Think about it like you know, numbers on their own are
forgettable, but stories take. Welcome to Business Analysis
Live. I'm your host, Susan Moore,
community engagement manager with the International Institute
of Business Analysis. I explore topics with industry
guests about the work of business analysis professionals
and how their work helps organizations achieve better
outcomes. And Yep, we take your questions,
too. Thanks for listening.
Let's get started. We talk a lot about business
analysis and we talk a lot about some of the techniques, some of
the skills and tasks that you as business analysis professionals
should bring to the table. So let me set up today's topic a
little bit for you. Today you have done all of your
data analysis. You are really excited about
what you found and you cannot wait to share it with your
stakeholders. Multiple choice.
What do you do? Do you take in all of your data
and show it to them clearly, they'll know what to do.
Do you give it to somebody else and have them do it?
Do you find a way to help them understand that data that really
helps them to make a better decision?
And if your answer is the last one, I've got good news.
If your answer is one of the others, I've got good news.
Today we're going to be talking about how to make your data
impossible to ignore with a technique.
It will really help you and your stakeholders understand the
impacts and the stories around your data.
So I want to introduce to the stage our guest today, Ankit
Agrawal. How are you, Ankit?
Hey, Susan, I'm doing really good, and thanks for inviting me
to the show. It's always a pleasure to talk
to you. Yes, happy to have you.
We don't always get a chance to talk about data and I know it's
a lot of what business analysis professionals do.
So it's really great that you are going to bring your
experience because you've got a lot of it and you're pretty
passionate about storytelling. And so we're going to talk about
that in a minute. But first, please introduce
yourself. Tell us a little bit about you
and what you do. Absolutely.
Before you get into my about myself, I just wanted to thank
everybody. And I know it's the middle of
the week Wednesday morning. So thanks a lot for taking the
time in the middle of the week and attending the session.
And also, you know, it's really great.
You know, winter is finally over and spring is here.
So it's really excited about what's ahead of us.
About myself, I work with Verizon, I work as ACRM lead,
customer relationship management lead for a consumer business.
And I help the company with the retention efforts, you know,
helping build loyalty with the customers so that they stay
longer. And obviously, you know, they we
get, we get lifetime value from them.
I did my MBA from Wake Forest University and my concentration
was business analytics. So, you know, throughout my
career, you know, whether it was marketing, supply chain,
manufacturing, data analysis has been the backbone of whatever I
have done. And I'm really passionate about
this subject. And you know, through this
session, I've been hope to bring some of the practitioners
insight on what storytelling with data means.
And I'm really looking forward to it.
Somehow, when you and I were catching up, I failed to ask you
where you had gone to school. I didn't know you went to Wake
Forest. You know, Wake Forest is only
like an hour and a half from me. Yes.
I've been to Charlotte a few
times. Yeah.
I don't go there as often as I would like to.
I'm in Northeast now, New Jersey.
Every time I get an opportunity, I do go there.
Well, stay away. Right now it is spring and the
we just get dust clouds of pollen.
So that's my struggle for today. So, OK, so storytelling Now as
you and I were getting ready today, one of the things I said
to you is that, you know, when we talk about storytelling, I
feel like business analysis professionals think it's not
like a real technique because it, you know, when we think
storytelling, we think of fantasy, right?
But it actually is a really important technique for how we
can get our stakeholders to immerse themselves in what that
data is telling us. So why don't you help us
understand what is data storytelling?
Well, absolutely. And and you know Susan, before I
even I get to storytelling with data, I want to just level set
on why data is so important, especially in business
environment and what does the company gain by making a
data-driven decision? Right.
So I'll just use an example. Imagine you're flying a plane in
dense fog, OK, and you don't have an instrument panel.
So you might think that you are maintaining the altitude, but
you know, you might be losing the altitude or you might be
heading towards mountain, you know, or you may be low on fuel,
right? So, so without realizing you
know, what is ahead of you, you're not able to really guide
the plane. Data analysis is that instrument
panel for the businesses. It tells you exactly where you
are, where you're headed, and what's the trouble that lies
ahead of you, right. So using that analogy in today's
business environment, you know, think about from a, if you put
customer at the center of everything, we know you know a
lot of data about the customer. We know in what behaviors they
exhibit, what the demographics are, what the purchase history
is, how what, what's the digital presence, what's the
transactions they have done in the past, right.
So using all these data points information can really help the
companies form a good strategy, right?
There are three or four things that you know that just out of
the gate, you know the companies can get out of analyzing the
data. One, it stops the kissing game.
Typically what happens is, you know, it's the loudest voice in
the room or you know somebody who has the highest title, you
know they have a big say, but when you bring data into
picture, you know the whole everything just becomes
objective and fact based. You're not doing any guess work
and you know that's what is helping you drive this.
Secondly, what's important is that you know, it really
understand, helps you understand the root cause for performance.
OK. So if certain things are
performing the way they are, you know what are the drivers, if
the sales are flying through the roof, you know what drove so
that you can double down on it. Or if you know the customers are
leaving left and right, you can stop the bleeding looking at and
what the root causes are. So that that's another
advantage. Thirdly, I'll point out is that
it helps you create the future. You know, looking at the past
data is indicative of what might happen in the future and that
really helps companies to do business planning.
I know capital allocation, revenue management, all of that.
And last but not the least, you know it really helps improve the
customer's experience. If you're look having a
data-driven approach and if you look at the customer's life
cycle journey, there are several points of friction that happens
along the way. And if you have inserted those
triggers that identifies those signals that cause friction in
the customer's journey, you can intercept those moments and
actually put a treatment in place, whether it's onboarding
or competitive response or, you know, any transactional issues
that the customers are facing and really help to improve that
customer experience and move towards personalization that
that's that's really important. Every customer has a name,
right? So those are some of the
advantages of looking at the data and analyzing the data.
Now that we have level set on, you know, why data is important
and why, you know, every company should have really have a
data-driven approach. I want to come back to the
original question of, you know, why is storytelling important?
And think about it like, you know, numbers on their own are
forgettable and what stories take.
If I were to say that Earth's existence has been around 4.5
billion years and life first appeared around 3 1/2 billion
years, Dinosaurs reigned between 250 and 66,000,000 years and
much of the recorded human history is 500,000 years old.
I'm sure you will. Your heads head will start
spinning and I bet you're going to have more like need more.
It's kind of dry. It's kind of dry, right?
You know, And I'm sure that, you know, tomorrow, you know, you're
going to lose everything and it's not going to stick.
But what if I told this story in a different way?
And I said that, hey, if you squeeze the existence of Earth
in 24 hour day life appeared at around 5:00 AM in the morning.
Dinosaurs rained between somewhere between 11:00 and
11:30 PM. And much of the human history is
only, you know, 3 seconds old. You know, I'm sure, you know,
that that's going to stick. So that's what storytelling
does. That that's, that's the
importance of storytelling. You know, it provides context.
It provides A-frame of reference that really is important to get
the buy in. Yeah.
And you capture people's imagination.
So if we lean into that part of storytelling, I mean, the
importance of storytelling is that we are creating a picture
for someone and we want them to envision themselves in that
moment. And so I can think about in your
story, oh, if I'm a watch, I understand what a watch is or I
understand 24 hours in a day and oh, well, that's really
interesting. Boy, human written human history
has only been available or tracked for the past 500,000
years. That that's three seconds.
That's so little. So yeah, and it's, you know, you
get people interested in a different way than just a slide
deck with a bunch of graphs on it.
So yeah, absolutely. A lot of times, you know, the
numbers gets thrown, but you know, frame of reference is
extremely important. Comparison point is extremely
important. And if you don't have then you
know, you all your stating is just facts.
So yes, it does not drive a lot of things.
Yeah. And ultimately what we're
wanting people to do with that data is understand the
situation. But then we we generally want
them to take an action. And I guess we just have to face
facts here. Those actions are going to be
more impactful if they are driven by a sense of urgency, a
sense of, oh, I understand why we need to make the decision and
it's now and it's this particular thing.
So it really is important in a way that just facts on paper
can't help our stakeholders to understand.
So, so you've done quite a bit of data analysis and helping
your organization to understand the impacts of that data.
So tell us, you mentioned that you've got a framework, yeah,
that you've used. So tell us about a framework for
good storytelling that our audience might use in their
work. Yeah, definitely.
So when I think about, you know, storytelling, you know, it has
two main components. 1 is the decision that you're trying to
drive, you know, from the data, obviously whatever data you have
analyzed and what's the decision that you're trying to drive.
And second one is the decision maker that you're trying to
influence. So those are the two important
things that anybody needs to understand that, you know,
what's the decision you're trying to drive and what's the
and, and who's the stakeholder in this?
Who's the decision maker when it comes to driving the decision?
Obviously, you know, there are three more three important
things that somebody needs to look at.
First, obviously you need to analyze the historical data.
What has happened in the past and what did we learn from it,
right in the current context, the problem that we are trying
to solve and why does it matter? Or what did we learn from the
past behaviors and what should we do in the future that will
help achieve the goal? So you know what decision it it
drives. Second one is you know the
stakeholders, which is, you know, what are the decision
makers that you are trying to influence?
And it's absolutely important to know the audience the way I
think about data and storytelling and audiences like
in a think about your Google map, right?
I would say data is Google map or in Apple maps or any map, the
story is the journey. You know, you're traversing from
one point to another and destination is really the
stakeholder, right? So that's the framework that you
know, anybody needs to to think about and really use that in a
data is really just a means to an end story storytelling.
And it's the journey which is as important as the destination.
And obviously, you know, the end goal is the destination, right.
Obviously. Yeah.
Well, I was going to say, what do you say to those folks who
are thinking, but I've spent all this time on this data analysis.
Isn't that important in and of itself that I've done this.
How does it so help us build that bridge?
How does the analyst bridge all of that work that they've done
into that plan for telling the story?
Yeah, absolutely. So the way I think about it is a
you need to be aware of what's happening in and around you,
right? Obviously, in the business
context, awareness is extremely important.
And what does awareness mean? Suppose I'll take an example,
You know, you're working in marketing.
All right, awareness means, you know, you need to be aware of
what the products and services the company is offering, OK,
because the value proposition is extremely important and how does
your value proposition differentiate from others,
right. You need to understand what are
the operations and you know, supply chain, dealer, retailers,
commissions, all those things. You know, you need to understand
macro economic environment that is immigration, you know other
things that tax and all those things, you know that impact a
lot of things. You need to understand what some
of the natural things that are going on in the North East
recently, you know we got hit by a massive snowstorms, right.
You need to know about competitive pricing, promos, all
those things. So when you have an awareness,
you've already done the groundwork about OK, you know,
this is in general, this is what is happening.
So more often than not, the analysis that you'll produce,
we'll tie back to one of these 5 or 6 things that I told you,
right? Product, operational,
macroeconomic environment, you know, calamities are, you know,
competitive of those things. Now, once you know all of these
things, then you start the actual work of, I'll take the
example of building a slide deck, OK?
Because I first want to cover, you know, hey, let's go through
like, you know, how, how will you put that in the paper in the
on the slide deck? And then you will go and do it.
OK, You know, how would you communicate that verbally?
All right, so good story. Let's say has 6 important
things. First is the problem that you're
trying to solve that that's you want to have a problem.
Otherwise, you know, you have a solution.
But if you're trying to find the problem, then you know nothing's
going to get accomplished. That's right.
So you need to start with the problem that you're trying to
solve, all right, every problem, once you identify what the
problem is, obviously this, you do all the data analysis, right?
You dig into like, you know, OK, now sales are down, sales are
up. You know, now what's driving
that on one of the channels or or there's something happened
with the competitor promo, all those things, you know, so you
identify this is the root cause that I have.
Then you know that OK, these are the actions that I might you
need to take in order to solve that problem.
So that would be the third thing you know that you need to put is
you know, hey, these are the actions.
Not a lot of times you can have multiple solutions to solve a
problem, all right, If you're trying to get more revenue,
either you can sell more of the product or you can increase
prices, right? So there are two solutions for
this problem. So always.
So most of the storytelling work that I have seen in A stops at
this point, which is, you know, hey, this is the problem, this
is the root cause and this is what the solutions may be.
The real work actually starts after this.
And the buying, the process of buying actually starts after
this. Because what the executives or
what anybody would want to know is, hey, if I do A versus, if I
implement solution A versus solution B that you told me,
what's the impact going to be for each of them?
What's the return on investment I'm going to get for each one of
those solutions? The good story needs to have the
next component of that would be what's it has to be time bound.
What does time bound mean? That is basically, you know,
when can I implement the solution and by when I'll be
able to see the return on investment because you know, it
can't be indefinite, right? I need to most of the companies
obviously you know, we show results on a quarterly basis
going to impact this quarter. Is it going to impact the next
quarter, You know, six months, 12 months down the line, that's
going to be extremely important because that helps with the
prioritization. So those are the some of the
things that executives look at. Last but not the least, I would
also call out that you know, every execution has or every
solution that you have as an execution risk.
Sometimes, you know, you might need legal approval, sometimes
you need new contract, you have contractual obligations,
sometimes you need to enter into new contracts, all those things.
So really the first three are like table stakes.
You know, everybody typically do that, which is, you know,
identify the problem, identify the root cause and have the
recommended solutions. But what executives really are
looking for is beyond that, which is, hey, what's the ROI
for each of the solutions that you have put for me?
What's how soon can I implement it?
Or, you know, when can I start seeing the benefits out of any
solution and what's the execution risk once we have
once. So that's that I feel is the
foundation for any good story, which is like in these 5 or 6
things. So if I think about like from a
slides perspective, and obviously you build all these
things in the slide deck for for anybody, a good story will
always make sure that you know the audience or whosoever is
viewing the content they go away with, you know with what what
what's the. They go away with.
Hey, what's the insight that is driving?
So you know, what's if you say everything in a sentence, you
know what that is? A lot of times what I see is
that, you know, I'll receive this, you know, 5 or 6 slide
deck and hey, you know, please find this slide deck attached
with this e-mail and I'll go through it and I'll go through 8
or 10 slides. The challenge becomes, you know,
AI need we need to find time from a busy schedules in order
to go through all those things. And, you know, a lot of times,
you know, you have to interpret on your own.
A good storyteller will always put an executive summary.
All right. So once you have done the hard
work, you know, that's when the smart work comes into picture,
which is executive summary. Always go if you're sending an
e-mail, you know, with a slide deck or something, you know,
always make put an executive summary in the body of e-mail.
A lot of times, you know, people may not even have time to go
over everything. But if you can just, you know,
put. Your points in two or three
simple sentences. Hey, here's the problem, you
know, sales are down in Southeast.
The root 'cause that we found was in a competitor launched a
promotion that drove their sales and kind of took some volumes
away from us. And we need to, you know, launch
a similar promo or at least you know better launch a better
promo to kind of get retain that market share and this is when we
can launch it. So if you put an executive
summary, you know, that does take a lot of burden from
anybody who's, you know, going through all that slide deck and
really helps you get the vine and think about that executive.
Somebody has your elevator pitch as well, right?
If you meet met that executive in the elevator and and they ask
what's going on, you know, you already have your talk track
ready, but two or three simple sentences.
So that's the beauty of a storytelling and that's getting
the vine. And I think it's deceptively
simple because you know, from I'm kind of synthesizing what
you're saying. So when you're, you know, the
data analysis is really not just the particular problem that
you're trying to solve, but also part of our work is to
understand that in the context of the broader organization.
And then on top of that, we need to know who it is we're talking
to because that is going to help us probably to craft our
recommendation, our stories, and what kinds of decisions either
they can make given their level or that we need for them to make
or that we need their influence to help get made.
That's a lot of work in and of itself.
And then on top of that, I think what I'm hearing you say is that
we need to be able to tell stories in a way that is
succinct, that gets to the point, that has a perspective.
And probably you didn't say this, but I have to believe this
is true. The story has to have a point.
And likely the point of that story is they need to feel some
sense of urgency in order to make a decision, and likely we
are providing a recommendation on that as part of it.
Does that kind of sound like all of the elements of storytelling?
Yeah, absolutely. And if you stick to, you know,
that framework, 5 or 6 things that I mentioned, you know that
that's going to get by. And obviously, like, you know,
the content has to be there, but some of the which obviously
depends on, you know, which function you're working, what
most type of problem that you're trying to solve.
But the two or three things that you know, folks miss a lot of
times is, you know, what's the urgency in terms of, you know,
when it can be launched or delivered, What's the ROI and
the risks are flagging. The risks are extremely
important. You can build a great strategy
and you can build, you know, great promotion or anything, but
if you're not able to execute it, everything goes in vane.
So de risking de risking the project or de risk in the work
is extremely important. And that is something that you
know, any leader would want to know because you know, you can
create all this hype, but you know, if you're, if you, if the
project falls through the cracks, you know, later on.
All the efforts. Gets wasted.
So it's. So there's a lot of analysis
that happens. But also I am wondering if you
find yourself when you are getting ready to go in front of
your stakeholders, do you find yourself personally like doing a
lot of preparation for how you're going to tell the story
and what that's going to sound like and how long it's going to
take? Is that also an element that
you. Yeah.
See, at the end of the day, yeah, at the end of the day,
whatever work, everyone wants to make sure that the work that
they're doing is right. And ground work is extremely
important. You have done all the hard work,
which is, you know, you have analyzed the data.
You have, like, you know, pulled data from multiple different
sources, ran models, everything. But a lot of times, you know, if
you do, don't do this prep work and everything falls through.
So I'll give you an example. Like there are certain pitfalls
I would call that, you know, people probably miss, but it's
extremely important to make sure that, you know, before even
presenting you take care of that, right?
So a lot of times what I see is. Analysts by.
Nature, you know, we have done a lot of hard work in order to run
all these models and everything. So we have a tendency to put
everything on the slide. Basically we are showing, you
know, how we have done the work and how we have arrived at
particular recommendation. Well, a lot of times what
happens is that you don't need to tell how the sausage is made.
For the most part. You know, everybody is
interested in knowing, you know, how the sausage is and how
what's the aroma, but you know, you don't want to show how the
sausage is made. So that's one of the common
pitfalls that I see is that, you know, you tend to show a lot of
things on the slide or anywhere, which is good, but it kind of
deviates the audience on the main thing.
A lot of times what I also see is that, you know, your data
does not match the golden source of truth.
So let's make sure that you know, whatever the golden source
of truth is for the company, just make sure that you know,
everything is matching at the high level.
Because if you are saying something and it's different
from what people know at the top of their head what the metrics
are, then you know, and then the question becomes say, is the
data even correct? So just make sure that you know,
you do the due diligence and make sure that it's matching.
Third thing that I'll say is very important is
stakeholdering. As analysts, you're bound to get
insights that are that are kind of different from the hypothesis
that prevails, right? And in that a scenario, if
you're making a grand reveal of that insight in front of the
executives, then you better stakeholder with the program
leads and make sure that like, you know, everybody In Sync, you
do the ground work So that you know, once you make the grand
review, you actually talk about what actions you're going to
take if the program is not performing.
If you're done campaign analytics and you know the
program is not performing, you better stakeholder with the
program lead first before making the grand reveal in front of the
executives. Otherwise, another question
would be, hey, you know, what are we OK?
You know, you have told me this, what can I do about it?
And then it comes on you that hey, you know, I don't know, not
a good answer. The program lead.
And then what you have done is you have thrown the program lead
under the bus. That should not happen.
You need to do the stakeholdering before that so
that you put a combined front that hey, look, obviously in all
transparency, we absolutely need to tell the executives that hey,
you know, something is not performing.
But, you know, this is we brainstormed, you know, before
you when you asked and this is the action or this is the course
correction that we are going to do.
It actually fosters collaboration and, you know,
builds a relationship with the program leads.
So that is something that's extremely important.
And well, I was going to say, you're right.
Sometimes the stories that we're going to have to tell are not
happy ones. And so, you know, if you are
going to be the deliverer of the story, that's not a happy one,
it probably is a smart thing to do to talk with your allies, to
give some folks some heads up, to make sure that there's a bit
of a united front. Because if you're out there
telling a story and it's going to be a surprise to people, that
is probably not a good place to be.
Yeah, agreed. Agreed.
And sometimes I also see that, you know, people get hung up on
the tools. Obviously, we have so many data
visualization tools out there. Yeah.
And hey, this tool is cool. This tool is really good.
At the end of the day, tool is just a means when and and we
need to even Excel or Google sheet works as long as it's
driving. Some action.
So don't get hung up a lot on the tools but.
Understand. What insights you're able to get
and what actions in it will drive.
That's such a good. Reminder, because I know that
people really do feel a particular way about a tool that
they use as if that will, you know, all I have to do is just
give my stakeholders access to this tool and they'll just see
it all. There is a use for our work as
being the reader of the data, the teller of the data.
Don't give that up to a tool because the tool can't know the
nuances of all of the things that you've talked about today.
That's really important and that is important work that business
analysis professionals do. But I've got a question for you
on the kind of the tail end of sometimes our data can tell a
not happy story, but we can also tell good stories with bad data.
Does that sometimes happen? And what what do you do?
Well, how do you get out of that mess?
Well, that's called making up some shit on the fly.
That's what I'll say. And you can get away with that,
you know, once, maybe twice. But at the end of the day, like,
you know, folks are who are ahead of us in the chain will
realize and figure out that, you know, you're making up.
So that kind of. That affects your.
Credibility that that's what I'll say and and.
It goes so. Credibility, building
credibility is very important because you know, once you build
that credibility, you don't have to show a lot of work on the
slide, right. If because you know, a lot of us
when we are trying to show a lot of our work on the slide or
anywhere, you know, we are trying to build that credibility
that look, you know, I've done my due diligence.
But once you build that, then you don't need to show that
because you know the expectation is that you have already done
all the the legwork and and. And we?
Are going to take it at face value, whatever you're saying.
So. So so, you know, one or once or
twice and you can get away with that, but not not often.
OK. So that's really important, I
think, for people to take away is that part of storytelling is
the storyteller. That's an important element to
this. And if you have a reputation for
credibility, you know what you're doing.
You've been in front of them before.
They've seen good results in some ways.
That saves you some work is what I'm hearing.
It saves you some work in the future, but you still have to do
the work today in order to build that trust.
So trust is an important part of storytelling.
Absolutely. Yeah, yeah.
Wow. OK.
So that's a big deal. Now, we are Live Today.
So for the folks that are listening to us, we're live.
What questions do you have about data storytelling?
I'm going to pop a comment in. I think this is more of a
comment from a listener and that'll give folks maybe a
minute to think about their questions.
So Doctor Divya Jase Wall, thanks for listening today.
So stories help stakeholders understand the why behind the
data, use data to build a logical case, but craft a
narrative that connects emotionally to encourage by and
see there's that. You got to you got to get their
emotions. The 5C's of storytelling
incorporate circumstance, curiosity, characters,
conversations, and conflict. What do you think about that?
Had you heard about these 5C's of storytelling?
I have not heard that before to be honest.
So in some form of action I have but exact same code.
Probably not, but I completely agree with the comment.
You know, situation, you know, ties back to the problem that
you're trying to solve for and. Then audience will.
Be engaged once they know that you know here this is the root
cause that is driving that problem and you know what can do
about it. Stakeholders are the characters,
right? You need to know the audience
and you need to make sure that like you know, you information
or build your narrative in a way that resonates with them, right?
And that's where all this conversation comes into picture.
And obviously, you know, conflict, something that we
talked about, which is like stakeholding and a lot of that
conflict. And actually, because all
through stakeholder, a lot of times, you know, you'll get an
insight which is obviously no different from what everybody
thought. So that's where, you know, it
becomes extremely important to do the stakeholdering and make
sure that everybody's aligned. You know, we may agree to
disagree, but we should always be aligned and and make sure
that like, you know, we know what's going on.
Yeah, I can imagine that sometimes you're giving, you
know, you're telling the story of the data is you've analyzed
it, but that there could be a stakeholder in the room that
takes something completely different away from the data or
has a completely different understanding of it.
And so being able to navigate that conflict, and I know
sometimes we talk about conflict on here, it is something that
can be a little bit scary, but also it's a one.
It's a great opportunity to build alignment, but it's a
great opportunity for business analysis professionals to build
trust if you can help to work through that.
So it is a really powerful. Thing is to.
Navigate conflict. OK, so I don't see any more
questions yet, so maybe let's keep talking.
We've got a few more minutes. I, I feel like it cannot end
today's conversation with you if I do not ask what the role of AI
is in storytelling. Do you use AI as part of helping
you to prepare or think or brainstorm stories?
Like what do you see the future of AI for storytelling?
Absolutely. You know, the feature is in more
data. I would say, you know, we have a
ton of data already, but the feature is going to be narrower
focus. As we get.
All these models the and AI does the heavy lifting of
calculations. The importance of human
storyteller becomes 10 times more important because that
that's what is going to ultimately Dr. the decisions and
and convert the insights into actions.
So that's going to be extremely important.
Now from our perspective, what I have, what we are observing for
the most part is. Data readiness for.
AI is extremely important. It's like the iceberg, you know,
you see only 1/4 of the iceberg about the surface, but three 4th
is, you know, under the surface. So data readiness is that three
4th portion of that iceberg and what does?
Data readiness. Mean, you know, having the right
definitions, having. Having all the data.
Sources, you know, at one place, making sure that like your you
have the right it is it's like in a garbage and garbage garbage
out. So you need to make sure that
like in whatever data you have like and it's complete, it's
accurate in order to drive the insights from that.
And that that's where I think you know a lot of.
Lot of. I would say failures.
Come into. Picture, you know, in terms of
implementation of the projects is, you know, you just pipelines
are not built properly, your data is not ready and you're
trying to learn something or you know, analyse something from
there. So that's the first and that's
one of the major things that we have done.
We are doing this to make sure that, you know, we have the
right data set up. All the sources of data that we
have, you know, converge at one place and the definitions are
proper and the linkage between all those data sources is proper
so that, you know, you have the transactional data, you have the
behavioral data, you have the demographic state.
Everything kind of ties. In and you are able to track any
particular customer so once you have all that that's like kind
of the ground work and I feel that you know that's extremely
important for any AI project once you do that then obviously,
like you know you got to leverage the tools that are out
there in a copilot general all those to get the insights from
there and really once you set up that data frames correctly, you
know the. The prompt part or or.
That becomes much easier once you have the data set up
correctly, yeah. So what I.
Hear you say, is that in fact, with the introduction of AI into
the data analysis that we would do to prepare ourselves for
these stories, the human work becomes even more essential.
So there's data accuracy. There's also context.
I mean, you know, you've talked about understanding your
environment in your audience. AI can't do that.
You have to do that as the analyst.
And then finally crafting that story and being able to tell it
is absolutely a human thing. In fact, in some ways, as we're
talking about this today, I'm, I am thinking that storytelling
becomes even more essential to maybe more than just the data
work that we're doing, even just a lot of other work, because it
kind of brings us to back together to that human element,
which AI just can't do. So it sounds pretty powerful.
All right, we've got a couple of questions in that time.
All right, maybe we'll try to take two just because we are
kind of running short on time. So here's a good one.
This was from Emmanuel. How do you adapt a data story
when presenting to non-technical stakeholders versus technical
teams? I'm sure that's something that
you have to do all the time. And let me, I'll add this piece
to it because I'm actually curious about this.
What if you've got data analysis that you have to present to
those two audiences? What are the things that you
have to think about those when telling essentially the same
story to two different kinds of audiences?
Yeah, absolutely. The first thing that I try to
understand is what is the metric that the stakeholder is trying
to drive? That is essential if I am
telling that story, to stick to a business person who is a
business executive, right? Obviously, learning the metrics
that they're driving is extremely important.
And take example, you know I work with a lot of executives
who are. One of the primary focus.
Is subscriber growth, you know, either you gain more subscriber
or lose or how do you prevent the subscribers from going to
the competitors? So then the work becomes OK and
how do I tie back my work to these metrics that executive is
trying to drive or he is the DRI or he or she is the DRI and
directly responsible individual, right.
But you know, if I'm talking to the technical teams, take
example, you know the models that they have built, right?
So for them the metric would be we have built a model for
subscriber editions. So for them probably the metric
would be what sort of lift a marketing lift that model is
trying to drive. OK.
So then the conversation becomes, OK, what are the
drivers of that particular model?
What are the things that are impacting that particular model?
So it's really depends on, you know, understanding what metrics
that the technical stakeholder versus the non-technical
stakeholder is driving and then in a curating the content based
on that metric. Yeah, that makes a lot of sense.
So again, understanding the context in which those
stakeholders are working and when you talk about knowing
their metrics, that's the thing that they're most interested in.
So that's like they got you. It's absolutely, you know, if I
if somebody's trying to drive, say obviously everything kind of
converges and ties back together.
But if the same thing, you know, if I'm telling it, hey, you
know, I can increase the lift of this particular model by 2X or
3X and they'll say it's good. But at the end of the day, you
know, the question that they'll ask is OK, then what does it do
to my bottom line or my top line growth?
So it kind of goes back to their metrics.
So understanding those two metrics, those the metrics that
anybody is responsible for this extremely model.
Yeah, I'm. I am.
Thinking so you know, how do we convert that?
This is the metric that I care most about, right?
Every story that you've ever read always has some sort of
element that is the challenge, right?
And so you've got to be able to not speak to.
Gosh, what am I trying to say? Here, so we're not wanting to
focus on specifically the metric, but the sense of urgency
around the growth of that thing. Sir, you're identifying that
challenge as part of the story. Yeah, that's what's really
important. OK, So we've got one more
question here. This question is from Matali and
Dated Storytelling. At what point does a complex
visualization stop being helpful and start becoming a
distraction? That's a good question.
What are the key elements of a truly great slide?
I can do another podcast on this.
What like what would the? SEO even be for that because
that's awesome when your visualization stops the helping
and starts hurting. First of all, like, you know,
visuals. Are extremely important, you
know, because of brain processes visuals faster than the
sentences. And the key to any great visual
is, you know, the three second rule is what I call if the
visual, if somebody looks at the visual for three seconds and if
they are not able to grasp what it's trying to say, you know,
scrap that visual. So obviously simplicity is the
key over here and aesthetics, I won't say matter, but you know,
aesthetics do not matter as much.
The hero of the chart should be the insight that you are trying
to convey. OK, that's extremely important.
Make sure the chart is decluttered.
Like you know, chart is not cluttered.
Simplicity is key over here. Make sure that you know the
fonts and everything are are consistent colors you know
certain. Colors have a negative.
Connotation. So make sure that you know, you
use the colors which are a consistent with the brand with
your branding colors, right? You know, red has a negative
connotation to it. So, you know, if you're doing
financial reporting and all, you know, make sure that you know,
red denotes something which is worse, performing worse and
green which is, you know, performing better and then the
expectations. So that that's important.
What type of chart you use. That is extremely important.
You know a lot of. Times you know you.
Choose a chart which is not representative of what the data
is trying to tell. Like, you know, use like if I'm,
I want to compare the category sales, I have 10 product
categories. I want to compare the sales and
I would rather use a bar chart or a column chart.
If you use in a pie chart for that, for showing, you know, 10
different categories, you're the size of the pie would be like,
you know, less than the pieces like.
So use the charts judiciously is what I'll say.
You know, if you're using a pie chart, no more than two or three
pies is what. I'll say trend.
Lines you should use, you know, if you are just a line chart, if
you are trying to analyze the trend over a period of time that
that's extremely important. Some correlations, if you are
trying to demonstrate correlations, then you know,
obviously you scatter plot and all.
So it's kind of, you know, that's storytelling one O 1
actually, I would say and extremely important, you know,
how do you visualize and a couple of other things that
comes to my mind is, you know, legends and all, you know, make
sure that legends and are clearly visible so that you
know, people understand. One thing I often see is that,
you know, on the data labels, suppose you are showing sales,
right? And if the sales are like, you
know, $5,000,000 for a particular product category a
month, a lot of times you know, you just write 5 and followed by
6 zeros. Well, that's a big the dust
platter. So in an abbreviated data
labels, you know, that's right, five M.
Those are some of the basics and some of the important things
that you should care about for making the visual simple.
You're going to come back in June, you're going to do a
webinar with us. And I feel like maybe
visualizations is what is maybe the topic.
I'm just putting that out there. Maybe visualizations are the
topic because I think you've got some good advice and I think a
lot of people are interested in not just, you know, how do I
create one, but those elements that you've given, I think could
be really helpful. Now, I don't you either you said
this or I, I interpreted this, but I think you said something
like you're the hero in this story, not the slide, but it
helps. And what that made me think of
is like Spider Man. So I mean, he's got that suit.
The suit's really important, but actually it's Spider Man himself
that does the things. It's just the suit makes him
noticeable. It really backs up who he is and
what he does. And I don't know if that's a
great analogy for what we're talking about with data
storytelling, but maybe that's a different way of kind of, you
know, the difference between the storyteller and the stuff that
backs up the storyteller is maybe that.
Yeah. Storytelling is marketing your
data analysis. That that's what I'll say, you
know. Yeah, it's I'm a marketer.
And in other storytelling is marketing your analysis work
that you have done. And without marketing, you're
not going to get far. Yeah, that's right.
Yeah. Exactly.
All right. Well, this has been a fantastic
conversation today. I really thank you so much for
your time. And like I said, you're going to
come back in a couple of months and do a member webinar with us.
And I know today we were kind of kicking around whether we might
identify some topics for that. And so maybe we've got a couple.
So we'll have to. Yeah, we'll be back in touch so
we can learn more about that. Thank you again.
Thanks a lot, Suzanne. It was a pleasure.
And look, looking forward to the next one.
Yeah. Take care.
We'll see you. Thank you.
All right. So I, you know, maybe you are
thinking about data storytelling a little bit differently now.
You've got some frameworks, you've got some things to do and
things not to do. I hope that helps make
storytelling a little more accessible to you.
We're going to see you again in two weeks and I'll have two
guests for that. One.
We're going to talk about the process improvement mistakes
that analysts make and this team of business analysis
professionals, they are going to tell us not only some of the
mistakes they made, but how they learned to do process
improvement a little differently and how they have built that
into an upcoming BBC tutorial that they're going to give.
So I want to thank you guys again for joining us today.
We'll see you in two weeks. Thanks for listening.
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