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...