BONUS | Ep. 4: Dan Smith - Technology & Data Analytics

(*EXTENDED EPISODE*
Conclusion of Episode 4 from 6/24/19)


#YAADS #datapossible


https://www.theorylane.com/
https://www.linkedin.com/in/daniel-smith-data-scientist/
https://www.linkedin.com/pulse/acid-just-sweet-80s-jeans-datapossible-daniel-smith/

https://github.com/thedanindanger


FULL EPISODE TRANSCRIPT


Music: (00:00)

Adam: (00:04)
Hey everybody. Welcome to Count Me In, IMA's podcast about all
things effecting the accounting and finance world. I'm Adam
Larson here with Mitch Roshong and this week we cover the topic
of data analytics and emerging technologies in accounting and
finance. We have an extended bonus episode for you where we will
cover multiple areas within this topic and conclude a previously
recorded conversation. Mitch, can you tell us more about
it?

Mitch: (00:28)
Thanks Adam. As you may remember a while ago I spoke with Dan
Smith at length about all these technology and data related
topics. Again, Dan is the head of innovation and the founder of
theory lane integration solutions and he offers a very unique
perspective on how these ideas relate to accounting, finance. Our
talk got even more interesting as it went on, so I'm really
excited for you to hear the remainder of our conversation.


Music: (00:54)

Mitch: (00:56)
How can senior management accountants who may have limited
knowledge when it comes to data analytics gain a deeper knowledge
or a better understanding so they can enable themselves and their
organization to kind of face these new challenges that are
presented or new opportunities as we've said to work with
technological tools.

Dan: (01:18)
Absolutely. We have this conversation almost every day. The
easiest answer for me would be to check out the the IMA's
analytics competency framework cause I've done a lot of advising
with you guys on that. Absolutely right. Quick plug!, A longer
answer is that I mentioned in a previous response the idea we're
starting to break down the barriers of traditional business
structure. There was a famous statement made over half a century
ago by I believe he was a doctor, Dr. Conway. It's called
Conway's law. It comes up in software development all the time.
Conway stated, "any communication system designed in a business
is going to model the structure of that business." In a modern
context, it means that any solution, any application that's
designed to solve a business problem is going to model the
structure of that business. Now we've created a whole new set of
ways we can current problems with the new paradigm of it's
actually the internet. It's digital data. It's not just
analytics, it's because now we can have information move in a
completely different way. We have a business structure that is
set up with a pencil and paper type of data in mind. Up until the
past 10 or 20 years, we've just used computers to accelerate what
was otherwise a written form of communication. Now we have to
have these cross functional competencies because information is
no longer constrained to a specific department. Those cross
functional competencies are what we've been calling data science.
That's the intersection of data statistics and business
application of data and statistics. In my general competency
framework, not the one that's just for accountants management
accountants. I replaced statistics with machine learning simply
because machine learning to me is the application of statistics
through computer programs as opposed to a more traditional
statistical approach. I don't think in many cases now for
financial accountants you do because you guys are heavy in math,
but in most cases you don't actually need to know that much
statistics. It's abstracted away in most of the models you just
need to know if it's right or wrong. So management accountants
are a little bit of an exception, but otherwise in terms of data,
the competency, if you know the lower level competencies, so you
know how data moves in an organization, where does it live? How,
how was it created, what are basic data structures and do you
know how to use the data to create analysis in such a way that it
benefits the business? Those are the low level competencies. I'm
going to get more into those later so I don't want to dwell too
much on them. fundamentally though it's the difference between
understanding the competencies, understanding the low level
reasoning behind what you're doing versus thinking about what
tools should I use or what program should I use to solve this
problem? Understanding what that tool is doing to solve the
problem as opposed to what type of tool should I use.

Mitch: (06:03)
Once we have that foundational knowledge, those low level
competencies, how do we, how do we move up? You know, how do we
get these skills, these competencies? How do we learn the tools
that are available so that we can make more effective
decisions?

Dan: (06:20)
Yes. Perfect segue. There's a slide that I use all the time and
you can probably find it on a webinar or on LinkedIn or somewhere
where I talked about the idea of concepts versus tools versus
technology. I use the analogy of building a house. When you first
want to build a house, you talk to an architect. That architect
uses the concepts of material design, of calculus, of structural
engineering, all these ways in which he or she knows where to
place a wall, to build a house, to put the foundation in, to put
up the roof, et cetera. What you don't ask that architect is what
type of hammer do they want to use for, or what type of CAD
software are they are they using to create these images? It's
irrelevant. and an architect certainly wouldn't start learning
architecture by going to the hardware store and picking out,
sitting there, evaluating what's the best hammer for the job.
They would figure out what the concepts are and they would
realize that, you know, a hammer might not even be what they
need. They might need a screwdriver, they might need a pneumatic
press. I look at learning a specific tool in the same way. Some
concepts and some knowledge and tools translates very easily into
other ones. I tend to recommend a bottom up approach with the
caveat that you want to be able to apply that knowledge as
quickly as possible. So you feel like your doing something, it's
easy to get discouraged if you just feel like you're taking
classes all the time. a nice mix of that is Python and Python,
Jupiter notebooks or the various notebooks, solutions that you
can find easily. If you know, if you know Python or if you can,
you can't, you can't know. You can't know a programming language.
First off, that's, that's a that's a common misconception. You
can be capable of solving problems using that programming
language, but you will never learn all of the programming
languages. It's impossible. It'd be like learning English. People
still study English all the time, but you can reach a level of
functional competency with it. With, with Python, you can
understand what's going on behind the scenes. You can do some
basic programming, but it abstracts enough so that you're not
bogged down in defining every single thing and working through
all this obscure knowledge with that knowledge, with
understanding basic programming competencies, you can move into a
lower level language like a Java or a C plus or a C sharp. you
can also easily move up into a into a more visual tool lik...