about it as an investment in the future Data Science
capabilities of your organization.
It can be a really positive, really value compounding way of doing data science.