 From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. Hey, welcome to theCUBE's coverage of IBM Think 2021. I'm Lisa Martin. Joining me next is Dave Marmer, the Vice President, offering management for the Cogniz Analytics, Planning Analytics and RegTech portfolios at IBM. Dave, welcome to the program. Thank you, Lisa. Thanks for having us today. So lots of change in the last year. That's an epic understatement, right? But I'm curious, some of the things that you've seen from a customer's perspective, how are they utilizing planning and reporting technology and analytics to adapt to such a disruptive market? Great question. The pandemic was truly a test for these organizations in terms of their resiliency and agility. But fortunately, our clients were able to leverage our planning and reporting technology to do several things. They were able to plan their financials to integrate and reset operational areas in planning. They were able to create multiple scenarios as disruptions continued to occur. And they were able to maintain confidence and insights for collaborative decision-making at truly an enterprise scale. They were easily able to increase the frequency of the planning process moving from quarterly to monthly to even daily for their operational areas, such as supply and sales. And this was really far reaching for customers like ranging from people like Barona who focuses on private employment to Vasan who is one of the largest bakeries in Europe and ancestry.com, which is the world's largest online family history resource. They're all were able to successfully navigate the radical changes in demand and in workflow and in cash flow. That's impressive considering things were in such a, and still are in somewhat state of flux, which is obviously different globally. You talked about the collaboration. That's one of the things that we saw so much change going on in the last year, but this dependence on technology to facilitate collaboration. Talk to me a little bit about how you've helped, maybe it's the same customers that you mentioned, be able to collaborate collectively across the organizations. So the concept that we follow, which is sort of this extending planning and analysis model is this concept of decisions, financial decisions even, or finance decisions being moved outside of the operational areas of the Office of Finance into the areas of supply chain, into sales, into workforce management. These all had to come together far more, actually in far more connected than they ever were before. Decisions that one organization was going to make was going to impact others. And they need to bring in additional exogenous data to kind of augment the decisions they were already doing. So it became very collaborative and hyper participation for the people closest to the decisions. Excellent. So if you, when you look at some of the things that have happened in the last year, what are some of your observations that kind of things that surprised you in terms of how companies have evolved their planning and their forecasting strategy in such a dynamic market? Well, the biggest surprise, and I guess it shouldn't be a surprise, but historical trends that they had been counting on for their planning activity, taking last year's activities and actuals and using those to plan out what would happen. Those were sort of out the window and data sources and drivers, new drivers to their business had to be considered. They hadn't had to deal with this in the past. Like our clients were kind of pleasantly surprised that they're moved to extended planning and analysis when planning is adopted outside of the office of finance stood up to the global disruption. For example, Ancestry had already adopted a enterprise planning platform as a reaction to phenomenal growth they experienced years back as they were first launching their DNA product. This put them in really good shape for what happened more recently. This allowed them to run multiple scenarios to the impact of their supply chain all the way through the labs and back to the clients. And so when the pandemic hit, the facilities were impacted, but they were able to make those adjustments accordingly and keep up a high level of customer service. So these seems like Ancestry was already in a really good position to be able to navigate some of the massive disruption that happened so quickly. How have you helped other customers that maybe weren't as far along to do that as well and to be able to forecast and plan in a dynamic time? So a customer like Visan, I mentioned they were one of Europe's largest bakeries, they live in a world of just ours. You're creating product that has a shelf life, a realistic shelf life and they have much demand changes for their facilities, but also to the stores and their frozen food products that they provide in addition to how they provide the daily fresh stuff that they do, they're very known for their rye bread and their sourdough, those type of things. But they had to make a lot of changes based on what they were seeing and taken to consideration even margins. So they've been evolving and taking more advantage of AI and augmenting their human intelligence in this way. They've been able to use various sophisticated algorithms with planning analytics to allow them to plan for things like energy consumption where they calculate the expected outside temperatures and the need for the facilities because where they are based in the Nordics, they face freezing temperatures where the facilities themselves have to be good so there's a lot of fluctuation and seasonality to that and so they need to adjust for that. They also really use this to take a look at the product life cycles that they had been using to get a better long-term estimate of what people would be buying instead of using human intuition because as they said, you can get sort of into this methodical radar listening model of looking at what had occurred in the past and then we're able to start to see things months earlier that they would have normally not been able to see if they'd not augmented their human intelligence with artificial intelligence. And I think the third thing they started to use was customer personal behavior where they actually were starting to see actual patterns of things that were changing and the expected propensity was changing for repeat purchases and cross-sell purchases and they're able to make adjustments on their offerings as a result. So if we talk about AI to augment human intelligence to empower decision-making, that's a great example of that that you talked about. What's the adoption been like that around different industries and different countries in the last year? So we see this universally happening that there's an adoption occurring. Certain industries are definitely moving faster. It's happening in the sales and operations planning area more so than the traditional places like the financial and planning analysis areas. So once you get into areas like supply chain and demand planning, we generally see retail and distribution, companies at a high adoption of this because of the sensitivity of making sure the right product is there at the right time. We see this near a customer service. We definitely see this as I mentioned in workforce analytics. This pandemic brought large disruption to people who had to exit the normal facilities and work in different alternative locations. And then this idea of how do we bring them back in a very managed way is a universal problem that everyone is facing and they're all starting to adopt that. So we're seeing adoptions on many of these things across all the different industries. But I see the ones I mentioned were certainly highly sensitive to the immediate problems that we all personally experienced. Right. In your opinion, based on just what you've observed, what do you think the true value of integrated planning field by AI? What's the true business value there? It's a great question. I think in business terms, the predictive capabilities like the algorithmic forecasting is really helping companies more accurately forecast their demand. And while prescriptive capabilities like decision authorization help them determine the best way to meet that demand, typically decision optimization excels at developing scenarios and considering constraints such as time, prices, cost and capacities. And those are kind of pulled in to help augment the decisions. Whereas predictive capability really helps the forecast demand as an example. You know, demand changes by season, by day, by hour, the prescriptive capabilities like decision optimization help determine the best plan for meeting the demand. But if you think about the energy example I gave before, you know, you have to consider things like, is it hydro, is it coal, is it nuclear? What are those types of things that are involved? Because each method has a different cost and a different capacity. So they kind of work together in that way. When you're having customer conversations, I'm curious what the perspective is of customers understanding the obvious business value of integrating AI with integrated planning. Is that something that they get right away? What kinds of questions do they have for you? So, again, I think they understand the concept of scenario planning and the fact of building different scenario modeling. I think what they're getting accustomed to is the superpower that we get to augment these humans with and to work against their intuition. We've seen this time and time again where project planning for, you know, one of our customers who manages, you know, on behalf of the government, certain projects that they would look at and say, if it wasn't for AI, we wouldn't have detected these issues in some of the project scope because we look at managing them in a certain way based on historical patterns. So it's, you almost have to unlearn the historical patterns, that's how to accept what the data is telling you and you're really matching probabilistic and deterministic information together to get a more accurate and informed decision to help you move and progress further. So for businesses, I'm curious to get your advice here for companies that are in this data flex as we all are and varying degrees of that across the globe, what advice do you have for those companies that are looking into utilizing planning and reporting technology to really fine tune their business performance, but they don't really know where to start? Yeah, so from a very high level, the advice I would say is first, you got to examine your current planning process and really identify what's working well and what business questions need to be answered. Then you have to understand that planning is primarily driver-based and because it's driver-based, you really have to understand and take a look at your current financial reports, see what's really making up the bulk of your business, what's really driving revenue, what's really driving expenses and really focus it on the drivers that have material impact, probably that 80-20 rule. What is 80% of our cost and revenues coming from? And then you need to understand the level of granularity that you need in your data to really develop the appropriate values that you wanna plan against and set those targets and you should refer to the existing spreadsheets. They have lots of value. Just understand the sources of data, the calculations that get used, what's effective and not effective across the different functions and how they link together. And then you really need to determine your planning horizon. You need to understand who's gonna be contributing to the plan who hasn't been doing this before because you want people closer to the processes and the decisions to do that. And what's the frequency? As I mentioned, people moved from quarterly to monthly as a matter of fact in a rolling forecast and they started moving to daily and you gotta understand when do you recommend this kind of a model for what businesses and how much attention do you wanna give to those plans on a regular basis? One more question for you, Dave. When you're in those customer conversations, I'm curious, is this a C-level conversation now in terms of, hey, we need to be able to utilize AI and predictive for planning technology and reporting technology? Is that elevated in conversation within the organization? So yes, the pandemic has opened up and just disruptions in general have opened up the conversation around the importance of better planning and business continuity and building resilience into an organization. That is a boardroom conversation that's very important. So it is definitely raised up into that level as planning starts to sprawl outside of just the office of finance into these operational areas, those line of business executives are getting very involved in saying, we need to plan to perform and setting that conversation up and using these type of new technologies and capabilities that we're kind of replacing what can't be automated by human beings, right? Or just can't be done with the amount of manual work involved. And we see this today, just the amount of sheer number of data, the amount of volume of, and the amount of data intersections that have to occur, you need the capabilities of something like plan to lend it to Watson to deliver something like that. Awesome. Well, Dave, thanks so much for joining me today, sharing what you've seen in the last year and how some of the customers have been very successful at adapting to a pretty dynamic time. We appreciate you coming on the show. Thank you very much. I appreciate it, Lisa. For Dave Marmer, I am Lisa Martin. You're watching theCUBE's coverage of IBM Think.