Episode 4

Episode 4: Bill Franks, KSU

Published on: 29th March, 2022

About Bill:

Bill Franks is the Director of the Center for Statistics and Analytical Research at Kennesaw State University.  He is also an accomplished author in the data science space. He joins Guy to discuss the ethics of data science, the issues with analytic models, and how data informs decision-making to drive ROI.

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Video of Interview

Transcript
Guy Powell:

Hi, I'm Guy Powell and welcome to the March episode

Guy Powell:

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please visit pro relevant.com and sign up for all of these

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pro relevant comm marketing machine dot pro relevant.com.

Guy Powell:

Today we'll be speaking with Bill Franks. He's the director

Guy Powell:

of the Center for statistics and analytical research at Kennesaw

Guy Powell:

State University. Bill is also the author of the new book

Guy Powell:

winning the room. And as well as taming the big data, tidal wave

Guy Powell:

the analytics revolution and 97 things about ethics. Everyone in

Guy Powell:

data sciences should know his work has spanned clients in a

Guy Powell:

variety of industries. For companies ranging in size from

Guy Powell:

Fortune 100 to small nonprofit organizations. You can learn

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more at Build dash Frank's dot com Bill dash Frank's dot com.

Guy Powell:

Welcome, Bill.

Bill Franks:

Thanks for having me, Guy.

Guy Powell:

Yeah, glad to have you here. So while you're at

Guy Powell:

KSU, let's talk a little bit about KSU. I've always been

Guy Powell:

impressed with the, with what they're doing there. And so tell

Guy Powell:

us a little bit about that before we get started.

Bill Franks:

Yeah, KSU. It's part of the University System of

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Georgia, probably obviously not as well known outside the

Bill Franks:

Atlanta area, as would be University of Georgia or Georgia

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Tech. But it's it's rapidly growing. We're up over 40,000

Bill Franks:

students now we've got a, you know, full range of degrees.

Bill Franks:

We're adding more PhD programs each year, it seems so I think

Bill Franks:

over the coming years, more and more people are going to hear

Bill Franks:

about about KSU

Guy Powell:

Yeah, absolutely. And, you know, I think that, you

Guy Powell:

know, certainly Georgia Tech, and University of Georgia, but

Guy Powell:

Georgia State and the whole Georgia university system has

Guy Powell:

really, really taken off and, and KSU, I think is a big piece

Guy Powell:

of that. Yeah, so anyway, let's talk about data sciences. That's

Guy Powell:

kind of what you're in my fields are with some overlap on the

Guy Powell:

marketing side. So. So data sciences seems to be growing

Guy Powell:

faster than Data Science Talent, and which leads to a lot of open

Guy Powell:

positions. So what do you see as how KSU fits in with closing

Guy Powell:

that talent gap?

Bill Franks:

I mean, well, first of all, I think that the

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interesting thing to me is that the talent gap is so big, I

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don't even think universities all combined are necessarily yet

Bill Franks:

cranking out enough new talent to really fill all of that I

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think we have, we have work to do as a as a society to

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encourage more people to get into these fields. But I think

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one of the things that we're doing specifically here is we're

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really focused on having some very applied programs. So even

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with our Ph D program, we look to get each student at least a

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couple of years working with a corporate client. That's

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actually the role of the center that I oversee that events begin

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the Center for statistics and research, it partners with big

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corporations, and they fund projects that we do joint

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research with them on. So we do projects with companies like you

Bill Franks:

know, traveler's insurance and Home Depot, Georgia Pacific. And

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these are real problems. So we're really trying to get our

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students versed in what's actually happening with data and

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data science in a business environment. And particularly as

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we get on to the master's level, and so forth, we have capstone

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courses, we support a variety of internships and so forth. So we

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were trying to put out students that have a knowledge of how to

Bill Franks:

apply what they're learning rather than just the theory. And

Bill Franks:

to me, that's part of the gap we've had historically, as too

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many, too many programs historically, put out people who

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were technically highly proficient. But and I'll put

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myself in this bucket when I came out of school, I was very

Bill Franks:

technically proficient, I didn't have any real practical

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experience in how to apply any of that in a real world setting.

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And I had to learn that all on my own, which, of course, is

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time and money for the company that does hires you initially,

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to get you up to speed. So I think that the more that

Bill Franks:

universities can and have started to do like KSU and focus

Bill Franks:

on not just the academic components, so I have a degree

Bill Franks:

in STEM field, but the applied nature of that, I think it's,

Bill Franks:

it's going to be beneficial.

Guy Powell:

Yeah, absolutely. I've been working with the Emory

Guy Powell:

consulting team in it well, business consulting, but usually

Guy Powell:

I get paired up with the marketing side of things and and

Guy Powell:

I think it makes a huge difference. And you can just

Guy Powell:

tell that those students that have had that practical

Guy Powell:

background, and have seen in the real business problem and how

Guy Powell:

the true data datasets work, and what the challenges are in the

Guy Powell:

real world, that makes a huge difference. And, and if you're a

Guy Powell:

hiring manager, that's really what you want to look for is you

Guy Powell:

want to look for somebody that's, that has some kind of an

Guy Powell:

understanding of what the real challenges are going to be in

Guy Powell:

terms of being able to actually apply those technical skills

Guy Powell:

that they've learned. A lot I can tell you, I, you know,

Bill Franks:

I'm personally overseeing one of the corporate

Bill Franks:

projects with some master students, and I was early on the

Bill Franks:

the status call with that client right before I joined you today.

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And you know, they wanted us to change direction on one thing

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and do something slightly different. So you know, I'm

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having a conversation with the students, you realize pretty

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much all the code you've been building to create these

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variables is still fine. You have to do it as of a different

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date, right? They no longer care about when the person initially

Bill Franks:

registered, they care about when they initially bought all of

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your logics, the same with the exception that you have to

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identify what did the customer look like, at a different point

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in time? You know, I made the point that this is a common

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thing. And you know, this market, right? The as of date is

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something that I mean, you do it again, and again, and again. And

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it's ubiquitous in any in any analytic it and in a lot of

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different application areas. But it again, it's not something

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that necessarily gets taught per se, right? It's it for the

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students, it was new, oh, doesn't this change everything?

Bill Franks:

Well, no, you're just going to grab the customer record from a

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different point in time. And it's really easy to identify the

Bill Franks:

proper record based on when it you know, when what the dates

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related to it are. But at the same time, while it's very easy

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to make that change, if you don't make that change, you

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jeopardize all of the analytics, because you have the totally

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wrong view of what that customer look like. So these are the

Bill Franks:

little practical things that I think it's honestly, without

Bill Franks:

getting your hands dirty. With a real project, it's almost hard

Bill Franks:

to, you can't really want to list every little thing that you

Bill Franks:

ever need to know, a lot of it you're gonna learn, but the more

Bill Franks:

that you can get students to see these things, while they're in

Bill Franks:

school, the easier it'll be for them to extend those when they

Bill Franks:

get their, you know, their real job.

Guy Powell:

Yeah, absolutely. And I think, you know, one of

Guy Powell:

the biggest challenges is, we're on the marketing analytics side.

Guy Powell:

So kind of a subset of what you guys are, what your folks are

Guy Powell:

doing. But the hardest challenge is really specifying what that

Guy Powell:

business question is that you're trying to answer. And then

Guy Powell:

really understanding that as you peel the layers back to say,

Guy Powell:

Okay, well, this is the business question, but you know, is it

Guy Powell:

this field, so to speak, you know, into your into your point,

Guy Powell:

or is it some other field, and then working through each of

Guy Powell:

those things as you really kick off that project? And, and, you

Guy Powell:

know, the problem is, you know, garbage in garbage out, if you

Guy Powell:

are using the wrong data set, totally, then then what you're

Guy Powell:

doing is worthless. If you're off a little bit, then you're

Guy Powell:

not really providing that little bit of extra value to that

Guy Powell:

business question that can really make the the value

Guy Powell:

overall to the company, really shine.

Bill Franks:

Yeah, you know, what, it's little things like,

Bill Franks:

back to your point of the definition, why it's so

Bill Franks:

critical. I had a recent scenario where, you know, you

Bill Franks:

would people would register with this company's website. Now,

Bill Franks:

register sounds pretty simple. There's a registration date.

Bill Franks:

Okay, great. Any analysis about registration, we go off the

Bill Franks:

registration date. In deeper conversation with the business,

Bill Franks:

it ends up that there's different types of registration

Bill Franks:

that aren't necessarily flagged as a type. But, you know, they

Bill Franks:

work with corporate partners. So some corporate partners will

Bill Franks:

quote, automatically register everybody at a soft register,

Bill Franks:

just give the name and the basic contact so that all someone has

Bill Franks:

to do is define a password, but they'd still have that

Bill Franks:

registration date is then so you get into Okay, well, when you

Bill Franks:

care about when someone registered, what exactly do you

Bill Franks:

mean, now? Is it when they showed up in the system,

Bill Franks:

including if their company just did the soft registration? Or is

Bill Franks:

it when they actually came in personally typed in information

Bill Franks:

and confirmed that they wanted to make use of that

Bill Franks:

registration? And again, there's not a right or wrong answer here

Bill Franks:

necessarily, but depending on the question, you know, the

Bill Franks:

question that they want to answer, it could make a huge

Bill Franks:

difference in the actual analytics that we do. And so

Bill Franks:

that was one I thought was interesting, because it sounds

Bill Franks:

so easy. Oh, there's registration? A, we're good to

Bill Franks:

go? Well, no, Yo, you always have to dig in. Does it mean

Bill Franks:

what we're assuming it means? And in this case, the answer was

Bill Franks:

no, not always does not always mean, you assume.

Guy Powell:

Yeah, exactly. And that kind of gets into maybe a

Guy Powell:

little bit of the the next question here is, so the

Guy Powell:

question is, are the roles that support data science efforts

Guy Powell:

changing? And it almost seems like what there's there's two

Guy Powell:

separate roles that we just talked about? One is defining

Guy Powell:

the business question. And then there has to be somebody that

Guy Powell:

peels the layers of the onions back to make sure that we get to

Guy Powell:

the truth, root question that we're really trying to answer.

Bill Franks:

Yeah, well, it's interesting. I mean, I've got

Bill Franks:

like entire keynote on this topic and written a bunch about

Bill Franks:

it as well. What's interesting is if you go back to when I

Bill Franks:

first got into the family business, and I think yourself

Bill Franks:

as well, you know, in the old days, I mean, if there was one

Bill Franks:

person, I would do have to do everything. I had to get the

Bill Franks:

data. I had to prepare the data. I had to model the data. I had

Bill Franks:

to work with the business folks. on the front end on the backend

Bill Franks:

all of that. Now granted, a lot of what we were doing wasn't

Bill Franks:

nearly as sophisticated automated, etc. But that's how

Bill Franks:

it was done. And what's happening in recent years now,

Bill Franks:

because Analytics has become not just more ubiquitous, but it's

Bill Franks:

becoming more embedded in operational processes. And it's

Bill Franks:

getting scaled. That rolls getting split, you now have

Bill Franks:

people loosely called translators who focus almost

Bill Franks:

exclusively on these conversations we just talked

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about on the front end, what what exactly do you mean? And

Bill Franks:

what do you need to solve and making sure they understand that

Bill Franks:

from the business side, and then translating that to requirements

Bill Franks:

for the technical coders, and on the back end, those same people

Bill Franks:

would help position the results to the to the client. But now

Bill Franks:

you have a you hear about data engineering as a thing, right?

Bill Franks:

Well, data has gotten so messy these days, with all the

Bill Franks:

different formats and all the different locations. Now in many

Bill Franks:

cases, you have a whole discipline around just getting

Bill Franks:

that data together so that someone can model it. On top of

Bill Franks:

that, because of the deployment, than being so much more scaled,

Bill Franks:

you now have people these ops roles, ml ops, you know, for

Bill Franks:

machine learning ops, or AI ops, there's DevOps in general and

Bill Franks:

software, where these are roles are really systems people with a

Bill Franks:

focus on analytics who are doing classic system optimization, how

Bill Franks:

do we get the processing capacity allocated properly? How

Bill Franks:

do we make sure the data is going through the network

Bill Franks:

properly, you know, very technical, deep network stuff,

Bill Franks:

but focused exclusively on the analytical type processing. So

Bill Franks:

there's this a variety of roles now, where as you look across

Bill Franks:

time in an analytical process, the steps where we're, you know,

Bill Franks:

specializing within each of those phases, and sometimes even

Bill Franks:

within a phase, so within data science itself, there's no you

Bill Franks:

know, there are still generalist data scientists, and we still

Bill Franks:

need them. I liken that to a general practitioner doctor,

Bill Franks:

they can do the triage and figure out the general

Bill Franks:

direction. But now you've got people specializing in nothing

Bill Franks:

but language processing, nothing but image recognition, nothing

Bill Franks:

but you know, classic risk analytics in a banking context.

Bill Franks:

And, and it's because they become so complicated, and so

Bill Franks:

sophisticated, each of those areas that you can't just dabble

Bill Franks:

in anywhere, if you're going to be effective, and you're going

Bill Franks:

to deliver the quality that's required, you've got to be a

Bill Franks:

specialist much like doctors have. So I think it's both

Bill Franks:

across and within these disciplines. There's just a lot

Bill Franks:

of specialization these days, which, back to the talent

Bill Franks:

crunch. So now, you know, we need everybody that we used to

Bill Franks:

need plus a bunch more in each of those roles. So yeah,

Guy Powell:

no, absolutely. And I was on a call with a friend of

Guy Powell:

mine. And what they do is, we specialize kind of in the mass

Guy Powell:

media and the online media space. And they're specializing

Guy Powell:

more or less in kind of the CRM in the sales space. Both of them

Guy Powell:

have overlapping components. But there's certainly a difference

Guy Powell:

in the, in the analog and analytics knowledge that you

Guy Powell:

need, in order to be able to really, really get that last

Guy Powell:

extra little percent out of the out of the models, and and out

Guy Powell:

of and then really be able to answer those business questions.

Guy Powell:

And so I think you're right, when you said, you know, you've

Guy Powell:

got, you know, for each kind of a role, you have sub roles that

Guy Powell:

that have to deal with different pieces of it. I

Bill Franks:

think that's absolutely right. And you hit on

Bill Franks:

something really important about the different skill levels as

Bill Franks:

well. I mean, this is the core theme of my of my book, Damocles

Bill Franks:

revolution was about this embedding and automating of

Bill Franks:

analytics and operational context where, you know, think

Bill Franks:

about a website making millions and millions of decisions a day

Bill Franks:

in milliseconds of what you know, what are the products,

Bill Franks:

it's going to try and cross sell you. And those kinds of

Bill Franks:

scenarios, you know, you can't afford to have really expensive

Bill Franks:

people spending a whole lot of time on each individual model,

Bill Franks:

you have what I call, I kind of call commodity models, you need

Bill Franks:

an automated process that will build a pretty decent model

Bill Franks:

pretty quickly, where there's some automated checks. And if it

Bill Franks:

passes them, it just goes live, right. And there's people

Bill Franks:

monitoring the overall pool of these than looking for any that

Bill Franks:

are going to miss to to yank them out, potentially. But you

Bill Franks:

can't you can't be building bespoke super fancy models for

Bill Franks:

every possible application high value ones. Absolutely. But

Bill Franks:

marketing's an example where a lot of things just have to be

Bill Franks:

automated. And you can make the argument well, you know, we

Bill Franks:

could get another another, you know, relative percenter to lift

Bill Franks:

out of this, if we really put some data scientists on it for a

Bill Franks:

few months, well, true. But you've got 200 of the models.

Bill Franks:

And to put those data scientists on that for those months on each

Bill Franks:

of those is going to end up being timely and cost

Bill Franks:

prohibitive. And it's just not practical. So embrace what

Bill Franks:

you're capturing with your somewhat automated models, as

Bill Franks:

long as they're working well, and take down you know, the 10

Bill Franks:

of the 11% theoretical max that you're actually able able to get

Bill Franks:

I think that's been a mind a mind shift, both within the

Bill Franks:

business community and the data science community. Early in my

Bill Franks:

career, I would have had a heart attack at the thought of I'm

Bill Franks:

leaving one out of 11% on the table. Now I'm like, look if we

Bill Franks:

can take down 10% In a matter of a couple of weeks and move on to

Bill Franks:

another problem to get the first 10%, that's actually a lot more

Bill Franks:

value than spending the extra month to get the extra 1% On the

Bill Franks:

first problem. And so that's back to the scale, the scale

Bill Franks:

changes the equation, when there's only five problems to

Bill Franks:

focus on, you're going to take them all down to the, to the

Bill Franks:

ultimate level of detail, but we no longer have that now we have

Bill Franks:

dozens or hundreds or 1000s, depending on the size of the

Bill Franks:

company, and you've got to get in, get a good value and move

Bill Franks:

on.

Guy Powell:

Yeah, absolutely. And that, we call that the

Guy Powell:

minimum viable product, so to speak. And, you know, and if you

Guy Powell:

can get 80% of the value out of, you know, let's say, you know, a

Guy Powell:

handful of weeks work versus a handful of months or a handful

Guy Powell:

of years, you're much better doing that. And then just like

Guy Powell:

you said, move on to the next big one. And then, you know,

Guy Powell:

capture all the big ones, and then you can reprioritize and go

Guy Powell:

to the next level. The other challenge I have is, as well, as

Guy Powell:

you know, I don't know, maybe it was five years ago, maybe

Guy Powell:

longer, really, before data analytics and data sciences kind

Guy Powell:

of took off, you'd have these spreadsheets and you know, you'd

Guy Powell:

build a spreadsheet, and then you'd add a little bit, you'd

Guy Powell:

add a little bit. And all of a sudden, that spreadsheet is a

Guy Powell:

monster, and it has so much in it that it absolutely has errors

Guy Powell:

in it that you will never find. And so if you were even to build

Guy Powell:

some very complex data analytic solution, you don't know if you

Guy Powell:

have an error in it, because you, you know, it gets so big,

Guy Powell:

that you might actually, you know, you're you're still adding

Guy Powell:

value, but you may actually be doing some things wrong in

Guy Powell:

certain areas, because it's just too big to see and have a, you

Guy Powell:

know, a good view of actually how all of the thing puts

Guy Powell:

together, it comes together to really solve them, you know, the

Guy Powell:

the business challenges that it's trying to solve? You

Bill Franks:

know, it's intro, that's a great comment. And, you

Bill Franks:

know, I find a lot of people get hung up on that, oh, my gosh,

Bill Franks:

you know, we made an error in this decision here and that

Bill Franks:

decision there. And that's the whole point of statistics and

Bill Franks:

probabilities about odds, right? It's the casino business, right?

Bill Franks:

At the end of the day, when I build a model, no matter how

Bill Franks:

good it is, what we're saying is, we think we, on average, can

Bill Franks:

can make the correct prediction as to say, Who will buy, you

Bill Franks:

know, 70% of the time, whatever it is. But what that means is

Bill Franks:

30% of the time, we're wrong. And we could be wrong, because

Bill Franks:

someone just changed their behavior unexpectedly, it could

Bill Franks:

have been a data error could have been our model wasn't

Bill Franks:

capturing something that it should have captured. But it

Bill Franks:

doesn't matter. The point is, on average, are you capturing about

Bill Franks:

70% of those who respond? And if so, you know, that's phenomenal.

Bill Franks:

But you can't let the exceptions you know, obviously, you can't

Bill Franks:

let the exceptions drive the rules. And, you know, real world

Bill Franks:

example that I used to hammer this idea home is, you know, we

Bill Franks:

should be looking at on average, does this model work better than

Bill Franks:

not having the model and as long as it's not doing anything

Bill Franks:

harmful, you know, on the mistakes making, it's a good

Bill Franks:

thing. So autonomous vehicles are out there. And we still are

Bill Franks:

in this world now, where anytime an autonomous vehicle hits or

Bill Franks:

kills a person anywhere in the world, it makes international

Bill Franks:

headlines. And everyone calls for either completely stopping

Bill Franks:

autonomous vehicle creation or regulating it further and

Bill Franks:

further. And my point is, that's the wrong way to look at it. You

Bill Franks:

got to look at for every 100,000 miles driven, what's the what's

Bill Franks:

the accident and death rate of autonomous vehicles and people,

Bill Franks:

as long as the cars are worse, obviously, then we want to be

Bill Franks:

very cautious. But we're going to get to a day where the

Bill Franks:

autonomous vehicles are, say, 1/10 1/20 of people. But that

Bill Franks:

doesn't mean they'll be error free, there will still be cases

Bill Franks:

where an autonomous vehicle will wreck where most reasonable

Bill Franks:

people would because of when usual later, whatever the case

Bill Franks:

is, you have to look at that trade off of Well, that's

Bill Franks:

unfortunate this accident happened here. And we know how

Bill Franks:

to get the autonomous code to work better. But we're saving,

Bill Franks:

you know, 10 accidents per 100,000 miles driven over people

Bill Franks:

who would have made these other accidents happen that the car

Bill Franks:

wouldn't have. The problem is you never see the examples. That

Bill Franks:

didn't happen, because someone wasn't driving the car. But it

Bill Franks:

applies very much here in analytics in the modeling as

Bill Franks:

well. You could always go find those cases where somebody got

Bill Franks:

the wrong offer, or was given the wrong diagnosis, prediction,

Bill Franks:

etc, etc. But on average, overall, are you much better

Bill Franks:

than you were before you had any models at all?

Guy Powell:

Yeah, absolutely. And it's funny, my brother just

Guy Powell:

sent me a video of a Tesla was in an accident, and the battery

Guy Powell:

exploded and they were out of the car all over the place. And

Guy Powell:

and I I responded back, yeah, but don't guess tanks explode.

Guy Powell:

And so you know, is one it's this, it's exactly the same

Guy Powell:

thing. You know, here you have something that, you know, okay,

Guy Powell:

so batteries, you know, are saving energy, you are more eco

Guy Powell:

friendly or whatever. And, and, and yet, you know that one case

Guy Powell:

makes the news, whereas all of the other cases don't make the

Guy Powell:

news. I've done. We did some consulting for loss prevention.

Guy Powell:

And one of the challenges with with loss prevention is you

Guy Powell:

can't prove how much loss you prevented, because it didn't

Guy Powell:

happen and You know, and so to your point as well, is that how

Guy Powell:

many accidents don't happen because you have all this

Guy Powell:

automation in the car or the automated driver or whatever it

Guy Powell:

is? And yes, absolutely, there's going to be one or two failures

Guy Powell:

where the the light, like you said the lighting isn't right.

Guy Powell:

So yeah, I agree with that. 100%. So

Bill Franks:

it's funny when people are uncomfortable with

Bill Franks:

this conversation we're having, I always point out that while

Bill Franks:

it's it's a dirty secret, it's how all public policy is built.

Bill Franks:

So for example, car mandates for airbags, we didn't used to have

Bill Franks:

to have airbags, because they were way too expensive

Bill Franks:

initially. And they effectively that both the government and

Bill Franks:

manufacturers of all types effectively do a, you know, life

Bill Franks:

saved, or injuries saved per dollar of cost. And at some

Bill Franks:

point, it's too high $1 per cost, and even the federal

Bill Franks:

government Yep, Nope, we're not going to mandate that because

Bill Franks:

it's too costly per life saved. But they know that that that

Bill Franks:

there would have been life saving if the airbase had been

Bill Franks:

there. So you have to have, you have to have some of that

Bill Franks:

rational trade off. And it is uncomfortable. But at some

Bill Franks:

point, I mean, if we wanted a car that would never wreck, we'd

Bill Franks:

all be driving five miles an hour in a tank, and paying you

Bill Franks:

know, and having to fill our fill our tanks with 100 gallons

Bill Franks:

of gas every 100 miles, but nobody, nobody would want that

Bill Franks:

even though objectively, it's far safer, right? And so, you,

Bill Franks:

you you accept that risk? Well, I'm going to go on the

Bill Franks:

interstate, it's convenient to go 70 miles an hour to get home.

Bill Franks:

But at the same time, if I get in a wreck at 70 miles an hour

Bill Franks:

I'm in, I'm in deep trouble. It's a it's a it's a big, you

Bill Franks:

know, there's a big risk point there. And so I think that's

Bill Franks:

what the models, and I'll do, it goes on behind the scenes in our

Bill Franks:

lives a lot more that I think that many people think about it,

Bill Franks:

and people don't don't don't realize, when you buy life

Bill Franks:

insurance, you're basically it's a bet, you're betting you're

Bill Franks:

gonna die so that you make money on it. And there are companies

Bill Franks:

betting that you won't, so they can keep your money. But it's

Bill Franks:

literally that computation, your rates are set on the

Bill Franks:

probability, they think that you'll die before that policy

Bill Franks:

expires. And when you buy it that you know, in effect, you're

Bill Franks:

you're betting on your death, because the only time the

Bill Franks:

insurance psychologically poor estate planning, it's a safety

Bill Franks:

net, but I'm saying from a money and math perspective, you win if

Bill Franks:

you die to collect more than the premiums that you put it.

Guy Powell:

Yeah, absolutely. And you know that COVID is a

Guy Powell:

prime example of, you know, how do you get to perfect so that

Guy Powell:

there's no spread of the disease with with masks or no mask

Guy Powell:

vaccine mandate or no mandate, and it's kind of a very similar

Guy Powell:

trade off. In some things, you know, you can never get a get to

Guy Powell:

100%. Perfect. And that's where then public policy comes in. You

Guy Powell:

know, oh, but speaking of public policy, then let's switch over

Guy Powell:

to Data privacy, because data privacy is now becoming part of

Guy Powell:

public policy, and certainly with the GDPR and the CCPA. And

Guy Powell:

in California, and what have you, the the value of your your

Guy Powell:

personal data is, is now coming into the public policy domain.

Guy Powell:

And so when you think about that, and especially now as we

Guy Powell:

move to the web three Oh, and Metaverse, and, and what have

Guy Powell:

you, there's a lot of ethical issues, and that marketers and

Guy Powell:

data scientists need to consider, let's talk a little

Guy Powell:

bit about that.

Unknown:

I mean, that's a it's a it's a big issue. And you know,

Unknown:

you mentioned the getting the book 97 things about ethics,

Unknown:

everyone data science, you know, focus on this was actually a

Unknown:

compilation of blog link submissions from, as the title

Unknown:

suggests, 97 different submitters, on various aspects

Unknown:

of it. And the reality is, you know, I still I've been in this

Unknown:

business for a long time. And I like to say, I've become

Unknown:

somewhat of a privacy freak, mainly because honestly, what I

Unknown:

do, it's like, I'm on the inside seeing what's happening. And

Unknown:

it's not always what's happening. But what could

Unknown:

happen, that bothers me more, right, I'll see the data company

Unknown:

has, and I'll know what the time they're not doing something I'm

Unknown:

uncomfortable with. But I also can identify 10 things they

Unknown:

could do with that data that I'd be incredibly uncomfortable

Unknown:

with. And the only thing stopping them from doing it is

Unknown:

their own sense of, Well, this would be illegal, unethical, or

Unknown:

otherwise, get us in trouble with the media. And then there's

Unknown:

companies that push those limits all the time. So I think this is

Unknown:

a topic that's going to be evergreen for a while. I mean,

Unknown:

when you when you look today, in the marketing space, and the

Unknown:

talk about getting rid of cookies, well, you know, people

Unknown:

on the one hand are cheering that this is great. Now, these

Unknown:

cookies, they can't track me the same way. But you know, they

Unknown:

postpone that once or twice because they're getting their

Unknown:

work arounds. And you hear about, you know, browser

Unknown:

fingerprinting, which is, you know, a way as I understand it,

Unknown:

you know, this might not be perfect, but your browser itself

Unknown:

reports information. When you request a web page, it'll tell

Unknown:

the browser version, it'll say what ad ends you have, etc, etc.

Unknown:

They might know things about your IP address. The point is,

Unknown:

there's ways to almost uniquely identify people just based on

Unknown:

the web request that they put in. That isn't isn't unique

Unknown:

because there's a cookie now it's even worse. It's your

Unknown:

computer and browser combination is uniquely stamping you in a

Unknown:

way that can be identified. I don't think most people realize

Unknown:

that that's possible. And the law is in the in the policies

Unknown:

around what's fair and unfair. With with the browser

Unknown:

fingerprinting, I don't think are fully developed yet either.

Unknown:

So everywhere we go, we're going to keep having this and you've

Unknown:

mentioned you know, things like this new this whole idea of

Unknown:

metaverse. Now, you know, it's one thing when everything that

Unknown:

you type or click is getting tracked. But now you get into a

Unknown:

Metaverse concept. And let's say you're literally having a 3d

Unknown:

avatar interaction with somebody. And you know, I

Unknown:

jokingly slap you in the 3d world. Now, is that is that

Unknown:

count the same as if I slapped you in the real world? Right? Am

Unknown:

I going to get in? Am I going to get in trouble for this? Am I

Unknown:

now violent? Am I now? You know, a person to suspicion because I

Unknown:

slapped your avatar with my avatar? I mean, I don't know.

Unknown:

But the point is that it that data is going to be captured and

Unknown:

much like people getting in trouble now for things that were

Unknown:

harmless 10 years ago and getting in big trouble because

Unknown:

it surfaces that they had done or said something that was

Unknown:

perfectly acceptable at the time of it now is it? Maybe today

Unknown:

it's a big joke, let's all go in and start slapping each other's

Unknown:

avatars and 10 years from now, that's considered equally bad as

Unknown:

an assault. No, go back, go. Look at Bill. He was slapping

Unknown:

and assaulting people in the metaverse in the early days left

Unknown:

and right. And get cancer. So you know, what is the policy on

Unknown:

that? What what what data should be captured? And? And how long

Unknown:

should it be kept? I mean, it's just it's a never ending

Unknown:

question. So I guess answer your question. I'm concerned about

Unknown:

the lat still about the lack of general focus by the average

Unknown:

person on this, I think most people just blow, they would

Unknown:

say, Oh, well, you guys are just old curmudgeons what do we care?

Unknown:

Who cares if they have my data, I'm not doing anything wrong.

Unknown:

And you know, yet, you hear about, you know, college

Unknown:

recruits losing a scholarship. And in Division One thing

Unknown:

because of one thing, they said one time on social media that

Unknown:

might not have even met, what it read as if it was in context.

Unknown:

And so I say, you know, it's all fun and games until you're the

Unknown:

person who gets burned, because your data has been captured and

Unknown:

analyzed in ways you didn't want it to. So I'd rather have the

Unknown:

transparent at least make sure I know how it's doing at least

Unknown:

make people acknowledge what's being done. And if you choose to

Unknown:

turn over all your data willingly, okay, that's your

Unknown:

choice. I just want it to be transparent, where people are

Unknown:

being made aware of exactly. In plain English, not these 80 page

Unknown:

documents in plain English, here's what we're going to

Unknown:

collect and do with your data.

Guy Powell:

Yeah, and that is, that's very difficult. And to

Guy Powell:

your point, as well, i The problem with public policy, or,

Guy Powell:

you know, regulation or whatever. Is that, okay? So now,

Guy Powell:

you know, the metaverse is kind of starting to take off, it's

Guy Powell:

been around a while with Second Life and a couple of other ones.

Guy Powell:

But now it seems like you know, might actually be taken off. And

Guy Powell:

the problem that public policy has to even regulate it in some

Guy Powell:

fashion is that they're always going to be five or 10 years

Guy Powell:

behind, by the time they finally figure out but there's a policy

Guy Powell:

that needs to be be in place, like no slapping on your first

Guy Powell:

date or something like that. Then it you know, the metaverse

Guy Powell:

has already moved on. And then there's some other kind of

Guy Powell:

adverse out there. So you know, it's it's very challenging, but

Guy Powell:

I did like your breakdown, you know, there's there's things

Guy Powell:

that are illegal or criminal, illegal or criminal, then

Guy Powell:

there's things that are ethical, or not ethical, and then there's

Guy Powell:

things that the media might get hold up, I kind of like that

Guy Powell:

breakdown, because that is exactly where companies and

Guy Powell:

their data and their policies and their internal actions

Guy Powell:

really have to take, you know, really have to play. And, you

Guy Powell:

know, the biggest one that I can think of is the Volkswagen

Guy Powell:

diesel gate, where they were manipulating the engines during

Guy Powell:

the, you know, the EPA tests to pass, so you'd get a good pass.

Guy Powell:

And then once that was done, they'd go back and, and then you

Guy Powell:

know, and then run the motor differently. And, and, you know,

Guy Powell:

clearly unethical, certainly bad for, for the media, I don't know

Guy Powell:

if it was illegal probably was illegal. But you know, those

Guy Powell:

things that culture within the organization is what drives that

Guy Powell:

use of the of the data. And in this case, then the use of you

Guy Powell:

know, that algorithm that's built into the computer that's

Guy Powell:

controlling the the diesel engine. Now in that hierarchy, I

Bill Franks:

always say in an ideal world, all three of those

Bill Franks:

would be lined up perfectly. But what's legal is probably the

Bill Franks:

loosest because to your point, things just haven't caught up,

Bill Franks:

right? There are things that are that that are not illegal today,

Bill Franks:

not because 90% of people wouldn't say that should be

Bill Franks:

illegal, just hasn't yet been recognized as a possibility to

Bill Franks:

be made illegal, then what's ethical, I think is tighter than

Bill Franks:

what's legal, certainly. But even what's ethical, you know,

Bill Franks:

one of the things I talk about ethics all the time is it's not

Bill Franks:

as cut and dried as you think So even something that you're

Bill Franks:

convinced is ethical. There's nothing that 100% of people are

Bill Franks:

going to agree is ethical. And so depending you do something

Bill Franks:

that seems perfectly ethical, and that the majority of people

Bill Franks:

might agree is ethical or whatever. 30 year customers

Bill Franks:

think it was totally unethical and they're the ones running to

Bill Franks:

the papers and doing the boy caught you're going to feel some

Bill Franks:

pain. So you've got to really think about all three of those

Bill Franks:

and that's where the I think the we sit today for the most part.

Bill Franks:

It's the culture and the individuals and the company.

Bill Franks:

policies that guide this, and there are companies I tend to

Bill Franks:

trust. And there are some large tech companies I don't trust as

Bill Franks:

far as I can see who I who I, you know, I believe skirt up to

Bill Franks:

and maybe over the ethical and legal lines on a regular basis

Bill Franks:

wherever they can that that's good for them and that, you

Bill Franks:

know, that's life. But I back to it. I wish I wish people would

Bill Franks:

be more aware. And frankly, I remember a shocking statistic I

Bill Franks:

saw was for all the data you get, let's I think it was

Bill Franks:

Facebook was this example for all the data you're giving them

Bill Franks:

to get the free service, the amount of ad revenue they made

Bill Franks:

off the average person use either per month or per year was

Bill Franks:

like five or $6, something like that. Probably per year, given

Bill Franks:

how big the revenues are, the point is, I would happily pay $5

Bill Franks:

a month to have a a some of the services I have that are free if

Bill Franks:

they'd be kept completely private. So it's one thing if

Bill Franks:

the if someone said, well, to get all your benefits of

Bill Franks:

Facebook is going to cost you $3,000 a year, a lot of people

Bill Franks:

might say, Well, I'm not going to pay 3000 I guess I'll just

Bill Franks:

have to give up my data. But you tell people you might pay around

Bill Franks:

5080 $100 a year and then Facebook won't have collect or

Bill Franks:

use any of that data. I think a lot of people go oh, geez, yeah.

Bill Franks:

If I'm giving up all that, and it only cost me that much. I'll

Bill Franks:

do it. And no, that's an alternative revenue model. What

Bill Franks:

is honestly, I don't see why Facebook would care if I'm going

Bill Franks:

to pay them their same ad dollars they make off me

Bill Franks:

otherwise, to keep my data private. That should be as well,

Bill Franks:

I'd love to see some companies actually go down.

Guy Powell:

Yeah. And I like your the way you're, you know,

Guy Powell:

connecting the privacy issue with what the value of that is

Guy Powell:

to the individual. And not that I think it's kind of a way to

Guy Powell:

look at a lot of these privacy rules and say, Yeah, I paid I

Guy Powell:

pay $5, or no, you know, I don't care if they take my data. And I

Guy Powell:

hate to say it. My wife hates it when I do this, but I'm a

Guy Powell:

marketer. And so I leave my cookies on and I leave a lot of

Guy Powell:

stuff on because I want to see how the marketers use it. And so

Guy Powell:

we just bought a new car. And so we were going out to the sites

Guy Powell:

Hyundai and Kia, Toyota and Ford. And it was fascinating to

Guy Powell:

see how quickly the manufacturers then took

Guy Powell:

advantage of that, and started showing us ads on our smart TV.

Guy Powell:

And it was Hyundai that was the winner. Now GM was a little bit

Guy Powell:

behind. But they were Neverland, us less their key of Ford and I

Guy Powell:

can't remember who else we looked at didn't do anything.

Guy Powell:

But Hyundai within a day, within 24 hours, we're using my

Guy Powell:

information and starting to show me ads based on the websites

Guy Powell:

that we have visited. Wow. Yeah. Yeah. So fascinating. It kind of

Guy Powell:

also kind of leads into the next question here, because one of

Guy Powell:

the challenges that you also have, and then some data sets

Guy Powell:

are siloed, because of the regulations around them. And,

Guy Powell:

and I remember going to a presentation, our this is maybe

Guy Powell:

10 years ago, and he was the chief data officer, I think for

Guy Powell:

the state of Georgia. And he said, Well, you know, what might

Guy Powell:

be available to you in this data sources not available to you

Guy Powell:

from that data source. So you'd have the Department of Labor

Guy Powell:

would say no, no, you can't have that data. And one of the other

Guy Powell:

departments would say, yeah, you can have this data. And in

Guy Powell:

particular, then you had the crossovers where you had

Guy Powell:

specially corrections officers, you didn't want that data to get

Guy Powell:

out in any one of the databases. So you had a database over here,

Guy Powell:

let's say was the Department of Labor, they had to have a flag

Guy Powell:

on that data that said, Nope, you can't allow any corrections

Guy Powell:

officers data to get, you know, sold or given out to the public

Guy Powell:

for whatever reason. And you know, just kind of fascinating.

Guy Powell:

And that then leads, though, kind of to not only data silos

Guy Powell:

for regulatory purposes, but also data silos for managerial

Guy Powell:

or power or political purposes within an organization. So maybe

Guy Powell:

talk a little bit about how you see that happening, and how, you

Guy Powell:

know, maybe data sciences can break those silos down, maybe

Guy Powell:

can't break them down, or maybe can break them down and in maybe

Guy Powell:

some kind of a white room or

Bill Franks:

whatever. You know, this is one of these things

Bill Franks:

where I think most people would agree that in an ideal world,

Bill Franks:

you just have all your data together. Right? And I think

Bill Franks:

we'd all start with that premise. But then there's always

Bill Franks:

the reasons why you have to break them, and you just raise

Bill Franks:

you, right? Oh, well, we have to have it all together. Except

Bill Franks:

here's this piece that for law reason we can't have together

Bill Franks:

now you just created a silo albeit for a good reason. Well,

Bill Franks:

you know, we've got to give this data to our partner over here,

Bill Franks:

but they're not allowed to access our actual system, nor

Bill Franks:

can they see some of the data. So we need to make an extract of

Bill Franks:

the data. That's just the data they can see and then put it

Bill Franks:

over in this other spot where they have access to and so

Bill Franks:

forth. And so I think the reality is that the reality

Bill Franks:

unfortunately, is that there's a lot of valid reasons why you end

Bill Franks:

up with silence, unfortunately, more than you like, but then

Bill Franks:

people pile on invalid reasons just because hey, you know what,

Bill Franks:

I'm annoyed that you're not getting the data back. So I'm

Bill Franks:

going to create my own database over here and pull all the data

Bill Franks:

for my own purpose, that's not necessarily a good, you know,

Bill Franks:

good reason. But it's going to be there. In fact, it's funny,

Bill Franks:

my blog last month was about, you know, you know, barrier to

Bill Franks:

scale boy analytics is the same one, it's been around for a long

Bill Franks:

time, when you have data in different places, if you need to

Bill Franks:

analyze that data jointly, you have to bring that data together

Bill Franks:

physically at the time of analysis. And if they're small

Bill Franks:

files, Excel spreadsheets, who cares, right doesn't matter. But

Bill Franks:

if you've got terabytes over here, and terabytes over there,

Bill Franks:

even today, that's still an expensive and or time consuming

Bill Franks:

process, right. So even on public cloud, you can pay for a

Bill Franks:

huge amount of capacity to suck that data over as fast as

Bill Franks:

possible. But you're going to pay a really big price for that

Bill Franks:

much capacity. Or you can pay for a more typical reasonable

Bill Franks:

amount of capacity. And it's going to take a really long time

Bill Franks:

and still run up a pretty big bill. So I think that the

Bill Franks:

challenge is to be aware of that you'll have to have some silos,

Bill Franks:

and then just be very diligent in trying to keep them to the

Bill Franks:

absolute minimum. And to the extent that there's huge large

Bill Franks:

data repositories that are going to be joined together

Bill Franks:

frequently, you're going to, you know, as much as you can get

Bill Franks:

them as close together, if not in the same, you know, overall

Bill Franks:

platform as possible.

Guy Powell:

Yeah, no, that makes a lot of sense. And I think

Guy Powell:

you're right there, data silos are going to be there. And, you

Guy Powell:

know, sometimes you can, if you get full access to it, or you

Guy Powell:

can do a full join to it. Or maybe you need anonymized

Guy Powell:

access, where maybe you get anonymized access, but you can

Guy Powell:

get a couple of fields with real detail. I think that that's one

Guy Powell:

of the and maybe that gets back to the new roles for data

Guy Powell:

scientists, one of those roles for data sciences is, is that

Guy Powell:

data governance piece to be able to make sure that the data is

Guy Powell:

properly, legally, ethically and media, I guess, being used so

Guy Powell:

that it's not being abused or misused in some fashion?

Guy Powell:

Absolutely. Yeah. That's a big lot of big companies spending a

Guy Powell:

lot of time on the on the governance and oversight of the

Guy Powell:

data itself in it, and it's useless. Yeah, yeah, absolutely.

Guy Powell:

Alright, so now I have a future question for you. Yeah, when I

Guy Powell:

was growing up, and I don't know about you, but my first

Guy Powell:

programming language was basic. And then I moved to Fortran. And

Guy Powell:

then it moved into Pascal, I think, and then it was, then,

Guy Powell:

you know, there were a handful of other ones. And then there

Guy Powell:

was C and C++ and C++ and C sharp, and then all of a sudden

Guy Powell:

programming moved into things like R now for especially for

Guy Powell:

analytics, and, and Python. And so it seems like Python is now

Guy Powell:

supplanted R. So what do you see is kind of the future of these

Guy Powell:

languages for for analysts?

Bill Franks:

Well, it's interest. So I guess, I'd be

Bill Franks:

hesitant to project because there, there could be language

Bill Franks:

10 out there that that you didn't mention that suddenly

Bill Franks:

going to take the world by storm, I mean, even, even not

Bill Franks:

too many years ago, it looked like art was gonna conquer it.

Bill Franks:

And then Python kind of came out of nowhere, at least in the

Bill Franks:

analytic space. But I'll tell you, in my mind, it doesn't

Bill Franks:

matter so much from this room. This is where I tell I tell this

Bill Franks:

to I mean, students particularly talk about this a lot. But even

Bill Franks:

professors will come go, Alright, Bill, know what

Bill Franks:

language should I know? Right? If I, if I want to get out there

Bill Franks:

and get the best job, should I know are? Should I know SQL?

Bill Franks:

Should I know? Python? What should I know? And I always say,

Bill Franks:

You know what, what you need to know is know how to program. You

Bill Franks:

need to know the logic and how to develop how to first define

Bill Franks:

analytic logic and translate it into code and do it well. And I

Bill Franks:

said, if you know one language really well, and you can show me

Bill Franks:

you can translate complicated analytical logic in that

Bill Franks:

language. I have utter confidence, because like you

Bill Franks:

just mentioned, you and I have translated in multiple languages

Bill Franks:

over the years, you can try, you can transfer that much like

Bill Franks:

speaking English. If I wanted to learn French, it's painful. But

Bill Franks:

I know exactly what I need to say. I just have to figure out

Bill Franks:

how do I say it in French, that's different than when you

Bill Franks:

were a baby. And you had to learn what language was and what

Bill Franks:

a word was and what a sentence is. And when you first learn

Bill Franks:

coding, it's a little bit like that you have to learn the

Bill Franks:

entire concept of coding incredibly, incredibly difficult

Bill Franks:

at first, but once you know how to code and you know, one coding

Bill Franks:

language to train, it's just a matter of translation. So I

Bill Franks:

always tell people, instead of trying to get a little

Bill Franks:

certificate in seven languages to claim you know that, but you

Bill Franks:

know about that D, show that you really know how to code. Well,

Bill Franks:

if you know how to code well, and I'm using a language in my

Bill Franks:

company, that's not the one that you know, I know that within a

Bill Franks:

couple months, you'll you'll pick it up in particular,

Bill Franks:

because your peers will already know that language and be able

Bill Franks:

to help tutor you along you're not going to be on your own. So

Bill Franks:

to me, it's it's really about the underlying logic in the

Bill Franks:

code. It's not even about the coding

Guy Powell:

language. Yeah, no, fair enough. And actually, you

Guy Powell:

know, good point. I don't know, when I was, in my programming

Guy Powell:

classes, I think they were trying to teach us kind of the

Guy Powell:

principles of coding. And, you know, so you kind of get that

Guy Powell:

structure. And, you know, I'm wondering if that's kind of the

Guy Powell:

same thing. Now, as you know, you need to go deep in at least

Guy Powell:

one so maybe it's our maybe a to Python, but then you also need

Guy Powell:

to kind of understand the overall theory because at some

Guy Powell:

point, Python is going to be supplanted by something else,

Guy Powell:

whatever that is. And you're going to have to be able to

Guy Powell:

translate your thinking and your methodology from Oh, you know, I

Guy Powell:

used to do it this way in Python, but now I need to do it

Guy Powell:

slightly differently, hopefully better in some new language.

Bill Franks:

Yeah. And you hit on it, too. It's I remember I've

Bill Franks:

learned basic first as well. I taught myself basic initially.

Bill Franks:

And I don't get too old. Yeah, well, and but the thing is, if

Bill Franks:

you look at it, if you go back and look at old basic code, to

Bill Franks:

be honest with you, a lot of the constructs are still present in

Bill Franks:

all these languages today. There's if and then there's, you

Bill Franks:

know, there's loops. I mean, is it a Do Loop a while loop or a

Bill Franks:

for loop? You know, I don't know, it depends on the

Bill Franks:

language. But what do they all do? They're all going to churn

Bill Franks:

through from one to 10. Right. So yeah, I think it's a to me

Bill Franks:

that I like your idea of even the foundation of coding when I

Bill Franks:

like students on the projects in the project classes, which I

Bill Franks:

tell them I say before you start coding, if you just go and start

Bill Franks:

coding, you're going to screw it all up, I promise you, before

Bill Franks:

you start coding, and there's sometimes teams where different

Bill Franks:

people on the team know different languages, right? I

Bill Franks:

said, What do I call pseudocode? map it out on the on the

Bill Franks:

chalkboard? What do you got to get to, and then you can split

Bill Franks:

up who's gonna do what piece, let them use whatever language

Bill Franks:

but you want to write down that logic and be convinced you have

Bill Franks:

the logic laid out before you code. Because once you start

Bill Franks:

coding, now you're locked into trying to fit it within what you

Bill Franks:

know in that language. And you're going to end up doing

Bill Franks:

things maybe you that weren't optimal for the problem back to

Bill Franks:

the very first point for the business problem. It's not

Bill Franks:

optimal, but it's how you know how to code in your specific

Bill Franks:

coding environment. So you do it. That's not optimal layout

Bill Franks:

what you need, and then figure out a way to do

Guy Powell:

Yeah, exactly. And so, so true. So true, that

Guy Powell:

definition of that business question at the top, and really

Guy Powell:

peeling back the layers on that. And then, and from a coding

Guy Powell:

perspective, is really outlining what your code, you know, the

Guy Powell:

big blocks of your code are going to do. And then you know,

Guy Powell:

and then going off and doing the code makes makes so much sense.

Guy Powell:

The thing I hated about coding was those those off by one

Guy Powell:

errors, I was always I always had that in there. I never could

Guy Powell:

get around them. But anyway, we're about out of time. Is

Guy Powell:

there one other thing that you'd like to talk about? Or maybe you

Guy Powell:

know, what is the future of data sciences, and then we'll close

Guy Powell:

up?

Bill Franks:

Well finish, I got to do a completely shameless

Bill Franks:

plug, guy, I'll just follow. Just this week, my new newest

Bill Franks:

book came out, you mentioned the game, Winning the Room: Creating

Bill Franks:

and Delivering an Effective Data-driven Presentation. And

Bill Franks:

what this is all about is, you know, over the years, I learned

Bill Franks:

a lot of hard lessons myself, but probably some of the most

Bill Franks:

painful meetings I've ever been in have been technical people

Bill Franks:

presenting data to typically non technical audiences, sometimes

Bill Franks:

even other technical audiences. And it goes horribly wrong,

Bill Franks:

because they they can't put it in terms people understand

Bill Franks:

they're over complicating it, it's to detail all of these

Bill Franks:

things. And so I tried to distill this down the book, it's

Bill Franks:

a little different. There's books on storytelling, there's

Bill Franks:

books on analytics, there's books on visualization, this

Bill Franks:

book is about a live presentation. Imagine you're in

Bill Franks:

front of a room, putting up a PowerPoint, what do you have to

Bill Franks:

do to make that be successful? So there's elements of

Bill Franks:

storytelling elements of visualization and such, but it's

Bill Franks:

really focused on that, how do you distill it down to a live

Bill Franks:

presentation, to a often non technical audience. And, to me,

Bill Franks:

as we continue to have all of these analytics, it's still as

Bill Franks:

marvelous as much as a problem as ever before. And you know, I

Bill Franks:

was on a call earlier this week, with a company I'm advising

Bill Franks:

wherein their client was concerned that this model they

Bill Franks:

had been delivered wasn't what they needed, they couldn't

Bill Franks:

understand it. And they weren't able to work with the data

Bill Franks:

scientist involved, to have that person help them understand, you

Bill Franks:

know, age old problem. And so the net result is maybe the

Bill Franks:

model was perfect, and maybe it was horrible. But it doesn't

Bill Franks:

matter if the clients not even understanding it, and they can't

Bill Franks:

be explained. And so I think that's where this this this,

Bill Franks:

this book was, was built. As I started teaching here at the

Bill Franks:

university, realizing, often because they hadn't had any

Bill Franks:

lessons, it's how bad the students initial presentations

Bill Franks:

were often but then how fast they improved was coaching. And

Bill Franks:

I see I got to put, I got to put some of this in a book. And it's

Bill Franks:

119 tips, just a minute or two each 140 illustrations, a lot of

Bill Franks:

the tips of illustrations to kind of show here's right,

Bill Franks:

here's wrong, very easy to digest. But I think, you know, I

Bill Franks:

think people will get it because as we move forward into the

Bill Franks:

future, it's going to be as important as ever able to

Bill Franks:

communicate these these analytics and these data driven

Bill Franks:

decisions and so forth. Yeah, effectively.

Guy Powell:

Absolutely. No, I'm looking forward to getting it.

Guy Powell:

And so tell us where can we buy it?

Bill Franks:

Everywhere where books are sold, it's I know,

Bill Franks:

it's up on Amazon. It's up on Amazon, Barnes and Noble, the

Bill Franks:

Wiley site directly. So it should should, at least online,

Bill Franks:

it should be available bookstores. I don't know, I, you

Bill Franks:

know, bookstores have their own method of choosing what books to

Bill Franks:

make it in. I'm sure you could order it online at any

Bill Franks:

bookstore, even if they didn't have it in stock in a local

Bill Franks:

store.

Guy Powell:

Alright, so why don't you show that again, it's

Guy Powell:

called Winning the Room. And what's the subtitle,

Bill Franks:

Creating and Delivering an Effective

Bill Franks:

Data-driven Presentation.

Guy Powell:

Fantastic. And I, you know, I've done data

Guy Powell:

presentations all the time, and I do them now. And you know, and

Guy Powell:

sometimes you run out of time, and you just make more mistakes.

Guy Powell:

And so we're definitely looking forward to that. So I can

Guy Powell:

mitigate, remove some mistakes that I made. So but anyway,

Guy Powell:

Bill, thank you so much been awesome. The conversation, we

Guy Powell:

could keep on going, I guess the hour but really appreciate you

Guy Powell:

participating, and really appreciate, you know, your

Guy Powell:

perspectives on data sciences and where things are going to be

Guy Powell:

going and the certainly the challenges that we've, we've got

Guy Powell:

today, definitely please go out to on Amazon or otherwise to

Guy Powell:

purchase winning the room, Bill's new book, I'm sure it'll

Guy Powell:

be great. You can also reach out to bill at Bill dash Frank's dot

Guy Powell:

com Bill dash Frank's dot com, and I'm sure you'll find you'll

Guy Powell:

be able to find more information there. Otherwise, please stay

Guy Powell:

tuned for many other videos in this series of the backstory on

Guy Powell:

marketing. And please visit marketing machine pro

Guy Powell:

relevant.com/getting started a mouthful, pro marketing machine

Guy Powell:

pro relevant.com/getting started and you will also be able to

Guy Powell:

download the first chapter of my book and other valuable

Guy Powell:

excerpts. Thank you so much, Bill.

Bill Franks:

Yeah, thanks for having me.

Guy Powell:

Absolutely. Thank you.

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About the Podcast

The Backstory on Marketing and AI
with Guy Powell
Dive deep into the dynamic marketing realm in the digital age with The Backstory on Marketing and AI, hosted by Guy Powell, the visionary President of ProRelevant Marketing Solutions. This enlightening podcast is your gateway to understanding the intricate interplay between data-driven marketing strategies and cutting-edge AI technologies.

Each episode brings to the table candid and insightful conversations with some of the industry's most influential leaders and analytics experts. They share their valuable perspectives and experiences on how to navigate the ever-evolving marketing landscape successfully. As a listener, you will be able to discover the most current trends shaping the marketing world and learn innovative ways to leverage AI to elevate your brand's presence and impact.

The Backstory on Marketing and AI is an indispensable resource for anyone involved in marketing, from executives managing to proactive marketers. Whether you're an executive overseeing a hefty advertising budget or a marketer at the forefront of a growing brand, this podcast is your resource for staying ahead in the competitive marketing world.

Tune in on Apple Podcasts and Spotify and be part of the pivotal discussions defining the future of marketing. Don't miss out on this chance to revolutionize your approach to marketing and AI. Subscribe today and begin becoming a more informed and strategic marketer. For more information, visit www.prorelevant.com.

Typical questions discussed in this podcast:
How is AI transforming traditional marketing strategies?
What is the role of data analytics in understanding consumer behavior?
What are the best practices for integrating AI into your marketing campaigns?
What is the future of personalized and content marketing with AI?
What are some AI success stories and case studies: Brands leading the way in AI marketing?
How can we best overcome challenges in adopting AI technologies for marketing?
How can we measure the ROI of AI-based marketing initiatives?
How can we build a customer journey map leveraging AI insights?
How can we maintain privacy, data protection and cyber security in the age of AI marketing.
How can we build a skilled team to leverage AI in marketing?
What is AI's influence on social media marketing strategies?
What is the right balance between AI automation and the human touch in marketing?
What are the limits of using AI to support Chatbots?
How can young marketers leverage AI in their careers?

Topics Discussed:
AI Marketing
Data Analytics
Predictive Analytics
Brand Strategies
AI Ethics
Creative Advertising
Marketing ROI
Customer Journey
Content Marketing
Chatbots
Data Privacy
Social Media Strategies
Small Business Marketing
Prompt design and engineering

Main Questions:
What is the difference between ChatGPT and Bard?
How can Canva be used for image development?
What is a Large Learning Model (LLM)?

Testimonials:
In this fun and easy read, Guy provides a roadmap on how you can navigate through today's choppy waters and come out on the other side with a successful, metrics-based marketing campaign.
Jamie Turner, Author, Adjunct Instructor, Speaker, and Consultant

Guy does a great job of outlining marketing strategies adopted during the pandemic through some very insightful case studies and is a must-have for marketers.
Sonia Serrao, Senior Director, Brand Marketing at Tarkett

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