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Welcome back to episode
148 of Count Me In,

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

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This is your host Adam Larson,

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

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

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focusing on teaching undergraduate
and graduate courses in accounting and

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finance.

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

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

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

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Keep listening as we head
over to the conversation now.

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In the field of finance and accounting,

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

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

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I'm just curious from your perspective,

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

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adoption at these larger enterprises?

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Yeah, so I think the biggest thing,

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

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realization of ROI (return on investment),

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where companies can use these
new techniques, analytics,

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automation to accelerate
their processing, right?

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So in finance accounting, for years,

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we've been doing things
manually and repetitively.

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And now with these new tools
and these technologies,

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a lot of companies are adopting
these tools to accelerate

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

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and there are benefits
like it's more accurate,

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there's more control,
better internal control.

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

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

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companies are just taking
advantage of this, the,

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

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

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

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And, you know, just for our listeners,

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

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

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

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

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

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and coming out, you know,

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

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happening in the finance
function in terms of,

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financial and managerial accounting.

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We're really talking
about Tableau and Alteryx,

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

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

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fairly new, but very much highly used.

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And we call themselves
service tools because,

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it's not something you develop,

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

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So for example, an Alteryx,

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

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

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

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

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

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

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

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

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

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assisted automations for Alteryx
and in Tableau it's really

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dashboards and visualizations.

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

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

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

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you know,

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

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you know, our, our listeners,

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

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

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

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

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

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

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what are the end goals,

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

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

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after some investment,

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what you can end up
doing is something that

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

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And, and we had this also,

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

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the self-service data analytics
and governance for managers,

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

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the MBA.

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

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and income statement data. And,

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

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profit margin, asset
turnover, return, and equity.

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And we do like the DuPont model,

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

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what we did as a case study in the book,

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we put it in the Alteryx

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

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

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

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but once we set it up in Alteryx,

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we're able to filter the data by industry.

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

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We started looking at graphs
for each company of all the

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ratios,

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

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

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

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

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

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

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it could still be profit
margin by region or geography.

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

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America and see my ratios, you know,

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

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

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

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

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

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

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And of course we have seen the benefits.

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

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

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

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In fact, it is still stable.

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

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

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

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

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

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

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

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essentially, as you know,
within the finance function,

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

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

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

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

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

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

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They're used to, as you said,

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

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the trust factor,

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

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experience, what does that look like?

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Okay. So, the learning curve is basically,

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

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

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you know,

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

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

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

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depending on the situation,

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

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

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

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Following the videos,

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

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and then the setup itself,

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

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

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

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

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

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bit more to play around with it.

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

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

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these podcasts, or,

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

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

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

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

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

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

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There's really no standard way.

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

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

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

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

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So I would think, you know,

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

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

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

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

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

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

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

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

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

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

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

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but the governance needs to be in place.

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So what in your, you know, your voice,

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

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

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

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we speak extensively about governance and,

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you know,

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

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

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And I think data governance
in this context really focuses

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on the input level because when you,

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

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accessible to management
accountants, the biggest,

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most encompassing issue is on

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the input side, right?

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

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

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

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as it moves from different places,

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

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

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

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is properly has proper
security and integrity,

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privacy, accuracy, and
then the extra step.

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

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was just like a whole field of study.

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

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

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self-service analytics are
things within the tool.

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

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

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

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

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a whole email database.
So it gets more advanced.

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The capabilities are there
and people are using them.

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

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Alteryx has to be verified,

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

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

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

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

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

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

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that's going somewhere else.

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

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we were going to build these,
analytics assisted automations.

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

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

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performance of those builds.

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So just real quick on that topic.

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

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

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

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like you said, the custody chain there,

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

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

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

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

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

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

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

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

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

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And we probably are aware of that already,

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but the additional piece here, is that,

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

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

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that centralized ERP system
already was designed with

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

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segregation of duties,

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and all the users were funneled
into those control funnels.

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

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doing old tricks on
their desktop, you know,

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getting their data from
wherever and inputting it in.

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

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

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

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control architecture. So we
need those additional controls.

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

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

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

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

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

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my additional system checks,

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

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

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

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

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And the other thing we recommend,

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

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

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

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

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

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we want to scale digital
transformation analytics.

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

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

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

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like I said,

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

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

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

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

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

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

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

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what kind of information or,

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

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

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

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and essentially what we do,

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

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this analytics and service
self-service analytics.

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And we discuss it in a way
that is very accessible

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

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And,

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

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

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

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

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

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technologies and we make the
argument that where we are,

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where we are today in
accounting is where aside from,

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for example, artificial
intelligence and RPA,

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which are tremendous topics in this world,

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

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is something that is really happening.

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

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

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

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

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

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

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Tableau, because it is decentralized.

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

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

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that each of these projects has
proper assurance capabilities

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and,

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development standards and it's
properly documented and has the

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proper data governance and in
risk governance talks about how

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

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

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builds and risk assess
each, and then monitor them,

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monitor them, report
risks, report exceptions,

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create risk transparency.

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

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

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in a way mistrust with technology.

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

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

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

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basically a checklist of governance
precepts, for project risk.

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And then we also talk about investments.

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

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

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grow your ROI. And we call
that investment governance.

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So again, before I wrap this up,

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

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

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

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

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hands on it?

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Absolutely. So the book is on Amazon,

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you get it through Amazon
or through Wiley directly.

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

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governance for managers, it comes up.

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

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Wiley has a very nice thing.

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

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

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

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This has been Count Me In,

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00:21:26,950 --> 00:21:30,700
IMA's podcast providing you
with the latest perspectives
of thought leaders from

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00:21:30,701 --> 00:21:32,380
the accounting and finance profession.

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If you like what you heard and you'd
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00:21:35,801 --> 00:21:37,360
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00:21:37,510 --> 00:21:41,740
visit IMA's website at www.imanet.org.