Change username form. Insert an info and press enter to submit, or press escape to close.
Create a new account form. Press escape to close.
Validate mail form. Press escape to close.
Lost password form. Insert an info and press enter to submit, or press escape to close.
Confirm address message dialog. Press escape to close.
Ep. 14: Brian Kalish - FP&A Embracing Big Data and Technology
September 03, 2019 | 18 Minutes
Brian Kalish, Principal and Founder of Kalish Consulting, joins "Count Me In" to talk about the benefits Big Data and emerging technologies have on financial planning and analysis (FP&A). As a subject matter expert of financial industry best practices, strategic forecasting and planning, and adaptive decision-making, Brian explains why the field of FP&A needs to embrace technology and describes how it can help organizations answer many of its outstanding business questions. He is a former Executive Director of Global FP&A Practice at AFP and has over 25 years experience in Finance, FP&A, Treasury and Investor Relations. In 2014, Brian was awarded the Global Certified Corporate FP&A Professional designation. Brian is passionately committed to building and connecting the global FP&A community. He continues to host FP&A Roundtable meetings in North America, Europe, Asia and South America. See Brian's contact information below the episode transcription!
LinkedIn - http://bit.ly/2hoZavs
Twitter - @FpandaBTK
LinkedIn - http://bit.ly/2hoZavs
Twitter - @FpandaBTK
FULL EPISODE TRANSCRIPT
Adam Larson: (00:05) Welcome to “Count Me In”. Thanks for coming back and listening to another engaging and insightful accounting conversation with us here at IMA. I am Adam Larson and I think you're really going to enjoy today's episode, as, in a minute we will listen to my co-host Mitch Roshong talk with Principal and Founder of Kalish Consulting, Brian Kalish. At the end, please take a moment and write a review and let us know what you think. Tell us how we're doing and what you think about the series either on this episode or by sending us a message with some feedback. So as I understand it, the theme of your conversation, Mitch, was why and how FP&A should embrace big data and technology. Tell us a little bit about Brian and some of his interesting points.
Mitch Roshong: (00:46) Sure thing. Brian is an avid baseball fan, a history buff, and an extremely successful FP&A, a professional. For our conversation. He was able to explain how the emergence of big data is an asset to financial planning and analysis and that technology is not necessarily disruptive. One of my favorite quotes from the conversation was, the science of today is merely the technology of tomorrow. And Brian does an excellent job shaping the conversation around the opportunities created in FP&A. This was a really well rounded and interesting discussion. So let's listen now.
Mitch Roshong: (01:28) Brian, what kind of impact have you seen big data have on FP&A?.
Brian Kalish: (01:33) Well, I'd say Mitchell off the bed. You know, my, my gut answer is a huge impact. You know, in FP&A, we're really in the process of developing an analytics based culture of data driven decision making. And certainly utilizing big data is one of the components of that evolution. It's just incredible the amount of data that just exists today. I always like learning new things. And one of the things that I've learned recently is that we are now operating in a world of Bronto. So B-R-O-N-T-O-B-Y-T-E-S bytes of data, which is 10 to the 27th power. So, you know, we're now just, you know, given all the conductivity that that just exists in the world today. We just have so much data available to us. And what's, you know, kind of what's really changed is that we now have tools and infrastructure that permit us to actually analyze all this data in a useful, timely and, and cost efficient way. And so if you think about what the whole purpose of FP&A is, which is to help the organization make better, faster, smarter decisions, big data really flows into that. So as organizations begin to utilize big data, what's important from my perspective is really kind of the persona that FP&A has within the organization. So for most FP&A groups, you know, the aspiration is the move from being a reporter to a commentator, to an advisor. And I'd say kind of at a truly visionary standpoint becoming a strategist and why this matters and how big data plays into it is it really can help us answer the questions that the organization has for us. So whether you're at the corporate level, you're embedded in a business unit you're helping marketing or HR by utilizing big data, we can move from just answering the question of what happened to where did it happen. And then where it really becomes important is why did it happen, what might happen? And again, kind of at that top level is, you know, having the impact to actually make something happen. So you're looking at another way of dictated will move us up the maturity curve from just providing hindsight to what I hear most business partners asked for today, which is insight and I, you know, I'm a little bit further out the curve. I really think FP&A can actually begin providing foresight to the organization. So if you think about the level of analytics that we can use, you know, we can move from just providing descriptive to diagnostic to predictive and then ultimately to prescriptive. And certainly big data is one of the pieces that can help us get there.
Mitch Roshong: (04:36) Sure. So as this big data flows into FP&A, and I love how you talked about the, the value maturity curve. As we kind of move along that curve, what are the challenges that are presented because of big data and the amount of it that you previously mentioned?
Brian Kalish: (04:54) Sure. So, for me, I kind of have four pillars of what FP&A is built out of and they're certainly, you can always find challenges within any of the pillars. So, you know, what are we talking about as people, technology, process and culture. You know, you have to have a culture that wants to consume business new level of analytics. I was recently with an organization engaged with them and basically their management doesn't want it, like they're not interested in it. It's hard to implement it if you don't have a consumer. So you have to be in a proper culture that is willing to embrace it and utilize it and, you know, spend the resources to make it happen. But you also have your, you have to have the right processes in place because obviously as we're introducing new data sources, it's important to have from both a data governance perspective, but also from a decision making perspective. Do you have the proper governance in place? Do the people have the right skills? And then I think where we spend a lot of our time with big data is, is the technology. So it's great that we have access to this incredibly large database of structured and unstructured data, internal, external. But do we have the proper technology to do the analysis? And do we have systems that are actually powerful enough in place? I mean, these things all exist today. I mean, there are organizations that are certainly leveraging big data and utilizing even in artificial intelligence. But for individual organizations, do they have the proper technology in place? And one of the things that's I think has been fascinating is that we've really, and I'm not trying to get too wonky too soon but just the infrastructure, you know, we've been dealing with for the last 30 years, what's kind of known as an ETL, which is extract, transform load environment, which is, you know, data warehouses and we've very familiar with that. But what's really incredible, and I think what really poses a lot of tremendous opportunity is that we can now move to an ELT structure, which is extract, load and transform. And we don't have time to dive into it today. But basically by making that structural change, we can leverage big data in a much more timely and cost efficient manner. So really things that, you know, once seemed like magic or are truly possible today.
Mitch Roshong: (07:22) In FP&A. How can individuals, you know, functions, organizations really embrace and take advantage of this big data that's available to them? How do they overcome the challenges and add value to the company?
Brian Kalish: (07:36) Well, that's a great question, Mitchell. And part of it is, you know, typically you're just, no matter how large the organization may be or small, you know, it's very difficult to make enterprise wide changes. So what I advise people to do is to, you know, look for small wins and make small bets. And so one of the things I always challenge FP&A teams that I work with is think about it just a persistent business question that you haven't been able to answer and really think about the opportunity to leverage big data to do that. So again, kind of my advice is, you know, start small and simple, you know, look for that low hanging fruit. And regardless of the issue that we're talking about is look for a way that you can really bring the full fire power of big data into adding value. So whether it's a forecasting question, it's a data analysis process, you know, basically looking for a very simple, clear demonstrative way of saying because we're utilizing big data, we were able to answer this for you. And so I'm a huge baseball person. Probably I'm beyond fandom. And so, you know, my thing is, you know, you know, start hitting singles, right? Don't, don't go for the fences because as you start to incorporate utilizing big data into just the normal course of your analysis, people will see the success that you're having. And I'm a true believer that success begets success. And as you solve problems for people, as you answer their questions, they're going to continue to come back to you. And so what's you're able to do is start small and then kind of move up the complexity curve and then create more, you know, strategically actionable insights. And as your business partners become more and more engaged in what you're doing, you will become much more important to them. And that's kind of how you'll move from being the, that reporter to commentator to, to advisor.
Mitch Roshong: (09:41) And that's just it. You know, in preparation for this call, I read up on some of the things that you've written and I noticed that you made the comment technology, like AI big data is not necessarily disruptive. And I think that kind of ties into what you were just saying about embracing change. And facing the fact that this stuff is here and it is part of the industry. So I just like to get your thoughts and kind of explain what you mean by this isn't disruptive technology.
Brian Kalish: (10:12) Sure. And so and one of the pieces that I read that you may have seen, you know, you know, I don't know, maybe it's just a matter of semantics, but to me when you say something that's disruptive, that has a negative connotation to me. So it's something bad, right? You're disrupting, you know what I'm doing, you're disrupting my, my thought process, right? That that to me means something negative. So when we're saying by introducing this technology that's going to be disruptive to the business, my takeaway, again, it might merely be somantics. Is that something negative. To me, technology is something constructive. And so to me, constructive is a positive term. So when I hear it, I think was something good. So, you know, I'm an FP&A guy, I'm a baseball guy, but I'm also a history guy. If you'll indulge me for a moment, you know, 1946, ENIAC, which was the electronic numerator integrator and computer was created at the University of Pennsylvania. Certainly the world thought that was disruptive. I would say it was constructive. 1971 Intel introduced the 4,000 for microprocessor was it was introduced. Again, people would say that was disruptive. I would say constructive. You know, '85, first version of excel was released. Disruptive? Most people feel, you know, the way that life changed. It's interesting as I'm using these examples, it's like the word is change, disruptive or constructive as the adjective that we use to describe it. So, and then kind of getting to where we are today. Back in 2010, Microsoft you know, released Azure, which was the first cloud application, I would argue, constructive. And then today, in all honesty as this kind of progression has occurred. It's all about big data. So to me, when you look at all these changes, and that's not just, you know, we're just kind of talking about technology and finance, but if you think about just the world in general you know, technology is just new tools that come online that just help us do our jobs better or what I think is really important as these technological advances occur, they actually permit us to do activities and jobs that we just physically weren't capable of doing previously. So if you think about all the technology we've had in the past, all the changes that have occurred, all the jobs that have gone away, but all the new jobs that have been created, I mean, that's, you know, without trying to get too deep, too fast, that's, that's what we're talking about. So I always liked, you know, reading people that are much, much smarter than myself. So someone that I really enjoy is Edward Teller and one of the quotes that he has, I, I kind of use a lot, which is the science of today is merely the technology of tomorrow.
Mitch Roshong: (12:52) I'm curious to get your perspective now on what your thoughts are on a, going back to your quote, what is the technology of tomorrow? Why is AI and all this stuff the future and how does that fit into FP&A and finance?
Brian Kalish: (13:06) Sure. I mean, if we think about it again, because we're operating in a world of brontobytes, they're just not enough humans in spreadsheets, you know, to throw at the data and in this highly competitive very volatile world that we operate in, that we think we're going to get timely cost efficient and useful actionable information if we're not utilizing the new technologies that are down there. So whether it's RPA, robotic process automation, machine learning, you know, artificial intelligence you're not gonna survive, you know, you got you, you know, you certainly not going to thrive. And there's just too, from my perspective, what I see with organizations and I am, you know, I'm, I think I'm one of the luckiest people in the world. I get the opportunity to travel around the world and talk to organizations big and small across all different kinds of industries, corporate structure and you know, get to the beauty of sitting on this lovely hub of all of this data and, and hopefully sharing some of it with, with, with people like your audience today, but also clients that I have and, and try to explain to them kind of what you were just asking about. This isn't science fiction. I worked with organizations today that utilize artificial intelligence. I mean, it's simply the path that we're on. Again, probably Brian and his endless references. He's like, you know, if you're familiar with the terminator, we're, we're not at the point of turning Skynet on, you know, it's not the idea that we're turning over the business to the machines, but all the sudden we are adding an incredible resource that can just help us think about things we've never seen before. I mean, if you think about how humans operate, we are excellent at spotting trends that we're looking for. We are terrible at spotting trends we aren't looking for. And so something like artificial intelligence can take massive amounts of data. And we're not talking about just running scenarios, we're talking about running simulations. And again, just something that a human physically can't do and certainly can't do with a spreadsheet. Um and just give us not so much a unique answer, but giving us a range of the most probable outcome. And by having that information, and I would argue that's truly knowledge in place in front of us when we're making decisions, it just gives us something to bounce it off against. So for example pharmacy, excuse me, a pharmaceutical company that I'm working with, they have AI. So when they're looking at decisions, they run it through the model. It doesn't mean the model's right. But it gives them pause to think about if they come up with an answer that's different or strategy that's different. Why do we think that it would differ from what the model is coming out? And the beauty is that the, the model itself is a virtual cycle. So it's always being updated. So, you know, when we think about what, what artificial intelligence, you know, can, can truly deliver for the organization, I see its competitive advantage. Now as those organizations adopt sooner than others, and again, going back to my baseball analogy, it's not about winning all the games about winning more than your competitors. It's not about hitting the ball every time. It's about having a high batting average. That's what it's gonna really differentiate the winners from the losers going forward. I happen to be a person and I, I truly believe we live in a transformational time just because the way that we've done things or in the last 20 years is certainly not the way that we're going to be doing and going forward. And so, you know, for FP&A, I, I do think it's a very exciting time. You know, the opportunity to basically dive deep into analysis that just, you know, a short period of time ago it was physically impossible. I mean, we are now going to be able to model, forecast, plan, drive business decisions at speeds we just previously were unheard of. But again, let's take a step back and ok, people can get just completely enamored with technology. Also, technology is never the solution. It's just a tool. And so it's important to remember that we must be continually developing our people and to leverage these tools to their highest capacity.
Announcer: (17:32) This has been, "Count Me In", IMA's podcast providing you with the latest perspectives of thought leaders from the accounting and finance profession. If you like what you heard and you'd like to be counted in for more relevant accounting and finance education, visit IMA's website at www.imanet.org.