Ep. 148: Gregory Kogan - Self-service Analytics

Gregory Kogan, CPA, joins Count Me In to talk about the value and importance of self-service analytics. Greg is a professor of practice in accounting at Long Island University focusing on teaching undergraduate and graduate courses in accounting and finance. He received his MBA from Rutgers Business School in Accounting and a Bachelors in Science in Computer Science from Rutgers University. He is also currently pursuing his Doctorate in Business Administration at the University of Scranton with the research focus of data analytics in accounting. In this episode, you will hear Greg talk about what is driving the accelerated pace of analytics adoption, how organizations can and should focus on self-service tooling for their analytics, and how data governance continues to play a role. Download and listen now!

Welcome back to episode
148 of Count Me In,

IMA's podcast about all things affecting
the accounting and finance world.

This is your host Adam Larson,

and I'm pleased to kick off
today's episode by introducing
you to Gregory Kogan.

Gregory is professor of practice and
accounting at Long Island University,

focusing on teaching undergraduate
and graduate courses in accounting and

finance.

He is also currently pursuing
his doctorate in business
administration at the

university of Scranton with the research
focus of data analytics and accounting.

So in our upcoming episode, you will
hear Greg discuss self-service analytics.

Keep listening as we head
over to the conversation now.

In the field of finance and accounting,

there's been a lot of talk
about data and analytics. And,

I know in your space you
have a lot of experience.

I'm just curious from your perspective,

what is really driving the accelerated
pace of analytics and the overall

adoption at these larger enterprises?

Yeah, so I think the biggest thing,

and if we're talking about the
finance function, it's really the,

realization of ROI (return on investment),

where companies can use these
new techniques, analytics,

automation to accelerate
their processing, right?

So in finance accounting, for years,

we've been doing things
manually and repetitively.

And now with these new tools
and these technologies,

a lot of companies are adopting
these tools to accelerate

processing, reduced processing time,
reduce hours, accelerate processes,

and there are benefits
like it's more accurate,

there's more control,
better internal control.

And those are really big benefits
on top of the financial benefits.

So there's sort of a
convergence, I think that,

companies are just taking
advantage of this, the,

to have more smooth and streamlined
processing. That's more efficient.

Now I know something that
you focus on or, you know,

you'd like to share a little bit more
here are these, self service tools, right.

And, you know, just for our listeners,

what are some of the defining
characteristics of this
subset and how does it

work into analytics? And, you
know, when it comes to again,

advancing some of these
opportunities, I guess you could say,

why do these tools lead to more of a
decentralized pattern for your reference?

Right? So the tools, yeah. So the
tools we're talking about, you know,

and coming out, you know,

very much out of the what's happening
in public accounting and what's

happening in the finance
function in terms of,

financial and managerial accounting.

We're really talking
about Tableau and Alteryx,

which are off the shelf tools.
And even in higher education,

we have a lot of these now in the
classroom. So this is a still pretty,

fairly new, but very much highly used.

And we call themselves
service tools because,

it's not something you develop,

what you end up developing is a
specific process within that tool.

So for example, an Alteryx,

you can create a little process
that say does a reconciliation or a

certain reporting. And it's
something that used to live in Excel.

That's really now living in this
tool and we call it self service.

It's in that bucket of you can
really do it yourself, much.

Like you do Excel yourself. You could
really, as a finance professional,

since it's low code or really no
code you pick up the tool you put in

your data, which you really,
you already have access to.

That's really something you
work with on a day to day,

and you can set up these,
we call them analytics,

assisted automations for Alteryx
and in Tableau it's really

dashboards and visualizations.

So it depends what part of it
you're working with. But yeah.

That's very helpful. And I know, you
know, in our space management accounts,

specifically, a lot of that, you know,
internal focused and we're really into,

you know,

the storytelling behind it and the tools
that you referenced literally enable,

you know, our, our listeners,

our finance and accounting professionals
to present this data in a way that's

easily easy to understand for everybody,
right. I think that's really the goal,

but, you know, taking it even a
step further here, try to, you know,

set the stage for us a little bit. What
are some of the primary motivations?

And, you know, there is some
kind of investment or, you know,

even if it's just a learning curve
in order to adopt these tools,

what are the end goals,

but what can our listeners
expect if they're able to
implement these strategies?

Right. So what you can, what are the,
some of the benefits, essentially,

after some investment,

what you can end up
doing is something that

you do on a recurring basis,
manually in Excel, right?

And, and we had this also,

as a case study in the book that
we're kind of referencing here,

the self-service data analytics
and governance for managers,

but this is something that I've been
doing as a case study with students and in

the MBA.

And what happens is we basically have
like five years of data of balance sheet

and income statement data. And,

and we do this in Excel where we
compute all the financial ratios,

profit margin, asset
turnover, return, and equity.

And we do like the DuPont model,

basically for all the companies
in the S&P 500. So for example,

what we did as a case study in the book,

we put it in the Alteryx

and then we set it up as like
little steps, rather than Excel.

It's sort of all in one big place
and you could still see everything.

And we do pivot tables and graphs.
It's still a very, very good,

but once we set it up in Alteryx,

we're able to filter the data by industry.

So all of a sudden we started looking
just at information technology.

We started looking at graphs
for each company of all the

ratios,

and then we started looking at specific
companies a little bit further down the

line to see, oh, wait, we just
keep looking for the best one.

What is the best industry? What is the
best company? And then for that company,

we have four dashboards for each
of the ratios over five years.

And after we set that up, we
thought, wow, if this was like,

say this was in management accounting,
and I was doing my own internal reports,

it could still be profit
margin by region or geography.

I could really sit with that and just
flip my filter from Europe to north

America and see my ratios, you know,

and then we were thinking about it
for me to do it in Excel every month.

And it's something I used
to do as an accountant.

I just imagine it's a lot of work, get
the new data uploaded, reconcile it.

And that's something that takes us
a couple of dates and just the flip,

the switch. And Alteryx where
you just upload the new data.

And it does it for you. That's
what we sort of started imagining.

And of course we have seen the benefits.

We've talked to people who've seen the
benefits, but just to feel it yourself,

like that amount of work going down
from three days to like 30 minutes

is exciting. And I don't know, I don't
think you lose anything in the process.

In fact, it is still stable.

It is controllable and it's more
flexible because the, all the charts are,

you still see them, you know,
and you just, you do it yourself.

It's not something you have
to call an IT person too.

So I think it can even
feel very empowering that
it's still your it's within

your role and your routine. You
just do it in a different way.

Just a quick follow-up on this,
part of our conversation here,

you just mentioned, you don't need it
for this. It is your responsibility,

essentially, as you know,
within the finance function,

but what is the learning
curve? You know, what,

what goes into actually being
able to upload this data and,

you know, work with it to a point
where you're comfortable, you know,

first of all, you have to trust it, right?

You have to trust that everything's
working because you're not the one who's

actually doing it. I think that's a
big thing with accountants, right?

They're used to, as you said,

reconciling everything and they
know that it's right, but you know,

the trust factor,

but then the learning curve and just
being able to do it all from your

experience, what does that look like?

Okay. So, the learning curve is basically,

and this is something that I went through
a couple of years ago is something

that, it can take a couple of weeks
essentially to get ramped up. And,

you know,

Alteryx itself has a bunch of
videos on their website and a,

and a guide on each one of the
processes. And they give you,

I believe the license. And also the
licenses are very affordable or free,

depending on the situation,

whether you're a student or
affiliated with an organization,

or it might even be available within
your organization. And I would say, yeah,

it's a couple of weeks that are kind
of playing around with the process.

Following the videos,

the training itself can take a couple
of weeks to get set up with these,

and then the setup itself,

or the specific process can be a
couple of hours or a little bit more.

So it's not, it's not as intensive
as you would think, oh, wow.

I have to take a year to
go study this stuff. No,

it's a couple of weeks of, I would say,

an hour or two a day to get
caught up and then a little

bit more to play around with it.

And the only thing I would recommend
is speaking to the other people who are

using these tools and kind of try to
connect whether it's online or through

these podcasts, or,

I know that IMA has several
courses that I've taken on

RPA. I'm sure now there's
other ones in data analytics.

So actually I've taken a couple of
IMA courses on data analytics and RPA,

but those are very helpful.
And if you, in fact,

if you start with one of those that I
think that one was four hours and you

build your own study and you connect
it with speaking to some people. Yeah.

There's really no standard way.

I think because it is a
little bit of a new space,

but I think organizations like yours
really help out because in a way I would

compare it much easier than say going
out and studying for the CPA. I mean,

we're talking about really like a
10th or one point of the effort.

So I would think, you know,

start with those smaller courses
and build up from there. That's

that's perfect. And thank you for
sharing that your personal experiences,

you know, I think that helps our, our
listeners really understand, you know,

all this sounds great, but having an
idea of what goes into it, you know,

it makes it a little bit more, you know,
feasible, I think, in their minds. So,

that's great. I appreciate it. And, you
know, taking this a step further now,

talking about analytics and the
different things you can do with data,

if you've taken a couple of
the IMA courses, you know,

one of the things that we've put at
the foundation of all of our data and

analytics conversations is governance,
right? And I think that's something that,

you know, you might be able to
automate some of these processes,

but the governance needs to be in place.

So what in your, you know, your voice,

what is the importance of data
governance and what goes into the,

the requirements, you know, your
recommended procedures, policies,

whatever it may be. Yeah,
yeah, absolutely. Yeah,

we speak extensively about governance and,

you know,

I think data governance is a huge
field of study, right? And, and,

and it, you know, and it ends up
probably the best way to enter it.

And I think data governance
in this context really focuses

on the input level because when you,

when you work with the self-service
analytics tools that are very much

accessible to management
accountants, the biggest,

most encompassing issue is on

the input side, right?

Much like with any Excel spreadsheet
and picking the same principles,

you have to make sure that those inputs
are correct. That there's integrity,

that there's a sort of
a custody chain of data

as it moves from different places,

whether it's in the ERP system
or a cloud to the Excel,

to the Alteryx out of Alteryx
to somewhere, back to the ERP.

So that custody chain has to be
maintained and made sure that every link

is properly has proper
security and integrity,

privacy, accuracy, and
then the extra step.

So all that I think is already well-known
in a way in the data governance

was just like a whole field of study.

And I know you focus that you
focus at a time a very much,

and then the additional pieces that
we discussed that are specific to

self-service analytics are
things within the tool.

So once you get in the pool
and say, Alteryx, yeah,

it sounds the one I described does
sound kind of like simple and exciting,

but I've seen some that have like 50
processing steps and they're looking for

fraud. And now they're using
advanced text analytics, the mine,

a whole email database.
So it gets more advanced.

The capabilities are there
and people are using them.

So what we recommend is also that
additional layer of each step within

Alteryx has to be verified,

has to be assured and has
to be tested, you know,

make sure what you put in is
what you're expecting to get out.

Make sure it doesn't seem like a black
box where you don't know what's happening

inside and making sure that the
auditability of it is there, you know,

whether it's for management, accounting,
and that's going to management,

or it might be a number that
ends up somewhere in a report,

that's going somewhere else.

So there's a risk assessment part to
it that we discussed where we say, Hey,

we were going to build these,
analytics assisted automations.

We're also going to make sure that we're
aware that there could be some risks

and we should be auditing some of that
risk and we should be monitoring the

performance of those builds.

So just real quick on that topic.

Cause I did have a follow up on
that as far as risk goes. Again,

some people who are new to this and may
not have the governance procedures in

place and, or, you know, any kind of
internal control, maybe over, you know,

like you said, the custody chain there,

if you don't take the time and put in
the effort in order to ensure that,

you know, all of these policies are in
place, what could the risks be? You know,

sometimes you want to give them a
picture of down at the end of the road.

If you don't do all this,
this is what could happen.

So why is governance so important? What
are you really hopefully preventing?

Right. So what's going, what happens
with these? And this is on one level,

there's that traditional risk that we
know from accounting where, you know,

number's wrong and ends up on a report
there's liability, there's risk,

there's reputational risks. There's
financial risk. Yes. That's all there.

And we probably are aware of that already,

but the additional piece here, is that,

you know, if you go
back before all of this,

all of our digital processing was
inside of some kind of ERP system

that centralized ERP system
already was designed with

the controls in mind, authorizations,
reconciliations, different checks,

segregation of duties,

and all the users were funneled
into those control funnels.

If you think about it, now we have
people sitting there doing Tableau,

doing old tricks on
their desktop, you know,

getting their data from
wherever and inputting it in.

So that's sort of what we
call data. democratization

where now users are using the
data to do their own processing.

So they're not being funneled into the
central ERP system that has all the

control architecture. So we
need those additional controls.

And one of the things that can
happen is just total chaos,

where everybody's doing their own
processing and they're doing their own

reporting and say, you're like a
higher level control manager at NuCalm,

and you say, how do I know any
of this is right? It's not,

where's the segregation duties where
where's the reconciliation where are

my additional system checks,

that's all happening on
those decentralized basis
now. So without governance,

it could be a situation where you
actually can't really rely on any of those

outputs anymore. So that's sort of a,
trying to get it in advance of that.

As it gets adopted, the governance
can really, can really help.

And the other thing we recommend,

and I think you mentioned that it's
also lack of governance has been

known to be the number one issue in
scaling the analytics. So people say, oh,

all of a sudden, oh, I can't
do this. Oh, this is too much.

And it's partly because there's no
governance and scaling analytics can be a

huge digital transformation
goal. So we sort of say, Hey,

we want to scale digital
transformation analytics.

That is sort of something that's
actually going to help you do that.

Otherwise it's, it's really, it's
kind of a steep slope without it.

That's perfect. That's exactly what
I was looking for. And, you know,

like I said,

sometimes you just paint that picture
upfront and give everybody the heads up,

but this all sounds great. And listen,
I know you briefly mentioned the book.

I want to give you an opportunity to
talk a little bit more about it here,

as we wrap up our conversation, because
it's all very valuable information.

So how is all of this really
presented in the book? And again,

plug the name for us one more time and
give us a little bit of the background

story to it. Why's it all
relevant? And then, you know,

what kind of information or,

takeaways can our readers and listeners
expect from some of the work that you.

Absolutely. Yeah. So the book
is by myself and Nathan Meyers,

and it's called Self-Service Data
Analytics, Governance for Managers,

and essentially what we do,

it's sort of a two-step process
where we first discuss all

this analytics and service
self-service analytics.

And we discuss it in a way
that is very accessible

to accountants because we're both
CPAs with an accounting background.

And,

Nathan has been leading these digital
transformations in the corporate world,

and I've been embedding analytics into
accounting classes in higher education.

And we sort of looking
at from a perspective of
making it really accessible,

making it really understandable and
making it really kind of down to earth.

And that's the first couple of
chapters where we define all of these

technologies and we make the
argument that where we are,

where we are today in
accounting is where aside from,

for example, artificial
intelligence and RPA,

which are tremendous topics in this world,

we kind of make the argument that
look this world of Tableau and Alteryx

is something that is really happening.

And we kind of make the argument that
it's something that may grow quite a bit.

And I, and I have seen it
growing since we started,

in the past year and we'll
see what happens next year,

but it seems like a lot of organizations
that are really using it. So,

and then we go and say, we say, well,

there's an issue with controls if
you're doing everything in Alteryx and

Tableau, because it is decentralized.

And we actually propose a whole
governance framework for users. And it's,

it's mainly around project governance
where you kind of make sure

that each of these projects has
proper assurance capabilities

and,

development standards and it's
properly documented and has the

proper data governance and in
risk governance talks about how

each one of these can be risk assessed
according to unique risk dimensions,

and basically treat them as a portfolio
and have a whole portfolio of these

builds and risk assess
each, and then monitor them,

monitor them, report
risks, report exceptions,

create risk transparency.

And basically the goal is to
create trust and the outputs.

And now we live in the world
where there's so much emphasis on,

in a way mistrust with technology.

And then there's also a ton
of emphasis by accountants.

I think we're leading the way
in creating trust around that,

but then that's really the goal.
Each chapter includes like a,

basically a checklist of governance
precepts, for project risk.

And then we also talk about investments.

So make sure that your dollars are
going through the right opportunities,

make sure you are prioritizing the
best processes and it's sort of to help

grow your ROI. And we call
that investment governance.

So again, before I wrap this up,

we may have to bring you back and talk
strictly about this governance framework.

I think that's something that our
listeners would really be interested in,

but in the meantime, you know,
before we get that reporting done,

where can the listeners find
this book? How can they get their

hands on it?

Absolutely. So the book is on Amazon,

you get it through Amazon
or through Wiley directly.

And, and essentially as self service,
if you go self service, data analytics,

governance for managers, it comes up.

Usually it's Amazon is the
way to go these days, but,

Wiley has a very nice thing.

And the other thing I'll say is that
if you are a student or part of a

university, I know it's widely available
in, in all the university libraries.

If you just go, if you just search
it in the library search box as well.

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