Hi everyone, it’s Nicki here from Empowered Analysts and you're watching 365 Data Use
Cases. I'm really excited to be involved with this initiative by 365 Data Science. So,
make sure you subscribe to their channel to keep up to date with this series. You can also feel
free to check out my YouTube channel - Empowered Analysts - for content on how to get started,
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be able to find a link for it in the upper right-hand corner of this video.
Now, there are so many great use cases for data, but one of my favorite over the past few years
is around employee productivity. So, imagine you work in a factory which employs hundreds
of people to pack products for customers. Those employees working hard is crucially important
for a few reasons. Firstly, making sure that customers get their product on time.
So, they need to be efficient in what they do. But also, we want to know that all those
employees are working for the whole shift. If they work hard for one hour, but not doing
much the rest of the time, our business will be affected. Customers won't get what they want by
the time we promised it, and we might stop getting orders if people no longer use us.
So that's where employee performance data comes in, we can query data stored in a transactional
database using SQL to manipulate the output for reporting. We may well store it in a
data warehouse to help speed up performance and have the output stored so that it can simply be
queried on by analysts at a later point. That way we can focus on the insight
rather than the data manipulation. So, there are a few key measures here that are really important,
firstly, being able to track which employees are due to work a shift and how long they get for
breaks. That way we can identify the total time they're available for work. So, let's call that
available hours. Secondly, we then want to know what they've been working on during that time. So,
we want some kind of logging system that monitors what tasks they're doing and how long it takes to
do those tasks. So, let's call that logged time. So, our first measure is going to be utilization.
Let me explain. Imagine Jane is an employee at the factory and she's going to be on a
shift for 10 hours. So, 10 available hours. These are the number of hours she'll work
after her breaks. Then, Jane also logs time for those whole 10 hours.
That means she's been utilized 100 percent of her shift. And that is great. Exactly what we want.
But suppose you have another employee, Samantha, who is also available for 10 hours, but only logs
eight hours, so she only 80 percent utilized, not so great. We really want to start understanding
why there's a difference in performance. The next measure we'd want to look at is
efficiency. So, this is the expected time it takes to do a task against how long it actually takes.
So, if Jane took two hours to do a task and we expect it to take two hours, she'd be
100 percent efficient. If she did that two-hour task in one hour, she would be 200 percent efficient.
But if she did that two-hour task in four hours, she would only be 50 percent efficient.
This calculation will contribute to how much product the factory will distribute, how quickly
their customers will get their products and ultimately massively impact the future products
if customers don't come back to purchase more. Tools like Power BI and Tableau can then be used
to summarize data for management to monitor. It can be presented at regional insight levels,
but also be set to allow managers to drill down to teams or individuals so they can
drive the behaviors that improve productivity in their factories.
Thanks for watching this Data Use Case video brought to you by 365 Data Science,
and make sure you subscribe to their channel if you want to see more great content.
And if you are an aspiring data professional, you can check out my YouTube channel,
Empowered Analysts, where there is a playlist on how to get a job as a data analyst,
where I explain more about the job first strategy and be sure to visit my website if
you want to create a CV or resume that showcases your experience, whatever level you are at.