Now that we've talked about six basic problem types,
it's time to start solving them.
To do that, data analysts
start by asking the right questions.
In this video, we're going to learn how to ask
effective questions that lead to
key insights you can use to solve all kinds of problems.
As a data analyst,
I ask questions constantly.
It's a huge part of the job.
If someone requests that I work on a project,
I ask questions to make sure we're on
the same page about the plan and the goals.
When I do get a result, I question it.
Is the data showing me something superficially?
Is there a conflict somewhere that needs to be resolved?
The more questions you ask,
the more you'll learn about your data and the more
powerful your insights will be at the end of the day.
Some questions are more effective than others.
Let's say you're having lunch with
a friend and they say,
"These are the best sandwiches ever, aren't they?"
Well, that question doesn't really give you
the opportunity to share your own opinion,
especially if you happen to disagree and
didn't enjoy the sandwich very much.
This is called a leading question
because it's leading you to answer in a certain way.
Or maybe you're working on a project and
you decide to interview a family member.
Say you ask your uncle,
"Did you enjoy growing up in Malaysia?"
He may reply yes,
but you haven't learned much about his experiences there.
Your question was closed ended.
That means it can be answered with a yes or no.
These kinds of questions
rarely lead to valuable insights.
What if someone asks you,
"Do you prefer chocolate or vanilla?"
What are they specifically talking about?
Ice cream, pudding, coffee flavoring or something else?
What if you like chocolate ice cream,
but vanilla in your coffee?
What if you don't like either flavor?
That's the problem with this question.
It's too vague and lacks context.
Knowing the difference between
effective and ineffective questions is
essential for your future career as a data analyst.
After all, the data analyst's process
starts with the ask phase.
So it's important that we ask the right questions.
Effective questions follow the SMART methodology.
That means they're specific, measurable,
action-oriented, relevant, and time-bound.
Let's break that down.
Specific questions are simple, significant,
and focused on a single topic
or a few closely related ideas.
This helps us collect information
that's relevant to what we're investigating.
If a question is too general,
try to narrow it down by focusing on just one element.
For example, instead of
asking a closed ended question like,
are kids getting enough physical activities these days?
Ask, what percentage of kids achieve
the recommended 60 minutes of
physical activity at least five days a week?
That question is much more specific
and can give you more useful information.
Let's talk about measurable questions.
Measurable questions can be quantified and assessed.
An example of an unmeasurable question would be,
why did our recent video go viral?
Instead, you could ask,
how many times was our video shared on
social channels the first week it was posted?
That question is measurable because it lets us
count the shares and arrive at a concrete number.
Now we've come to action oriented questions.
Action oriented questions encourage change.
You might remember that problem-solving is about seeing
the current state and figuring out how to
transform it into the ideal future state.
Well, action oriented questions help you get there.
Rather than asking,
"How can we get customers to
recycle our product packaging",
you could ask, "What design features
will make our packaging easier to recycle?"
This brings you answers you can act on.
All right. Let's move on to relevant questions.
Relevant questions matter, are important,
and have significance to
the problem you're trying to solve.
Let's say you're working on a problem related to
a threatened species of frog and you asked,
"Why does it matter the Pine Barrens tree frog
started disappearing?"
This is an irrelevant question
because the answer won't help
us find a way to prevent these frogs from going extinct.
A more relevant question would be,
what environmental factors changed in Durham,
North Carolina between 1983 and 2004 that
could cause Pine Barrens tree frogs
to disappear from the Sandhills region?
This question would give us answers we
can use to help solve our problem.
That's also a great example for
our final point, time-bound questions.
Time-bound question specify the time to be studied.
The time period we want to study is 1983-2004.
This limits the range of possibilities and enables
the data analyst to focus on relevant data.
Now that you have a general understanding
of smart questions,
there is something else that's very important to keep in
mind when crafting questions, fairness.
We've touched on fairness before,
but as a quick reminder,
fairness means ensuring that your questions
don't create or reinforce bias.
To talk about this,
let's go back to ours sandwich example.
There we had an unfair question because it
was phrased to lead you toward a certain answer.
This made it difficult to answer honestly
if you disagreed about the sandwich quality.
Another common example of
an unfair question is one that makes assumptions.
For instance, let's say
a satisfaction survey is given to
people who visit a science museum.
If the survey asks,
what do you love most about our exhibits?
This assumes that the customer loves the exhibits,
which may or may not be true.
Fairness also means crafting
questions that make sense to everyone.
It's important for questions to be clear and have
a straightforward wording that
anyone can easily understand.
Unfair questions also can make
your job as a data analyst more difficult.
They lead to unreliable feedback and missed
opportunities to gain some truly valuable insights.
You've learned a lot about
how to craft effective questions,
like how to use the SMART framework
while creating your questions,
and how to ensure that
your questions are fair and objective.
Moving forward, you'll explore different types of
data and learn how each is
used to guide business decisions.
You'll also learn more about visualizations and
how metrics or measures can help create success.
It's going to be great.