As a CFO, how can you create an analytics-driven culture? What barriers are standing in the way? I talked to Matt Schwenderman, principal, Deloitte Consulting LLP, to get his take on these questions. One of Schwenderman’s most important points is that as a CFO, you can’t be a catalyst for your organization unless you’re able to take information and turn it into action.
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Do you prefer reading? Here’s the full transcript, edited for clarity.
Greg Thomas: Stop me if you’ve heard this one before: Data is the new oil. CFOs are acutely aware that data itself has value, but just like oil that’s still stuck in the ground, if you want to get the value from your data, you’ve got to process it. That processing increasingly takes the form of analytics and the predictions that come from it.
I’m Greg Thomas from Workday. Today on the Workday Podcast we’ll talk about finance analytics. How can CFOs create an analytics-driven culture, and what are the barriers standing in the way? We’re joined by Matt Schwenderman, principal at Deloitte Consulting LLP. Among other things, Matt helps CFOs steer their way through financial transformation.
Matt Schwenderman: Thank you very much, Greg.
Thomas: Let’s just dive in. Finance has moved well beyond the old role of just balancing the numbers and doing standard reporting. Why are analytics so important to where CFOs want to go?
Schwenderman: Excellent question. Years ago at Deloitte we came up with something we call the four phases of the CFO. What many people are knowledgeable about is that finance is a steward and an operator of the organization. What we found was an increased importance to the role of strategist and catalyst.
You can’t be a catalyst for your organization unless you’re able to take information and turn that into actions. If you’re going to be a significant component of setting the strategy of the organization, you need a keen awareness of where you’ve been, where you’re going, and how you’re going to measure where you’re going forward.
So, analytics has helped move the CFO into that more balanced four phases over time.
Thomas: So where are we on that journey? When you think about people adopting analytics in a wide way, what do you see?
Schwenderman: Unfortunately, I think we’re at a very fragmented part of that journey. We have some organizations that are doing incredibly creative and avant-garde things with data and insight-driven decisions. We have other organizations that still rely on what I refer to as “human middleware” and moving spreadsheet-driven information throughout the organization, walking into very important meetings with various sets of the same results, and arguing about what the right number is, as opposed to what they should be focusing on, which is how to actually move the business forward.
The other thing we notice on the analytics side is that it really is also culture and skills driven. Those finance functions that are more focused on being a strong business partner, and have a depth of capabilities in the organization, are utilizing analytics to a much more rigorous extent than those who are interested in holding onto the old steward-gatekeeper roles.
Thomas: I want to come back to that point because we did some research on that, but is there a chicken-and-the-egg there? If you want to be that business partner, as finance, do you need to have the data first in the analytics culture? Does the analytics come first?
Schwenderman: It’s hard to say if it’s chicken or egg, but you definitely need a couple of key things. One is that you need access to the data, and historically that’s been a challenge for all parts of the organization.
The scorekeeper-type finance organization has always been able to say, “Hey. We have the right numbers.” As I’m sure your audience understands and can appreciate, there’s always a certain “check the box” function of finance that has occurred to say, “No, these are the numbers you need to use.”
What doesn’t always come along with that is an appreciation for the business and what the business needs. That’s where analytics really comes in. You can run models and you can run analysis on any set of information, and many organizations will do it ad nauseum. What we found is that there are only a few sets of key performance indicators that really drive performance of an organization.
Having a good way to get at that data, using more current technologies to be more predictive about that, and learn off of that, will drive greater results than trying to produce large masses of data that ends up sitting on a shelf.
Thomas: You mentioned technology as one of those drivers for getting to analytics. To come back to this research that [Workday] did, we surveyed about 700 or so finance organizations around the world. What we found was there was a pretty clear break, almost along generational lines, between finance folks who said, “We really view technology and what we can get from technology, the ability to use data in a more proactive way.” If you were under 40, or thereabouts, that was so clear to you that you needed technology in your corner, and it was a key driver. Those who were a little bit older than that didn’t see it as much of a barrier to where they were trying to go.
Does that resonate with you and what you’ve seen?
Schwenderman: Absolutely. The interesting thing on that is you have to have both generations. We’re seeing in our future of work, from research, there’s five generations in the workforce. We need to find ways to tap into the value of all of those generations.
Put that to the side for a second. On the data side, age 40 and under, and even age 25 and under, grew up native with technology. I have three daughters, they’re all under the age of 25, they don’t know a situation where they can’t get whatever information they want in the palm of their hand. Many people are very frustrated, they’re at home and they speak into a device and it orders their groceries for them, but they go to work and they have to sit in a cubicle, log in to a mainframe, or go through three different password protocols to get to a simple report.
You’re seeing that we have, in many finance organizations, built up these technologies and these architectures over time, which don’t make it easy to get at the information, and surely make it hard to consume.
The more senior generations, like myself, are used to processes and protocols that have been built up around ERP1.0. So, what you’re seeing is this real unique point in time where we have businesses that are running on digital operating models and finance functions that are not yet there.
So, analytics becomes a key way that finance supports a more digital operating model, but we don’t necessarily have the technologies and the talents yet aligned to that.
Thomas: When you think about your clients—you talk to CFOs—where do you advise that they start when they think about adopting more of that modern approach to technology, and how should they use it?
Schwenderman: The first thing is you have to have credibility in what it is you’re producing to begin with. You want to make sure that the client actually has reliable information and credibility in just the ongoing financial planning analysis and reporting.
So, activities that need to happen around that is to really understand what metrics—what KPIs—are used to support the business, that actually drive shareholder value, and then working forward to what needs to be produced on a regular basis for that.
Thomas: So, just to pause you for a second, is that really about, “This is the table stakes, and if I can’t establish the credibility as a finance officer, that we can do all of these things that the organization requires us to do. I maybe don’t have the credibility to go and do these new better things?”
Schwenderman: That’s exactly right. So it is. If you have that credibility, then you can progress faster, forward more quickly.
We’ve seen organizations do great things when they have that credibility. So, getting that credibility is important. One thing I particularly like about the Workday platform and what comes with it is when I have that there, and the organization agrees on the data model and what’s there, I know at any point in time everybody’s looking at the same version. That’s critical.
Then where we go from there is to make sure that there’s some sort of governance around that data; there’s a way to control that and move it forward. Then what we like to do is to have organizations start piloting some capabilities around analytics and predictive modeling. We like to say, “Plan, pilot, fail fast.” So, get out there, start doing it, see the results, have a feedback loop, and come home.
One particular example I like is in the financial planning and analysis—the annual planning, forecasting cycle. Lots of organizations have numerous analysts that use their gut and intuition to say, “Here’s what our monthly updated forecast is going to look like. This is what we’re going to do quarterly.” They use some historical drivers, and they roll that out.
We’ve actually worked on something we called “Precision View.” We built predictive modeling into that process. We’ve proven the ability within a 90-plus percent confidence level to drive a forecast, but then an analyst can come and provide that insight, if they know that there are unique business events coming up. So, you take a large chunk of the menial busywork out of the forecast process, and turn the analyst into that true partner, the true insight, and, once that model is built out and working, now I can add in artificial intelligence and machine learning to make that model actually improve upon itself as it gets more data.
Thomas: One of the things that we hear a lot in the broader conversation around some of these technologies, is they’re going to take away jobs, and they’re going to make people obsolete. The way you just described it actually elevates the role of that FP&A person.
Schwenderman: Yes. Much of our human capital work has been specifically around the future of work and what it’s going to be. We’ve found in our studies, and in our work with clients, exactly that—it’s the changing nature of work, not the elimination of jobs.
We recently released something called “Finance 2025,” a thought piece. It looks at what the work of finance is going to be like in the future. Leveraging blockchain and predictive analytics, automation, and whatnot. The thing that I hear most from CFOs when I go around and talk about that is, “How do I get there with my organization? What are the steps that need to happen?” They’re not challenging whether or not those technologies are impacting them and whether or not you’re going to have—not quite touchless, but near touchless—transaction processing. They’re not questioning whether they need predictive modeling in real time. What they really don’t know is, “Where do I get the talent today, that has that vision, that can help me get there faster than my competition?”
Thomas: Yeah. That’s a great segue to that topic of talent, that the jobs we have today may not be the jobs that we have in the future. The skills and the talent that people have in their organizations are almost assuredly going to change as these technologies come onboard.
What has your research shown in terms of where are we in closing that talent gap? What are the biggest opportunities out there?
Schwenderman: For finance specifically, one of the biggest gaps is really the difference between what I’ll say is the doer roles and the insight roles. Many organizations have folks in all of those functions, but at a disproportionate volume to the doer roles. Finance thought it was changing that when it went through outsourcing.
Now finance is going back to insourcing, and using process automation and blockchain, or analytics as a service, finance as a service, accounting as a service. How do we bridge that gap of having the majority of my organization measure its value and performance by executing quickly and efficiently, to being able to measure it by providing insights, being a partner, working well in teams, and whatnot. That’s kind of where we’re seeing the gap.
What we’re seeing clients, controllers, and CFOs do is start to look, reach out, and actually bring data scientists into their organization. Reach out to universities and say, “Look, you’re not doing a good job producing the skills that I need today. So, we need to partner, and you need to figure out how to get me more of these resources.”
It’s CFOs and CIOs working together to really understand where that new boundary is in terms of who plays what role. So, the CIOs are, maybe, contributing a bit more to the function than before.
Thomas: Yeah. That notion of whether schools producing the skills that people need. We’re here at Workday Rising, you can hear the background noise, and one of the topics that we’ve been talking about this week is that notion of how universities shift what they’re teaching students—not just the technical skills, because those are changing all the time. What I do in my job is very different than what I would’ve learned when I was in school. But those soft skills, and the ability to collaborate, are always going to be important. Maybe more so, as there’s more automation around some of those doer tasks, as you were talking about.
Schwenderman: Certainly, we’re a huge employer of campus graduates ourselves. Clearly those skills, as you mentioned, around the ability to have some of the softer skills, and the consultative skills, to go with the core. Don’t get me wrong. There isn’t a controller and a CFO who is going to want someone being in their accounting function that doesn’t know accounting.
How do you get them exposed early to what process automation can do? If you use Workday in your organization, what should they be looking for in the community? What should they be talking to their peers and colleagues about in terms of asking you to build machine learning into different parts that are very specific and give insight?
The other thing that I think finance can take advantage of goes back to what, I mentioned about five generations in the workforce. There’s been some great work out there around mentorships, and combined teams with younger and more senior workers, and realizing that there’s institutional knowledge. There’s technical knowledge. There’s functional knowledge. There’s cultural and consultative skills. Putting more team- and project-oriented groups together.
Specific to the analytics topic, which worked incredibly well for some clients, is that they will take an integrated, “black ops-type” team, put them off to the side, and say, “Okay, what we really need to do is understand better how we can drive revenue across this product line. We’re going to bring someone in, maybe from the marketing and products side. We’re going to bring in financial analysts. We’ll bring in someone who knows how to account for these transactions and some technologists. We’re going to figure out some predictive modeling capabilities. We’re now going to bring in some external data that we never brought in before. We’re going to start putting all that together.” Those are the things that finance can really get involved in and drive value because, at the end of the day, what we’re good at, as finance professionals, is understanding data. Being critical, in a good way, about it. Then helping the business apply it in a meaningful manner.
Thomas: That’s a good place to continue on the analytics and the predictive idea. We’re going to hear a lot this week about machine learning, artificial intelligence, predictive technologies. Where do you see that having business impact over the next 3 to 5 years, in finance?
Schwenderman: Certainly, we’ve seen it already be impactful in areas around fraud detection, controls anomalies. I think we’ll see it more embedded in core business processing. I think where that real value is, is what we’re talking about on the more analytics side of finance. I think you get more learnings around the things that are tangential to finance, or those other systems, and how they work with finance, or bringing in external data and bringing in unstructured information and marrying that up.
Thomas: That’s almost the data equivalent of that cross-functional team you were talking about.
Schwenderman: That’s exactly right. We had a client in the media space, involved in motion pictures. One of the things they did with external data and their internal structural data, is start looking at social media as they were putting out early PR and marketing around a new release. They started to look at social media scrapes around the project, around the stars that were in it, and see what was coming in favorable or unfavorable, which then turned around and informed the production volumes of the accessories, spinoffs, and all the other things in retail that they might make money off of, associated with the film.
Why I equate that to finance is because it’s hard to imagine an organization—I haven’t been able to imagine an organization—where large decisions around investment of capital and profitability on product lines doesn’t involve finance or doesn’t impact cash flow and profit-and-loss statements and message to the street.
The more we can add those external data sets, the more precise our predictive models can be. I can, in near real time, flow that through. Then I can make better resource allocations as a CFO. I can provide better guidance to the street. I can generate greater shareholder value.
Thomas: And you can do it with, as you mentioned earlier, the key performance indicators that really matter to those different functions within the business.
Schwenderman: Yes. What I see as being unique, at this point in time, is what I just described as something that an organization would go through but is not necessarily a new idea or need. What’s new is you’re seeing the prevalence of the capabilities in the technology growing so fast that I can really put that into place and set it up to almost run on its own in some time.
Again, I look at what the Workday platform provides to a CFO. Whether it’s the core financials, Adaptive Insights on top of that, Workday Prism Analytics, that combination of technologies is something that, not too long ago, I would’ve pitched to a CFO and it would’ve cost more money to build that out than the value of that solution.
What we’re seeing is the ability of the technologies, the cost to implement and then maintain and run those technologies, and keep them current—what I like to call “the no excuses time” for finance. You can’t tell me you can’t do it because it takes too long. You can’t tell me that you can’t bring in that information and manage it and govern it, because those capabilities are here. The only thing holding finance back is embracing that role and setting up its organizational agenda and its talent to be able to execute it.
Thomas: When you think about embracing that and taking that on, what’s the barrier there? Is it not knowing how that maps the strategy? What do you think holds people back?
Schwenderman: One, is a lot of CFOs were burned by investments in very large BI programs that became stale the day they went live. There’s a cautionary tale there for them.
Two, there’s a certain amount of control that you’re giving up if you’re moving to the things that you and I have been speaking about. If you’re moving to more business partnering and you’re pulling in marketing, product development, and it’s a collective, collegial group, it’s very different than you managing all the data and all the data flow, and no one does anything until you produce the monthly or quarterly numbers.
So, that leads me into number three, which is concerns about it being the right numbers, and too soon. And if I’m making decisions in real time, or near real time, on ever-evolving models and analytics, is there a risk that we’re going to make the wrong decision? This is where the intuition does play. Do I have people who know enough about our business, enough about what we’re trying to do, to say, “Hey, that looks right or wrong.” You know, to raise a hand at the right time and say, “We might need to re-look at the algorithms.”
Thomas: Yeah. This notion of human judgment takes on a very interesting and almost heightened dimension when there’s more modeling, there’s more data, there’s more predictions, because someone still needs to look at that and say, “Does this make sense? Is this the way we want to go?”
Schwenderman: That’s right. I don’t think we’re ever going to get away from that, and I think that’s a good thing. What I would like to see us get back to is the point in time where finance was leading the charge, in many organizations, around the adoption of technology for the good of the organization. Particularly around efficiency and standardization. I think certainly in the last five years, maybe a little longer, the roles around the adoption of technology in an organization have changed where customer-facing functions are much more innovative and out there in adopting new technologies quickly to try and drive benefit. Finance has been very cautious. Part of that is trying to make a very clear business case.
How do I make a clear business case for doing some of this in finance? Your business case has to be based on something different than what ERP and Y2K was. That’s something I’d like to see finance do is just get back to really looking forward as to how technology can be used, not just differently in finance itself, but in an innovative way within the organization.
Thomas: Well, that’s all the time we have for today. I want to thank Matt Schwenderman from Deloitte for joining us on the Workday podcast. Matt, if people want to learn more where should they go?
Schwenderman: You can certainly go to our Deloitte site, www.Deloitte.com. We have some information out there on finance in a digital world. As I mentioned, we just released our Finance 2025 “Crunch time” piece. We have our whole “Crunch time” series for the CFO, because we consider this, obviously, to be a pivotal point in time for them. There’s a piece on blockchain. There’s a piece on analytics. That’s a piece on digital technologies. That’s a great place to start, and we’d be happy to talk to any and all folks in the audience about what we’re seeing.
Thomas: Great. Thank you for listening to the Workday podcast. If you’d like to hear more, please subscribe. I’m Greg Thomas, and thanks for listening.