Transcript
WEBVTT
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When weed more into the organizations,
most of the time we are finding less
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than ten percent of the people are
at the appropriate level of data liiteracy.
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Less than ten percent, which is
shocking. Data strategy, data culture and
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data literacy. They're interrelated, but
they are not the same thing. Today's
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conversation we kind of peel those things
apart and we get specific about data literacy,
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specifically persona based data literacy. How
do we make sure each person in
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each role is as knowledgeable as they
need to be to perform their job effectively
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with regard to collecting and using data? Our guest is Pianca Jane. She
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is president and CEO of a ring, a data analytics company that's working with
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companies like Google, apple, adobe, paypal, box, electronic arts and
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so many more. So many solutions
to helping your team get more data literate
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for the benefit of your customers.
My name is Ethan Butte. I host
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the CX series here on BTB growth, I host the customer experience podcast and
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I'm chief evangelist at Bombomb, were
make it easy to get facetoface through simple
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videos. Here is my conversation with
Pianca Jane. Leveraging your data in order
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to lower your customer Mara acquisition costs
and Improve Your customer experience. Today's guest
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is president and CEO of a ring, a company that does just that for
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clients like Google, apple, adobe, paypal, box and electronic arts,
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among many others. A few processes
and techniques her team uses developing their clients
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data DNA, building data literacy throughout
the organization and creating citizen analysts. She's
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a best selling author and keynote speaker
on data driven decisionmaking, data analytics and
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data science. PIANCA Jane, welcome
to the customer experience podcast. Thank you
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for having me anything. I'm really
excited to get into this and into my
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memory. We haven't done a full
and proper conversation on data here on the
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show, but it obviously underpins any
effort that anyone's undertaking throughout the organization.
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But before we get going, we're
recording in early to mid April. You're
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in Silicon Valley. What's the situation
there with regard to coronavirus? How's it
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affecting you or your family, your
team or your customers? Kind of just
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set the scene really quickly. Yeah, so we have been in shelter,
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a shelter in place for put a
good four weeks now. I think I'm
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going to go with the May Forst
Week of my as off. Now let's
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see how it changes. You know, the numbers up somewhat black doing or
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in nondepends on how you see it. The testing it at least this flat
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doing. Who knows? And comes
out? We were, we are at
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a more team to begin it's so
we are. I mean I'm based here
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Si that's some folks in East Bay
La, some folks in Toronto, we
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have folks in India. So we
are all remote to begin with. So
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we are set up that way.
So I didn't affect us from the working
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perspective, but our clients are definitely. I can we can see there's a
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lot of panic and lot of budgets
freezing and and a lot of like not
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knowing what to do. The two
things we do for our couporate times as
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a consulting site that we do end
to end. There's also the training part.
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We do the DETA literacy, data
culture work, I think, which
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is what we're going to talk a
whole lot about. And then, because
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this is a prime time, many
people are seeing this as a downtime for
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their for their employees. There's a
lot there's a bigger interest in data culture,
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developing Ada culture, upgrading skills and
so on. So there's some shift
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happening within our our sort of our
portfolio of offering as well. But,
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you know, overall, I think
we all need to do the right thing
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and stay safe and this too shall
pass, just like anything else. Yeah,
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but yeah, I think in general
people are taking it well, for
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what I understand, very good.
Yeah, I think it is. You
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know, this two shall pass.
We're not sure where exactly we are in
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it right now, and I think
that's why there's a lot of freezing and
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not a lot of dramatic decisionmaking.
But just like, okay, O,
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caause, let's see what's growing out
here and move for I'm really glad to
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hear the people are investing in training, in development. That's awesome. So
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we're going to start where we always
start here, which is customer experience.
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When I say that, what does
it mean to you? For me,
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customer experiences of holistic experience right like. So, for example, if I
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am buying something online, I'm a
I'm a big Amazon Shopper because I'm a
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big convenience lover. You know,
for me that experience of you know how
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I order, how how I can
I can count on receiving when they are
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saying it's receiving. So I was
six year role and I just ordered a
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butterfly project for her because we're staying
at home and I need to do something
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more creative and interesting with her.
And so we're going to see butterflies grow
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for next three weeks. And so
I ordered something from from an kid,
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a butterfly hits from Amazon, and
know sort of the data tay come and
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I know I can prep my daughter, like you know, it's coming here
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and then we will really making logs
and how are we going to tract,
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like you know, and she knows
all those, all these stages of the
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you know, the butterfly like a
coooning, crystallism and all of that.
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So I love that, the the
dependability, I love the ease things get
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dropped to me. I love the
ease of you know, something is not
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working, I can call in,
I can get a customer sport experience.
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Very very, very convenient, very
easy. They know me and they also
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probably know me as a high value
customer. So I get very easy.
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You know, easy access, easy, everything is easy and I like easy.
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So for me, be customer experience
is how I get touched by a
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product company, end to end,
so not only from my ordering to my
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receiving the product to my if I
have a problem, you know the order
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is not arrived. What happened?
You know it if you should right away.
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For you, there's something got misplace, whatever else. And US.
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So easy versus, you know,
sort of having to idea the ones.
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I started shopping in Amazon on them
as only we like seven years ago.
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I haven't gone back and I hardly
shop. I don't I mean these days
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a gold shop anyways, but you
know I hardly shop and retail stores.
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Actually, my daughter doesn't know what
a Moll is because we've never probably gone
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to a man. Well this.
Yeah, so they yeah, the frictionless
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experience Amazon has come up a number
of times on the show, especially with
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the closing question of you know company
that you really appreciate in its that frictionless
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aspect to it, and you also
spoke to the transparency. You just know
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what's going on and should anything be
disjointed at any point, you already have
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this trust built up there's a relationship
piece to what you talked about. To
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you know, it's not just about
that transaction, it's about all of the
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transactions for years to come. So
you know, if you're seven or eight
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years in now, you'll obviously still
be working with them seven or eight years
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from now and they'll know you better, they'll be able to serve you better,
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which is awesome, which I think
leads into where I wanted to do
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next, just one step deeper into
cx here, which is, from your
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point of view, what's the relationship
between effective use of data and delivering effectively
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a high quality customer experience? That's
a great question, Ethan, and I
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love the way you framed about trust
and relationship as well as I. You
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know that's that's a big guy.
That's giving some good ideas to start thinking
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about the way I think about because
expedience and now putting in the context of
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data driven customer expedience. So I
think that having in effect of data strategy
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or having an effective way of getting
access to data, we call it data
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maturity in general, that's a foundation
for to deliver a good custom experience,
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a build better products or delight your
customer in any way you want. So
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that's a foundation that's a must have
for you to be able to delight your
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custom for you be for for Amazon
to know that I'm high value from Amazon,
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to know that you know this is
this persona probably is a convenience lover.
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So don't argue with her about things. Make it easy for her,
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as she calls things like that.
So you need data done well. You
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need good access to data and good
and right access to data to the right
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people, because it's not, you
know, all these decision is not being
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all made by mean. There's a
lot of auto ml going on, there,
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a lot of machine learning, a
lot of recommendation system but there's also
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people there and different people need access. A customer support needs a different access
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to my data versus the you know, the person who's doing maybe there's some
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fraud activity. Is something happened.
Different job and person and you can call
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it a different job title function.
Need different kind of access to my customer
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data so that the rightful access and
true access, an easy access, is
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is data maturrey? That's a foundation
that's injustice. What is data maturity if,
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when you have that now, you're
set up to actually crack the code
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for anything this, you know,
the thx be a product experience, be
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it product features, be it marketing
date, whatever you want to optimize,
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as it relates to directly to customer
experience. I think the way we approach
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it, at least for our clients, as we start thinking from what is
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it that you want? What is
it must have experience that you want the
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customer to have, and then back
built backwards from there. So, once
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you know, what do you invasion? I think, for example, you
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know, and I look up Steve
Jobs, for example, the data's visionary,
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he is to talk about the experience, the experience that he wants people
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to have, like, you know, when they hold a phone and iphone
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in their hand. This is how
they should feel, this is what the
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feeling if you can start from that
and then work backwards to it. If
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we want to deliver that, what
all do we need to enable on our
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systems and from our people's from our
processes perspective, and from there, data
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can support you in, you know, in supporting that vision that you have,
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but that assuming you have good data
maturity underlying it. Otherwise you will
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be like running after data and not
knowing where to you know, how do
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you chase, the stays after data. It's because it's non existent, or
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is it hiding somewhere? So good. There's so many follow questions I want
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to ask your I couldn't. I
should actually have been writing down some of
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my thoughts. They're so you can
follow up. And a couple key things.
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You're one. Obviously, data is
a means to an end, not
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an end in and of itself.
It's a tool to get a job done
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and you really positioned it clearly and
obviously there and that it is foundational to
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making any aspect of the business better
and more effective. And and you also
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pretty well tied up where I what
I wanted to ask next, which is,
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you know, for folks who aren't
familiar with a ring, who are
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you? Who is your ideal customer
and what are some of the problems that
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you solve for them? Sure,
great, would love to share air ring.
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We are a data science consulting company. We Are Boutique, so I'm
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not large, but we are lean
and mean and our flavor of data science
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we do is all about practical data
signs. So we are all about you
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have some data, whatever form of
data you have, you have some questions.
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How do we quickly use the data
you have at hand to solve that
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problem? Now, we do do
that to two ways. For the corporate
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client. One is we going and
solve large, you know, high value
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problems for our clients hands on and
we are known to be the fastest and
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the most efficient. So between two
to three cutsertants can build a machine learning
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model with an eight to nine weeks
and we are fast because we are hypothesis
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is driven. So underlying all of
the work we do is a framework called
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bother, which is our recipe for
baking gake. We have a recipe for
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baking data essentially, and its it
marries data science with decision signs and it
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is hypothesis driven, which basically enables
US fast delivery to insights and make sure
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make sure that whatever we are,
whatever we were to deliver for our client,
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we get used. So that's our
sort of like our footprint, so
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to say, in our blueprint of
what we do. So there's one part,
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is data science from sulting, when
we go into organizations and solve you
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know, we have custom acquisition issues. We want to lower TUSTMAT position to
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cost. We want a segment our
customer, we want a better target.
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We want to create offering and messaging. You know that Matrix, to optimize
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Roy. You have customer retention,
employee attention, you know, you name
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it, marketing to sales attribution,
all of those things. We love solving
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complex problem. So that's one one
end. But we are also all about
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we also a lot about evangelizing.
So we are all about how can the
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empower our customers to do it better
and do it themselves? And so there's
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a one big part of work we
do is on data culture and detaliteracy.
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So we go into organizations who already, especially very mature organizations, organization where
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they the data. Digital transformation has
happened to them, but they are not.
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The employees are not ready for data. They are scared or data.
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And we go into such organization and
we establish a culture of data to a
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curated me, you know, member
framework. So we first assess the data
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culture for that organization. Again,
we have a methodology for that, and
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then, based on what we find
the gaps in either data maturity, we
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talked about data maturity already, or
data literacy. That's the second sort of
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therefore, these data culture. The
second one is data literacy. The third
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one is are different leadership and the
fourth one issue making process. And as
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we find gaps in any of those
four, which is broken down to thirty
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dimensions, we are able to address
that and fairly quickly. With then we
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can turn around to a turnaround from
moving a data culture portion of five to
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above seven within nine months. If
the organization is ready and a large scale
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we can stay. Look five thousand, ten thousand people. So that's our
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tow body work we do for our
corporate clients. We also have an academy
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much like corset up, but it's
focus on the business problem. So we
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have an academy where people can go
on Academy Dot aidingcom and they can take
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the systectally for sales and marketing folks
as a citizen analyst track that they can
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upscale themselves with sixteen hours of course, and then a project fall on project.
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They have a data of future data
scientist track. We also have a
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current data scientist tract and an executive
track. Awesome. I will link that
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up in if for folks who are
listening. I we always write up some
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of these EPI we always write up
these episodes whole, a handful of video
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clips we embed the audio and you
can see all of that at bombombcom slash
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podcast and I'll include a link to
the Academy for folks that and we're going
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to get into some of those definitions. By the way, cruis talk about
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citizen analysts, so people can self
identify and maybe jump into one of those
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learning tracks as they see fit.
So let's talk about some of the basics
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of data literacy. Like I expected, many organizations probably wouldn't call themselves data
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illiterate, but I'll also bet that
many of them are metric rich but information
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poor. And so just talk about
how do you define data literacy and,
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as you know, for the companies, maybe the eve engaged with or that
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your team is engaged with over the
past couple of years, kind of what
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is a common state of data literacy
and then we can maybe progress from there.
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Yeah, that's that's a great question. So you know, from the
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last base such I read coming from, I think, accenture and fuel.
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It published it together from their global, you know, sort of survey they
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did. They found sixty four person
of the or more of the people were
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not identified as data literate enough.
When we go into the UN, what
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is data literacy. So they are. Literacy is the ability to use data
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for making useful decisions, to use
that for you to derive insights and usual
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with Mrs now, that is not
one sise at all. You can't say
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I'm data literate or illiterate. It
is am I at the level that I'm
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functioning? If I'm a customer support
agent, do I have the right data
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literacy to be at for my level? And so how do we define that?
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We define at their personal levels.
For customers support agent, maybe they
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need to be a data enthusiasts.
That definition varies across business to business and
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what what the leadership wants for that
role. So, for example, most
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of the time customer support agents are
tend to be what what the target deliriously
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profile they should get to as is
data enthusiasts. For a marketing manager,
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the target profile is often data educated
for or actually are Su sometimes as an
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analyst for a product product folks.
Usually it's data educated for executive. Is
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Typically data driven executive. So they
are. These are definitions, but they're
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basically varying level of data literacy.
Some people, you know, if you
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start from the data enthusias, they
need to be enthusiastic about data and to
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be able to understand basic search charts
and tables right for all the way from
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there to data scientists, they need
to be able to use advanced methods,
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to be able to derive in size, make models and informed decisions and work
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with stake good is information. So
there's a wide variety of those, you
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know, and everything is in between, right. So when we go into
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the organizations, most of the time
we are finding less than ten percent of
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the people are at the appropriate level
of data literacy. Less than ten percent,
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which is shocking you. Most People
Think, oh, I can download
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data and excel and I can look
at it and I can pick the number,
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so I should I am data literate, while are you data literal enough
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for your level of what you are
able to do? And the surprising and
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the sad truth is most people are
not. And what does that mean?
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WHY SHOULD WE CARE? They are
dedly dealt it or not. We should
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share, because if they are not
data little enough, they're not able to
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optimize the decision they have at hand. Your marketing manager, who's going to
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spend your hundred K next in next
quarter? If they're not data little enough,
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they're not going to be optimizing that
hundred case pen maybe that hundred k.
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You know, maybe they'll spend hundred
K and they'll get you five hundred
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K in return from, you know, incremental sales or whatever else. But
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what if they were data literate and
they could get you a million? Right,
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you're losing your leaving that five hundred
k on the table. And many,
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many CEOS and CMOS and achieved a
low officers. They are recognizing,
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Oh wow, we are leaving money
on the table and especially in the context
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of right now, in this post
pandemic situation, all of our margins have
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been eaten away. You know,
we have. We have kept employees level
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to the same level. Are All
of us are in sales and marketing specifically,
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are pipelines have dried up. We
could have been squeezed on both ends.
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Do you think you have now you
can you afford to do? Oh,
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I'm going to do these ten things
and let's see which one sticks.
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Can you afford that anymore? You
can't. And many, many organizing and
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in fact, I I predict that
companies who invest in data atacy right now,
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who investing upskilling their their their employees. They are the ones that want
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to try post pond, I mean
because post, I think the there's only
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a new word right. And if
you're not, if you don't know the
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language of business, if you don't
know how the language, the language of
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business is data, you don't know
how to do what to do with data.
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I think what we left beyond in, you know, you're lead to
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look for some other, another profession. It's so interesting. Obviously now I
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understand why you used personas earlier in
the conversation, because it's essentially what you're
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talking about is mapping the appropriate level
of literacy to the appropriate role, or
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to a specific role rather. is
also really interesting to think about. You
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know, coming into the conversation,
as I think about my own challenges and
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using data, they're often around,
you know, should I believe this source
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or that source? Am I getting
the right data? Can I trust it
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it? Is it being presented the
right way? Is it hygienic, etc.
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But you're talking, I mean that's
a completely separate set of challenges and
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problems. You talking again it with
literacy in particular, are very specifically about
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two people actually understand what's being presented. Can I would assume that asking the
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right question is probably critical to effective
data literacy. So you started with that
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ten percent number. Share anything that
just came to mind there and then maybe
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give a couple tips on like if
you want to make that path from ten
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percent to, you know, forty
percent literacy, what are some easy things?
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Shouldn't have said it that way.
What are some things? What are
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some things that could be done to
start moving that direction? Yeah, that's
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that's great. Their lots of less
loves there and I want to address everything.
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So the part that you've talked about, which was about, you know,
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the is a data accurate and is
it making sense? Where can I
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get it at that is all data
maturity. So that that's why there are
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four days of data culture. The
part that you talk about is the foundation
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on which everything else sets. If
you don't even have easy access to single
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source of truth, everything else will
be a little and because you can make
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data, you can make me,
you can train a best data scientists.
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If they don't have access to the
right data, what will they train their
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Bodos? Right. So data,
data maturities, as the part that you've
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talked about, a lot of people, a lot of people think about data
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when they talk about day literacy.
They start thinking about, Oh, you
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know, do we where do I
get the data from? You know,
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all of that is data maturity.
So that's set, that's a that's a
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foundation if that's given to some level. There's not a zero one for Datamaturity,
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but that's given a meaning. You
are on a scale of zero to
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ten. Your seven or aw.
That means you have some good cohesive sense.
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You know, maybe you have to
Click Your Tableau Board and in your
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wait for five minutes instead of five
seconds. okay, understand there's some there's
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some curdles and hiccups in your customer
experiences, but you can get data.
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That means you have some level of
data maturity that can enable the rest of
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the three Ds. The other other
three Ds, as we talked about,
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is data literacy, day, diven
leadership and decision making process. So talking
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specifically about data literacy. So and
let me talk about the dons and then
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I can do this right. Here's
what's happening in the A and the I
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can. I share this with a
little bit of frustration because I am part
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of these conversations where a CEO has
told maybe ahead of DNA or a head
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of bi or CIO and saying we
need a culture of data. Let's up
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skill, let's get Deta Literacy,
let's get detailacy. So the CIO or
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the head of DNA or some sets
up a meeting with the LD leader,
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a Ch our person. Say we
need data liutacy. Can you go find
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out what training we can put our
employees through? And then you can imagine
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what goes after that. They start
looking for training. Sometimes we surface up
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in that conversation. We I'm having
a conversation with the led leader and they're
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saying we need this training. You
guys have these these training. We just
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need these training. We can license
it. And I'm sitting out there and
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thinking we can license these training to
them, but they are not going to
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get data litric. You know why? Even the have insufficient maturity. Well,
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they can be first of all,
their approaches. Let's train everybody.
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Data literacy is not one size at
all. Right, the data that customer
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support agent will need to be a
different level than the executive he needs.
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They need a different skill set.
Yeah, the persona mapping, persona mapping.
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So you're without even persona mapping and
persona map needs to be mapped to
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competencies. You haven't thought about competencies
and skills. You haven't thought about personas.
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You want to put everybody through one
training so in three months you can
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just say, Oh, there,
letacy done. It's not gonna happen.
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And you know who you're going to
blame. You're going to blame the technology
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or the training material. None of
them are to be blamed. The what
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is what is to be blamed this? You haven't thought through this. How.
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What is data literacy? What is
my vision for data literacy and how
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am I going to get people to
these different levels of the air litacy right?
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And when you start thinking that way, you automatically start thinking about competencies.
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You're going to think about what is
the out what is the output I
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want? What is the outcome I
want? At which level? And that
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means you will start thinking, what
are they at current levels? You're going
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to start with assessment. All those
things will naturally happen when you start thinking
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that. With the other thing which
people do and we all do right,
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like haven't you and I? We
all gone to training. We've been so
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excited with it, online or offline
or off site, you know, slipping
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a glass of wine and taking not
like wow, this is so fundamental,
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this is mind blowing, and you
come back to work and you've forgotten all
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about it. You mean, you
know, you talk about to do your
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coworkers for two days, three days
next week. We kind of don't remember.
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The week after you have forgotten what
you learned there and your notes will
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sit there and you'll probably never go
back to looking at them. What's happening
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there is that we have not contextualized
the learning in our workflow. And Data
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Literacy is even day science is not
trivial skill. So think about even a
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even are more you know, it's
a more complex scale. And then you
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then you don't practice it. You
didn't bring it to to Somebody's workforce.
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You just send them to training and
you expect them, Oh, our team
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is our employees are data literate.
They are it's not going to happen.
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You need to bring that training in
the context of your workflow. So they
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need to do one thing that we
found to be successful is they need to
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do a project, follow on project
where they get to apply it right away.
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That's urgency. Is Very important because
it's these are complex concepts. Now,
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somebody who's never seen a history brown
versus by chat and you saying you
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know which information you're going to represent
which way. How are they going to
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connect the dot? It's too hard. So we have to immediately, and
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that's why we use a framework,
work based approach. We treat, we
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teach using bother, and the reason
we do that is because it's fairly complex.
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And so if I want to today, you know, start for you
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know, for example, its and
do you do? You Cook? I
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do, okay. Have you ever
made fullaful? I have not. Okay,
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so the question for you is,
if I ask you tomorrow, let's
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like, can we make fullawful tomorrow? How are you couldn't do it.
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I'm going to start with what are
the ingredients? So I make sure I
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have them exactly. So you're going
to look up some recipe, look in
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the within the recipe is going to
tell you the steps and going to tell
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you the ingredients. I'm fallawful because
it's for so much, so many of
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us is so out there. We
need a recipe. The same way for
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data science and and getting those data
science skills. We need a recipe and
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that's why we have the bothered framework. If you have the if you have
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the framework, if you have a
recipe and you you're taught step by step
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how to do this, how to
do this. Next step. How do
386
00:25:33.930 --> 00:25:37.799
you do next step? You have
something to now take it to your work
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flow. So now you have a
framework. Then we want you to apply
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it to a use case like,
okay, I'M A, I'm on marketing.
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I have this hundred ki to spend. This is what I was thinking
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about spending. Now that I have
this framework, let me see if I
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can do a better targeting, better
segmentation, and I can do that and
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I can see whether you know what
my now that I can apply it,
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and while I'm applying it, of
course, this is the first time I'm
394
00:26:00.470 --> 00:26:03.630
trying, you know, frying fullaffel. I don't even know the consistency of
395
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the balls that need to go into
the frying pan. Oh, I have
396
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a mentor, I have I have
somebody who has done this and they say,
397
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Oh, this is too liquidly,
we need to add more flower in
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00:26:11.900 --> 00:26:15.380
it, whatever else. Okay,
now somebody's guiding me, and that's what
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00:26:15.579 --> 00:26:19.369
mentor data signcement, and that's what
we do when we do data a little
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large, large enterprise wide data literacy. We have mentors either from our dreams
401
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or the train, the train of
trainers we have trained who go in and
402
00:26:30.289 --> 00:26:34.440
who are mentors on this framework applied
to their workflow. That but the end
403
00:26:34.640 --> 00:26:38.200
of that exercise that by the time
they've done with their stargeting in segmentation,
404
00:26:38.279 --> 00:26:41.440
that one project with me, take
them four weeks, maybe take them six
405
00:26:41.480 --> 00:26:47.000
weeks by the time they're done with
that. Now the framework is there.
406
00:26:47.390 --> 00:26:51.309
They know how to apply this framework. Maybe they'll not get everything from the
407
00:26:51.349 --> 00:26:53.589
class, but they'll know at least
they'll know how to take this framework and
408
00:26:53.630 --> 00:26:57.750
apply at least to this use case. Now you think about now. You've
409
00:26:57.789 --> 00:27:02.420
empowered many, many of these marketing
folks together. They're working many of the
410
00:27:02.500 --> 00:27:06.700
sales works. You have creating a
surround sound. Everybody's talking hypotheses, you
411
00:27:06.740 --> 00:27:10.940
know business question you talking the same
language. You start developing a culture,
412
00:27:10.940 --> 00:27:15.259
or at least your data little sees
up. But wait, it cannot if
413
00:27:15.299 --> 00:27:18.609
it starts there and your data.
Your leadership is still not data driven.
414
00:27:18.650 --> 00:27:22.569
They're still making decisions I respect to
your data. They're still not holding any
415
00:27:22.650 --> 00:27:26.690
their team accountable. Slowly they will
be no point for that data literacy right.
416
00:27:27.049 --> 00:27:30.720
So the data little did a different
leadership needs to come up as well.
417
00:27:30.319 --> 00:27:33.720
And then, lastly, they did
driven decision making process. And there
418
00:27:33.839 --> 00:27:37.119
isn't a process by which decisions are
being made, then the models data will
419
00:27:37.160 --> 00:27:41.640
be happening here and the decisions will
be happening in a parallel path and they
420
00:27:41.680 --> 00:27:45.470
have no connection and you're not going
to be optimizing your decisions based on data.
421
00:27:45.910 --> 00:27:48.190
So all data diffen decision making processes
also needs to come up. You
422
00:27:48.269 --> 00:27:52.029
know the you know the review.
The planning and the review process both need
423
00:27:52.109 --> 00:27:56.829
to have data in a systematic manner
that you can see. Hey, we
424
00:27:56.990 --> 00:28:00.619
planned on this. We were expecting
an autoway of Fenex. We actually got
425
00:28:00.700 --> 00:28:03.900
ADEX. What happened? What was
us? I'm Jamad does, I'm shows
426
00:28:03.900 --> 00:28:06.819
of we all. And how can
we use this information for next set of
427
00:28:06.900 --> 00:28:10.420
planning? And that's only possible when
you can have this kind of process.
428
00:28:10.700 --> 00:28:12.809
So I know I I gave it
a long, leveling answer here, but
429
00:28:14.089 --> 00:28:17.849
that's really good. I didn't want
to stab you because you're essentially answering one
430
00:28:17.849 --> 00:28:21.849
of the questions I had, which
is like this obviously needs to be cultural.
431
00:28:22.089 --> 00:28:25.049
It needs to be deeply baked in
who we are. This is how
432
00:28:25.089 --> 00:28:27.559
we do it around here. You
know, any cultural element is that way,
433
00:28:27.640 --> 00:28:32.240
and so one of my questions for
you is going to be how do
434
00:28:32.319 --> 00:28:37.160
we make sure that this isn't just
a project that those people over there are
435
00:28:37.319 --> 00:28:41.279
working on? And really you addressed, and I think that's what the For
436
00:28:41.430 --> 00:28:45.990
d's framework is really all about,
is again combined with that persona mapping doing
437
00:28:47.029 --> 00:28:49.309
the project work. I know that's
how I learned. Best is I'm gonna
438
00:28:49.309 --> 00:28:52.509
do all I'm going to do a
little bit of studying on my own,
439
00:28:52.869 --> 00:28:55.670
but I'm going to pair up with
someone who's done it once before, will
440
00:28:55.710 --> 00:28:57.819
do it together. Then I'll maybe
do it on my own and know that
441
00:28:57.900 --> 00:29:02.019
that person is within arm's reach if
I need to reach out for a question
442
00:29:02.180 --> 00:29:06.579
that I'm often running. And so
this idea of making sure people are doing
443
00:29:07.220 --> 00:29:11.730
projects relevant to their role, relevant
to the new learning right away and it's
444
00:29:11.769 --> 00:29:15.809
unique from what someone and another team
is doing in a different seat, makes
445
00:29:15.849 --> 00:29:18.849
so much sense. And so,
causs you're they're kind of two place I
446
00:29:18.930 --> 00:29:22.329
want ago. I guess I'll start
here. Where does this process go wrong?
447
00:29:22.450 --> 00:29:26.799
I think you already alluded to some
of them. is particularly around executive
448
00:29:26.839 --> 00:29:30.759
buying, but are there any other
kind of common hurdles. If you're getting
449
00:29:32.279 --> 00:29:37.480
some of these pieces in place,
what is prevented people from really making full
450
00:29:37.640 --> 00:29:41.509
cultural adaption? Yeah, and I
can talk about some of the most common
451
00:29:41.589 --> 00:29:45.309
mistakes and seeing o they're one thing
that people are doing. Like I said,
452
00:29:45.470 --> 00:29:48.430
you know, the LND leaders at
our leaders, sometimes the IDA teams
453
00:29:48.470 --> 00:29:55.180
are going out there and buying courses
and maybe sometimes university courses at local universities
454
00:29:55.500 --> 00:30:00.500
and local universities. I used to
teach I'm a UF, I'm graduate students,
455
00:30:00.579 --> 00:30:03.779
so I was teaching previously I was
teaching statistics there and then I was
456
00:30:03.859 --> 00:30:07.450
teaching calculus. So I know how
statistics is taught in for a senior level
457
00:30:07.609 --> 00:30:11.690
class, for a statistical statistics and
math major. I know how statistics is
458
00:30:11.769 --> 00:30:17.329
tart there also in the context of
business, that statistics doesn't you know from
459
00:30:17.369 --> 00:30:21.170
you you take the class and you
learn about averages and means and maybe some
460
00:30:21.329 --> 00:30:25.039
error and maybe built ship, govern
normal distribution, Goesian distribution, and then
461
00:30:25.079 --> 00:30:26.480
you come to the world and saying, Oh now you apply it to you
462
00:30:26.599 --> 00:30:30.799
know, your targeting and segmentation.
There such a gap between the two and
463
00:30:32.039 --> 00:30:36.549
I had to bridge that gap.
So I come from two masters my both
464
00:30:36.630 --> 00:30:40.150
of my thesis and will applied statists, mathematics. I have a I'm I
465
00:30:40.269 --> 00:30:44.430
was talking about Ai, you know, robotics and Ai Auto Ml. At
466
00:30:44.470 --> 00:30:45.750
that point, twenty years. I'm
beating myself, but two years ago we
467
00:30:45.829 --> 00:30:48.910
were working on that right. And
then I come to the real world and
468
00:30:49.619 --> 00:30:52.779
nothing that I had learned. I
mean I was doing nonlinear regression there.
469
00:30:52.819 --> 00:30:56.460
And then I come to this real
world and and I'm given this I joined
470
00:30:56.539 --> 00:31:00.619
robe and has given this problem of
you know, how do we better engage
471
00:31:00.619 --> 00:31:03.900
our customer? It doesn't come together, like I didn't know what to do.
472
00:31:03.099 --> 00:31:06.609
I have all the skills, like
I have all the skills, but
473
00:31:06.650 --> 00:31:11.049
I don't know how do I apply
it. And slowly and I failed miserably
474
00:31:11.210 --> 00:31:15.369
initially and and then over a period
of time I learned this process because I'm
475
00:31:15.369 --> 00:31:18.680
so impact driven. I was dissatisfied
with or just being able to do simple
476
00:31:18.799 --> 00:31:22.920
cross tabs or something. I'm like, I'm not using full utilizing the tool
477
00:31:22.960 --> 00:31:26.720
kit and as a result, I
developed this the framework by the framework came
478
00:31:26.759 --> 00:31:30.400
out of this journey that I had
myself of how do I take all of
479
00:31:30.519 --> 00:31:34.069
my quant background and then all of
this business context and I put it in
480
00:31:34.150 --> 00:31:37.470
the context and give it to people
in a recipe that they can do,
481
00:31:37.549 --> 00:31:40.509
because I'm at some point I got
really good enough, my taught team got
482
00:31:40.549 --> 00:31:42.509
really good and we became a sport
team for paypal and you all of that
483
00:31:42.630 --> 00:31:48.059
happened. And so I just basically
contest all of that learning into this fame
484
00:31:48.140 --> 00:31:51.859
world and thereby it makes it easy. You know, you give when you
485
00:31:51.940 --> 00:31:55.420
start teaching bad in the context.
Oh, here's a winery, how do
486
00:31:55.460 --> 00:31:59.819
you oper optimize their marketing spend?
Oh, here's a hospital, how do
487
00:31:59.859 --> 00:32:04.049
you optimize their outcome, disease outcome
and and discharge outcome and all of that.
488
00:32:04.730 --> 00:32:07.009
When you start teaching people in that
context and then you teach them,
489
00:32:07.089 --> 00:32:10.009
you can teach them average is at
statistical ers and all of that, but
490
00:32:10.089 --> 00:32:14.890
when you teach it in the context, that's when it connects. And I
491
00:32:15.009 --> 00:32:16.839
think too many people are going the
wrong way. One is they're doing it
492
00:32:17.079 --> 00:32:22.279
for all like this training mandatory for
all employees. That's waste of time.
493
00:32:22.680 --> 00:32:25.720
You're going to get what is it
called fatigue? Employee fatigue, because you
494
00:32:25.799 --> 00:32:29.880
know, too many too many hours
and and and they don't see their value.
495
00:32:30.000 --> 00:32:32.549
Not Everybody needs to be the right
level. So that's one error.
496
00:32:32.630 --> 00:32:36.750
That the one, one mistake,
and I see people doing one training for
497
00:32:36.789 --> 00:32:39.710
all. Said, will in the
in the cognitive and emotional energy again to
498
00:32:39.829 --> 00:32:43.990
cover that gap that you describe,
which is like, I get this uniform
499
00:32:44.069 --> 00:32:45.859
training, but how does that relate
me? I know that this is important
500
00:32:45.900 --> 00:32:49.099
because we all did it, but
I don't know what it means. It
501
00:32:49.180 --> 00:32:52.900
so was like all this wasted energy, yes, or the context, and
502
00:32:52.980 --> 00:32:55.339
then teaching them python. Does anybody, everybody, need to learn python?
503
00:32:55.339 --> 00:33:00.250
I mean Python is twenty percent of
your problems. Should require actually you learning
504
00:33:00.329 --> 00:33:02.970
to use python and and using,
you know, statistical methods, of machine
505
00:33:04.009 --> 00:33:07.890
learning methods. Why is everybody learning
python? Why is everybody learning sequel?
506
00:33:07.009 --> 00:33:10.009
So you're real like you know you're
investing, you're taking too much of your
507
00:33:10.490 --> 00:33:15.680
employees time and putting them through a
generic training and you're going to create frustration
508
00:33:15.720 --> 00:33:19.839
and is not going to be there's
going to be very little learning transfer rate
509
00:33:20.200 --> 00:33:23.440
because you're not teaching them in the
context and then whatever transfers, you going
510
00:33:23.440 --> 00:33:27.119
to lose it because you're not doing
use case after that. Right, so
511
00:33:27.230 --> 00:33:29.710
specifically in data and let you see, these are the things I'm going to
512
00:33:29.750 --> 00:33:32.150
talk about, which is these are
do not do, because even a fail
513
00:33:32.269 --> 00:33:36.630
and and you're going to come out
of six months later, you know you're
514
00:33:36.630 --> 00:33:38.190
going to say, Ay, I'm
and I the other last thing is they
515
00:33:38.230 --> 00:33:44.220
don't define success metrics. You go
in for a project and you ask the
516
00:33:44.220 --> 00:33:47.140
learning and transfer you know, learning
and development managers and hur managers. So
517
00:33:47.180 --> 00:33:50.900
how you want to measure the success
of this project? This works or not?
518
00:33:51.740 --> 00:33:53.940
Oh, that's a good question.
Like if you don't define what is
519
00:33:54.019 --> 00:33:58.650
success for you, that means you
don't know six months later, nine months
520
00:33:58.650 --> 00:34:01.130
later, you successful or not.
You just have some surveys that people are
521
00:34:01.170 --> 00:34:05.049
filled out where their voice or it
was a waste of time or whatever else.
522
00:34:05.369 --> 00:34:07.929
But how would you know what worked
or not? Right? So I
523
00:34:08.010 --> 00:34:10.639
don't know, I'm there's a lot
to cover here, but I'm giving you
524
00:34:10.760 --> 00:34:15.639
know some of the top things.
Don't do this. Think about your strategy
525
00:34:15.800 --> 00:34:19.679
first. Think about your data lit
to see goal. Think about that persona
526
00:34:19.800 --> 00:34:22.480
mapping and strategize it. Who needs
to be at what they will spend that
527
00:34:22.679 --> 00:34:27.590
front and time. The other thing
is it cannot be done without executive buying,
528
00:34:27.670 --> 00:34:30.469
and we again get too many of
these requests. Oh we will do
529
00:34:30.550 --> 00:34:32.989
this deal see how, but we
don't need to, and I reeve our
530
00:34:34.030 --> 00:34:36.710
first step is voice of executive say, Oh, what's it is a good
531
00:34:36.869 --> 00:34:38.300
we don't need to talk to executives. Why do we need to waste their
532
00:34:38.340 --> 00:34:45.780
time? You can't change a culture
of the organization without executives putting in their
533
00:34:45.860 --> 00:34:50.139
skin in the game right. It's
not possible. You're talking about changing the
534
00:34:50.300 --> 00:34:54.170
culture and that's shifting the mind.
Who is a marketing manager to look up?
535
00:34:54.210 --> 00:34:57.650
If the if, the CEO and
the leadership, the CMO, is
536
00:34:57.690 --> 00:35:01.010
still making decisions right of the Bab, the seat of the pants, why
537
00:35:01.050 --> 00:35:04.570
are they going to be later driven? Why are they going to pick up
538
00:35:04.570 --> 00:35:07.840
these skills right? So I think
that's another big failure. I see people
539
00:35:07.880 --> 00:35:12.440
think, oh, it's a siloid
approach. I let's just fix this team.
540
00:35:12.519 --> 00:35:15.719
The team is not functional. Let's
fix this steam. You can fix
541
00:35:15.800 --> 00:35:19.840
the theme all you like, but
unless you fix it systemically, you're not
542
00:35:19.960 --> 00:35:23.429
going to be able to drive culture
or data. So good another drive into
543
00:35:23.590 --> 00:35:29.150
making it a cultural transition, not
just a project orientation. So you've talked
544
00:35:29.150 --> 00:35:31.309
about the data drive. An executive, you talked a little bit about the
545
00:35:31.349 --> 00:35:36.579
data enthusiast. I can imagine what
the data skeptic is, but I'm really
546
00:35:36.699 --> 00:35:40.179
curious to hear more about the citizen
analyst. What are the characteristics of a
547
00:35:40.260 --> 00:35:45.500
cities in analyst in what are maybe
a couple roles in an organization that it's
548
00:35:45.699 --> 00:35:49.659
that that maps to? Yeah,
that's great question. So, going back
549
00:35:49.659 --> 00:35:52.170
to my eighty twenty eight percent of
the problem, you and I, as
550
00:35:52.250 --> 00:35:59.329
individual professionals, should be able to
use simple analysis and do it in Excel,
551
00:36:00.050 --> 00:36:01.929
Excel, excel, or whatever tool
of choice you have, MICROSOFTC cell,
552
00:36:01.929 --> 00:36:05.440
everybody has, but know whatever tool
of choice you have at hand,
553
00:36:06.039 --> 00:36:09.880
which means eighty percent of the problem
in marketing, in product in customer support
554
00:36:10.079 --> 00:36:15.119
is solvable using simple man aggregate analysis, correlation analysis, sizing and Tren analysis.
555
00:36:15.800 --> 00:36:20.389
Those simple methodologies are FM, for
example, for segmentation, recent AFQUENCY,
556
00:36:20.429 --> 00:36:23.789
montory. Simple stuff can be can
solve eighty percent of a problem.
557
00:36:23.989 --> 00:36:29.309
So, citizen analyst, they're too
like so marketing managers. Problem Managers,
558
00:36:29.349 --> 00:36:32.619
typically marketing folks, and the product
folks, not product folks, need to
559
00:36:34.019 --> 00:36:37.500
to do this simple analysis and those
so they need like these basic skills to
560
00:36:37.619 --> 00:36:43.179
be able to utilize excel, for
exampleized set and graph and chart and the
561
00:36:43.300 --> 00:36:45.539
systematic again, bother frame. Will
we call this bother framework? But hands
562
00:36:45.579 --> 00:36:50.409
on business analytics. They need hands
on business atic skills for that. For
563
00:36:50.690 --> 00:36:54.050
Marketing folks they need a little bit
more. They need a be testing as
564
00:36:54.130 --> 00:36:58.969
well, to be able to do
control design of control experiments that if I
565
00:36:59.010 --> 00:37:01.920
did this processes, if I change
the shade of this button from pink to
566
00:37:02.320 --> 00:37:06.519
read, what happens, and a
lot of those things. So they're two,
567
00:37:07.360 --> 00:37:10.400
not two personas that we see,
data educated and citizen analysts that we
568
00:37:10.519 --> 00:37:15.599
use typically in the organization. Sometimes
they are typically in an organization. Their
569
00:37:15.599 --> 00:37:19.389
six to eight person us. Sometimes
there is need for more and we introduced
570
00:37:19.550 --> 00:37:23.829
more personas. But typically for you
know, marketing, product sales person they
571
00:37:23.869 --> 00:37:30.380
fall somewhere between data literate, data
educated and citizen analyst, depending again on
572
00:37:30.460 --> 00:37:34.860
the outcome. Sometimes sales people,
sales professionals. They need to be able
573
00:37:34.900 --> 00:37:38.340
to utilize the output from a DNA
team, but not necessarily do some things
574
00:37:38.500 --> 00:37:42.739
hands on. They need to be
able to understand it because they their literacy
575
00:37:42.739 --> 00:37:45.570
level need to be data needs to
be Deda literate right. So there's again
576
00:37:45.610 --> 00:37:52.250
again these are not set in stone
definitions. They vary by organization because the
577
00:37:52.369 --> 00:37:57.409
organization's goal is different. For some
organization a product manager needs to be able
578
00:37:57.449 --> 00:38:01.679
to pull data using sequel and do
real time testing and so on. They
579
00:38:01.800 --> 00:38:06.559
they need a higher skill for that
level, right, higher data skill.
580
00:38:06.719 --> 00:38:09.719
So it varies, but this is
kind of roughly what it fits. Really
581
00:38:09.760 --> 00:38:14.670
good. Thank you so much for
that. I'm glad I ask that before
582
00:38:14.710 --> 00:38:17.829
we go to a couple kind of
closing elements. Any closing thoughts for you
583
00:38:17.949 --> 00:38:22.949
here? Any hopes, your expectations
for a better data future? Like you
584
00:38:22.030 --> 00:38:25.150
know, if you want to impart
one, you know, big message,
585
00:38:25.469 --> 00:38:30.260
to put a button on this really
interesting and wide ranging. I wish I
586
00:38:30.340 --> 00:38:34.900
wasn't so ambitious with how much we
wanted to learn or how much I wanted
587
00:38:34.900 --> 00:38:37.179
to learn with my time with you, but you know, anything you want
588
00:38:37.179 --> 00:38:43.329
to add here? My last thing
I want to tell your audience is this
589
00:38:43.570 --> 00:38:46.250
is a perfect time to learn.
You know, one word, two one,
590
00:38:46.369 --> 00:38:50.329
two words, one one stone.
Kind of situation. Right now and
591
00:38:50.369 --> 00:38:53.050
again we are according in early Aprils
the pandemic and me and post pandemic.
592
00:38:53.170 --> 00:38:57.000
This kind of chaos, this news, the depressing news, and all of
593
00:38:57.079 --> 00:39:00.519
that is going to continue and it's
a time of transition. Our minds are
594
00:39:00.559 --> 00:39:02.119
transitioning. When I get up every
morning I don't know what's happened in the
595
00:39:02.199 --> 00:39:07.119
world like this, it's the world
is transitioning. That means our mind is
596
00:39:07.760 --> 00:39:12.110
in the perfect place to learn.
So if you are yourself, an individual
597
00:39:12.150 --> 00:39:15.909
wanting to invest in yourself, learn
the skills. If you are a leader
598
00:39:15.389 --> 00:39:19.630
and you want your team, you
know, you invest in your team to
599
00:39:19.750 --> 00:39:23.780
learn because you know post pandemic,
it's a situation where you can't do without
600
00:39:23.780 --> 00:39:27.619
data and these skills are going to
you know, if you invest in your
601
00:39:27.619 --> 00:39:30.179
team right now, you're going to
get a hundred folds, because all each
602
00:39:30.219 --> 00:39:34.300
of an every of your decision go
to be optimized for in future. And
603
00:39:34.380 --> 00:39:37.420
you want that because you know the
margins are gone for two thousand and twenty
604
00:39:37.420 --> 00:39:40.409
at least right and and so for
as an individual, I would also say
605
00:39:42.010 --> 00:39:45.090
invest time in learning, because you
know that will. I heard from some
606
00:39:45.210 --> 00:39:50.969
podcasts. You know that fires new
synaptic neuron connections and it actually makes your
607
00:39:51.090 --> 00:39:55.719
brain grow and is actually towards a
better, wholesome you versus you know,
608
00:39:55.800 --> 00:40:00.239
the news that we have been bombarded
with it's just it's really depressing. So
609
00:40:00.280 --> 00:40:04.119
I would say right now is a
perfect time. Right now, I'm continuing
610
00:40:04.199 --> 00:40:07.550
on for attorney. Is Not an
easy year. Perfect time to learn if
611
00:40:07.590 --> 00:40:09.989
you are yourself a sales and marketting
individual wanting to invest in time, in
612
00:40:10.070 --> 00:40:13.909
yours, your time in it.
So learning these skills is going to be
613
00:40:13.989 --> 00:40:16.389
very, very useful. And also, if you are a leader, invest
614
00:40:16.630 --> 00:40:20.710
in in data literacy, invest in
data culture for your organization. It's going
615
00:40:20.750 --> 00:40:23.539
to serve you really well so that
you know you're going to try as a
616
00:40:23.619 --> 00:40:29.900
company and serve your customers better.
Wonderful for folks like again, I feel
617
00:40:29.940 --> 00:40:32.659
like I was overly ambitious and what
I wanted to do here. If anyone
618
00:40:32.699 --> 00:40:37.050
wants to go back, we're doing
full transcripts, we're doing video clips,
619
00:40:37.090 --> 00:40:42.889
we're doing summaries at bombombcom slash podcast
and when you visit that page, not
620
00:40:43.010 --> 00:40:45.329
only can you see the most recent
episodes, including this one, but you
621
00:40:45.369 --> 00:40:51.199
might also enjoy episode thirty six with
Sarah Toms from the warten school at pen.
622
00:40:51.559 --> 00:40:54.360
We talked about the financial side of
CX, which customers should you invest
623
00:40:54.599 --> 00:41:00.000
in and using LTV and other data
to figure out where to invest in an
624
00:41:00.039 --> 00:41:04.670
episode sixty five. More recently with
Chris Hicken, we talked about product usages
625
00:41:04.710 --> 00:41:07.510
of vanity metric and we talked about
a number of metrics and how different teams
626
00:41:07.550 --> 00:41:12.550
within the organization can use them.
So before I let you go, I
627
00:41:12.590 --> 00:41:15.670
would love to give you the chance, because relationships are our number one core
628
00:41:15.750 --> 00:41:19.659
value, to think or mention someone
who's had a positive impact on your life
629
00:41:19.699 --> 00:41:22.699
or your career. Even we I
am listening a lot these days, like
630
00:41:22.860 --> 00:41:27.059
again, so bombarded with news and
not so good. I'm listening a lot
631
00:41:27.420 --> 00:41:32.809
to neuroscientists, potentials, autern reality. I'm think I'm going completely in different
632
00:41:32.849 --> 00:41:37.130
direction these days, because that helps
me see bigger, bigger. I can
633
00:41:37.170 --> 00:41:39.650
zoom out of this situation and see
a being, a bigger self. So
634
00:41:39.690 --> 00:41:45.769
I'm listening a lot to Dr Jode
Dispenser these days and this whole aspect of
635
00:41:45.889 --> 00:41:51.000
we create on reality and Abraham Hicks, and I really love the empowerment that
636
00:41:51.199 --> 00:41:53.800
comes from that. You can change
your bread. Energy makes matter. I
637
00:41:53.960 --> 00:41:59.440
love this concept and we, if
we you know from from mindset and we
638
00:41:59.519 --> 00:42:01.670
know. We know this equal doing
square, but I don't know how it
639
00:42:01.869 --> 00:42:07.150
translates to my world and I'm loving
this idea about energy, math makes matter
640
00:42:07.349 --> 00:42:12.309
and how I can energetically change the
vibration of what I am thinking, of
641
00:42:12.429 --> 00:42:16.179
what I'm doing and actually manifest matter. So I'm loving that and ass having
642
00:42:16.219 --> 00:42:20.420
a huge, insignificant impact for me. And then you know, obviously,
643
00:42:20.460 --> 00:42:23.300
all the all the good folks who
came before that and open my mind.
644
00:42:23.699 --> 00:42:27.739
I'm a lifetime learners. I love
learning, and so I'm learning from a
645
00:42:27.820 --> 00:42:32.250
new scientist, your your new scientists
right now. Awesome. And you already
646
00:42:32.289 --> 00:42:38.769
mentioned Amazon in the context of data
and customer experiences. There another company you
647
00:42:38.849 --> 00:42:43.929
maybe really appreciate or respect for the
way that they deliver an experience for you
648
00:42:44.050 --> 00:42:49.880
as a customer. But I can
probably name quite a few. But I
649
00:42:50.119 --> 00:42:57.400
love the new new aid, new
startup companies who are all about I met
650
00:42:57.639 --> 00:43:01.070
you know, must have experiences for
their for their client, and so I'm
651
00:43:01.150 --> 00:43:06.230
a customer for third love it sees
one of our clients, and this this
652
00:43:06.429 --> 00:43:08.389
aspect of there, and then many, many, many others, but this
653
00:43:08.590 --> 00:43:14.420
aspect of being able to make things
easy for their customers, to be able
654
00:43:14.420 --> 00:43:19.139
to give them choices, to empower
them and to sell them seamlessly, not
655
00:43:19.300 --> 00:43:22.619
overtly, but you know, it
sort of, I don't know, opposite
656
00:43:22.619 --> 00:43:25.179
of world, but you know,
basically, make it seemless, make it
657
00:43:25.300 --> 00:43:29.050
easy, and I'm all. I'm
a I'm a convenience and easy lover.
658
00:43:29.610 --> 00:43:32.650
And so all these new startups and
experiences, you know, like there are
659
00:43:32.730 --> 00:43:38.010
lots of startups about like giving science
kids. They're making science kids for my
660
00:43:38.090 --> 00:43:43.079
child and you know, all of
those I love. I love these companies
661
00:43:43.119 --> 00:43:46.320
who make seamless experiences. That does
it for me. You know, then
662
00:43:46.360 --> 00:43:50.079
it's not a question of how much
money I'm going to pate, so question
663
00:43:50.159 --> 00:43:52.840
of Oh, I got the experience. And now also, I'm looking at
664
00:43:53.079 --> 00:43:58.750
these days. I'm also very I'm
very attractive to positive news coming out.
665
00:43:59.110 --> 00:44:04.389
So the companies are investing in the
employees and companies that are making I mean
666
00:44:04.389 --> 00:44:07.269
I'm a big fan of Bill Gates
and like the kind of investment is doing
667
00:44:07.309 --> 00:44:13.460
in vaccines. And you know how
facebook is using its platform to spend spread
668
00:44:13.820 --> 00:44:17.860
positive information about covid and I love
these, these things that people whould so
669
00:44:17.940 --> 00:44:22.619
much such a big network and such
big influence and doing such high value work
670
00:44:23.059 --> 00:44:27.369
for the humanity. So I love
these. I don't know, I roundered
671
00:44:27.409 --> 00:44:30.329
on again, but no, that's
really good. I'm never going to stop
672
00:44:30.409 --> 00:44:34.289
somebody who's talking about the positive.
Pianka. This has been awesome. If
673
00:44:34.369 --> 00:44:37.489
someone wants to follow up with you, where they want to learn more about
674
00:44:37.530 --> 00:44:38.880
a ring like, where would you
send people if they want to follow up
675
00:44:38.920 --> 00:44:44.119
on this conversation? They can go
to airdingcom and from there they can reach
676
00:44:44.199 --> 00:44:46.719
us, reach out and there's a
lot of lot of resources there people can
677
00:44:46.800 --> 00:44:53.110
download and and case studies and you
know are we share exhibitantly. So we
678
00:44:53.190 --> 00:44:55.789
have lots of lots of cases.
We write a lot, we talked a
679
00:44:55.869 --> 00:44:59.550
lot, so download stuff from there. If you do want to reach out,
680
00:44:59.989 --> 00:45:01.829
you can use the form there.
You can also follow me on Linkedin
681
00:45:01.869 --> 00:45:07.110
or connect with me on Linkedin,
and you can also follow me on twitter.
682
00:45:07.619 --> 00:45:10.820
My handle is analytics creen, analytics
screen perfect. I love it.
683
00:45:12.300 --> 00:45:15.139
This has been so good. I
appreciate your time so much. I hope
684
00:45:15.179 --> 00:45:20.820
you enjoy the butterfly hit with your
daughter and I will share all of these
685
00:45:20.900 --> 00:45:23.849
links at Bombbcom podcast. Thank you
so mudy. Then, there was a
686
00:45:23.889 --> 00:45:30.170
pleasure talking to you and I really
enjoyed conversation. Thank you. I hope
687
00:45:30.170 --> 00:45:35.610
you enjoyed that conversation with Pianca Jane, President and CEO of a ring hey.
688
00:45:35.690 --> 00:45:40.559
If you want more episodes like this
one, you can visit Bombombcom podcast.
689
00:45:40.639 --> 00:45:47.639
It's bomb Bombcomla podcast, or you
can search the customer experience podcast in
690
00:45:47.840 --> 00:45:52.869
your preferred podcast player. Again,
my name is Ethan Butt. I host
691
00:45:52.989 --> 00:45:57.789
this series and I welcome your feedback. Email me, Ethan etch an at
692
00:45:57.829 --> 00:46:00.150
bombombcom. What would you like to
see more of? What would you like
693
00:46:00.230 --> 00:46:05.500
to see less of? which episodes
or conversations really stood out to you?
694
00:46:06.059 --> 00:46:09.860
You can also share that feedback via
linkedin. Hit me up Ethan Bu last
695
00:46:09.860 --> 00:46:15.340
name is spelled beute. I'm pretty
sure I'm still the only one there and
696
00:46:15.460 --> 00:46:19.610
I welcome again your thoughts, your
feedback, anything you have to share about
697
00:46:19.690 --> 00:46:25.929
the CX series here on BTB growth. Thanks for listening. Is Your buyer
698
00:46:25.969 --> 00:46:30.650
a betb marketer? If so,
you should think about sponsoring this podcast.
699
00:46:30.010 --> 00:46:35.000
BB growth gets downloaded over a hundred
and thirty thousand times each month and our
700
00:46:35.079 --> 00:46:38.320
listeners are marketing decision makers. If
it sounds interesting, Sind Logan and email
701
00:46:38.519 --> 00:46:40.559
logan at sweetfish Mediacom.