T minus 3, 2, 1, 0, ignition. Lift off.

T minus 3, 2, 1, 0, ignition. Lift off.

Remember when Jeff Bezos made headlines, not because of his personal space race with other billionaires, but for telling employees that one day Amazon would fail because the lifespan of most large companies tends to be just 30 years?

Remember when Jeff Bezos made headlines, not because of his personal space race with other billionaires, but for telling employees that one day Amazon would fail because the lifespan of most large companies tends to be just 30 years?

I guess, in the digital era, you could say it might be even 10 years, so maybe just Bezos is wrong. But from an insurance perspective, 173 years and going strong, so I guess we're here to keep our promises for a long time,

I guess, in the digital era, you could say it might be even 10 years, so maybe just Bezos is wrong. But from an insurance perspective, 173 years and going strong, so I guess we're here to keep our promises for a long time,

That's Nimesh Mehta, the Senior Vice President and Chief Information Officer of a company that's now in its sixth 30- year cycle. National Life Group originally called National Life Insurance was founded in Vermont in 1848. Forget COVID- 19, it had customers that were victims of the Spanish influenza pandemic in 1918. It had customers on the Titanic. The company hasn't just survived the passing of time, it is thrived. And while it might seem odd to be talking about the past on a podcast that's firmly focused on the future, there are lessons to be learned as we consider the innovation and creativity that empowers companies to be successful over the long term. Sure, being bold and creative can lead to failures and missteps, but as a beloved American actress once said, " If I had to live my life again, I'd make the same mistakes, only sooner."

That's Nimesh Mehta, the Senior Vice President and Chief Information Officer of a company that's now in its sixth 30- year cycle. National Life Group originally called National Life Insurance was founded in Vermont in 1848. Forget COVID- 19, it had customers that were victims of the Spanish influenza pandemic in 1918. It had customers on the Titanic. The company hasn't just survived the passing of time, it is thrived. And while it might seem odd to be talking about the past on a podcast that's firmly focused on the future, there are lessons to be learned as we consider the innovation and creativity that empowers companies to be successful over the long term. Sure, being bold and creative can lead to failures and missteps, but as a beloved American actress once said, " If I had to live my life again, I'd make the same mistakes, only sooner."

Welcome to Future Enterprise, a podcast series proudly presented by the Thought Leaders of IDC. I'm your host, Joe Pucciarelli, Group Vice President and IT Executive Advisor. On this episode, I'm excited to explore a subject area that's aimed at helping organizations become digitally enabled, resilient, decision- making machines, the Future of Operations. Through most of the last half century, the operations of companies across most industries from manufacturing to retail were driven by two things, increasing efficiency and reducing waste, but going forward, we believe it's critical for companies to use digital capabilities, to build more resilient decision- making frameworks to meet shifting market demands, unpredictable global events and other disruptions. We'd like to think of it as building a digital house of resiliency that would make this man one of the main architects here at IDC. Kevin Prouty is Group Vice President, Energy and Manufacturing Insights. Kevin, welcome to the podcast.

Welcome to Future Enterprise, a podcast series proudly presented by the Thought Leaders of IDC. I'm your host, Joe Pucciarelli, Group Vice President and IT Executive Advisor. On this episode, I'm excited to explore a subject area that's aimed at helping organizations become digitally enabled, resilient, decision- making machines, the Future of Operations. Through most of the last half century, the operations of companies across most industries from manufacturing to retail were driven by two things, increasing efficiency and reducing waste, but going forward, we believe it's critical for companies to use digital capabilities, to build more resilient decision- making frameworks to meet shifting market demands, unpredictable global events and other disruptions. We'd like to think of it as building a digital house of resiliency that would make this man one of the main architects here at IDC. Kevin Prouty is Group Vice President, Energy and Manufacturing Insights. Kevin, welcome to the podcast.

Thanks Joe. And that's a pretty good foundation to lay for Future of Operations. I appreciate that.

Thanks Joe. And that's a pretty good foundation to lay for Future of Operations. I appreciate that.

Kevin, I can't wait to chat more with Nimesh but first maybe you can elaborate a little more on the Digital House of Resiliency before we dive into the conversation.

Kevin, I can't wait to chat more with Nimesh but first maybe you can elaborate a little more on the Digital House of Resiliency before we dive into the conversation.

So I think that the critical component is connecting operations more tightly with the market or the customer. Operations in most industries, not all but most industries, tends to run somewhat loosely linked to customers and markets and it takes a fairly significant change to force the change back through to operations. And part of that is around the decision- making process. So, I talk about this a lot in one of my reports around middle managers and how they sometimes slow down and alter the decision- making process, unless you're in a really resilient organization and part of that is how data moves around. And so, one of our core concepts is operational data governance and how data flows up and down in the organization through middle management and the key part of that is something we call a resilient decision- making framework. And that's something that we talk about a lot in Future of Operations.

So I think that the critical component is connecting operations more tightly with the market or the customer. Operations in most industries, not all but most industries, tends to run somewhat loosely linked to customers and markets and it takes a fairly significant change to force the change back through to operations. And part of that is around the decision- making process. So, I talk about this a lot in one of my reports around middle managers and how they sometimes slow down and alter the decision- making process, unless you're in a really resilient organization and part of that is how data moves around. And so, one of our core concepts is operational data governance and how data flows up and down in the organization through middle management and the key part of that is something we call a resilient decision- making framework. And that's something that we talk about a lot in Future of Operations.

Perfect. Thanks Kevin. Now let's get to our guest who we've heard from briefly at the beginning of the episode. It's my pleasure now to formally introduce a man who's been promoting new ways of thinking at one of the oldest mutual life insurance companies in the U. S. Nimesh Mehta is the Future of Operations at National Life Group. Nimesh, thanks so much for being here.

Perfect. Thanks Kevin. Now let's get to our guest who we've heard from briefly at the beginning of the episode. It's my pleasure now to formally introduce a man who's been promoting new ways of thinking at one of the oldest mutual life insurance companies in the U. S. Nimesh Mehta is the Future of Operations at National Life Group. Nimesh, thanks so much for being here.

Thank you, Joe. Thanks for having me. We'd love to chat about the Future of Operations, pretty near and dear to our heart and Santee.

Thank you, Joe. Thanks for having me. We'd love to chat about the Future of Operations, pretty near and dear to our heart and Santee.

Gentlemen, we have a lot of ground to cover, but, Nimesh, before we get rolling let's talk more about the pandemic, COVID, that is not the Spanish flu. A lot of organizations had to learn to do a lot of things differently over the last 18 months can you talk about some of the changes you and your team made?

Gentlemen, we have a lot of ground to cover, but, Nimesh, before we get rolling let's talk more about the pandemic, COVID, that is not the Spanish flu. A lot of organizations had to learn to do a lot of things differently over the last 18 months can you talk about some of the changes you and your team made?

Absolutely. Let's start about decision- making. Whenever you're making a decision, whether we entered into the pandemic or not, a decision is usually made in pretty much fuzziness, I call it. And fuzziness is because things are just not clear when you're going to make a decision, because if you had everything and you really wouldn't be making a decision, you'd just be moving forward. So decision to me is really realizing two things when we're trying to do it. It's about values and experience. Values are something each individual has been brought up with, it's ingrained in us and experience is something we've learned over time. And sometimes they conflict with each other when you're trying to make a decision. So as we make decisions at National Life, it's not about perfection, it's leaning into a concept and it's called a Japanese concept, it's wabi- sabi. It's about finding perfection in imperfection As we tied to make decisions. Three things I'll leave you with as we talk about the future of decision- making. One, find a clear problem. If you can't figure out what you're trying to solve, you're probably not going to be able to make a decision. Two, it's not about who is right but what is right. We tend to focus on the who a lot and we tend to avoid what is right. And that is the important part of asking the right question, building teams with a lot of diversity of thought and promoting intellectual wrestling so we can get to the right decision. And finally, look back and learn not to second- guess yourself. You make a decision, you learn from it, make a judgment call, and then you lean forward and you move on.

Absolutely. Let's start about decision- making. Whenever you're making a decision, whether we entered into the pandemic or not, a decision is usually made in pretty much fuzziness, I call it. And fuzziness is because things are just not clear when you're going to make a decision, because if you had everything and you really wouldn't be making a decision, you'd just be moving forward. So decision to me is really realizing two things when we're trying to do it. It's about values and experience. Values are something each individual has been brought up with, it's ingrained in us and experience is something we've learned over time. And sometimes they conflict with each other when you're trying to make a decision. So as we make decisions at National Life, it's not about perfection, it's leaning into a concept and it's called a Japanese concept, it's wabi- sabi. It's about finding perfection in imperfection As we tied to make decisions. Three things I'll leave you with as we talk about the future of decision- making. One, find a clear problem. If you can't figure out what you're trying to solve, you're probably not going to be able to make a decision. Two, it's not about who is right but what is right. We tend to focus on the who a lot and we tend to avoid what is right. And that is the important part of asking the right question, building teams with a lot of diversity of thought and promoting intellectual wrestling so we can get to the right decision. And finally, look back and learn not to second- guess yourself. You make a decision, you learn from it, make a judgment call, and then you lean forward and you move on.

So, Kevin, as Nimesh was speaking, I could see smiling and nodding your head, obviously, a lot of what he said resonated with your experience.

So, Kevin, as Nimesh was speaking, I could see smiling and nodding your head, obviously, a lot of what he said resonated with your experience.

Yeah. I think Nimesh's comments about how you make those decisions, the value in the experience, I think that experience piece is part of what we talk about a lot in that decision- making framework. One of the issues, and I talked about this and Nimesh mentioned it, and that's the personal bias that is you're applying not just values as part of the business, you have personal values as well. And the what's right versus who's right. You tend to find a lot of middle management, there's a lot of who's right. The what's right tends to be more forward- looking senior managers and you try to promote people that are more concerned about what's right versus who's right. But you still end up with those who's right. They have a lot of institutional knowledge as well and that's where that personal bias sometimes gets applied. I always think back to one of my first jobs as a lowly engineer in a plant, my boss told me... If there was something going on in the plant and we didn't like the decision we thought was going to get made about it, he would always tell me, " Send it to my boss' boss, because it will sit on his desk for six months so that by the time the decision is made, it's out of date and it's moot." So I think that comment about... And it's also the battles about who's right, but that's been my experience in that decision making framework.

Yeah. I think Nimesh's comments about how you make those decisions, the value in the experience, I think that experience piece is part of what we talk about a lot in that decision- making framework. One of the issues, and I talked about this and Nimesh mentioned it, and that's the personal bias that is you're applying not just values as part of the business, you have personal values as well. And the what's right versus who's right. You tend to find a lot of middle management, there's a lot of who's right. The what's right tends to be more forward- looking senior managers and you try to promote people that are more concerned about what's right versus who's right. But you still end up with those who's right. They have a lot of institutional knowledge as well and that's where that personal bias sometimes gets applied. I always think back to one of my first jobs as a lowly engineer in a plant, my boss told me... If there was something going on in the plant and we didn't like the decision we thought was going to get made about it, he would always tell me, " Send it to my boss' boss, because it will sit on his desk for six months so that by the time the decision is made, it's out of date and it's moot." So I think that comment about... And it's also the battles about who's right, but that's been my experience in that decision making framework.

Kevin, as you're talking about a six- month decision- making timeframe, I'm reminded of the conversation we had in the green room. Nimesh was talking about how National Life Insurance put together a new policy, electronic policy product in 23 days during the pandemic.

Kevin, as you're talking about a six- month decision- making timeframe, I'm reminded of the conversation we had in the green room. Nimesh was talking about how National Life Insurance put together a new policy, electronic policy product in 23 days during the pandemic.

I think Winston Churchill said this, is, " Don't let a good crisis go to waste." And sometimes it's about putting the Lego blocks of the pieces you have together and create something rather than trying to reinvent the wheel. So a lot of decision making and doing things fast is making sure you're adopting and using what you have. There's two camps and cultures. In most organizations, as Kevin was just talking about, you've got the camp that goes, ready, aim, aim, aim, aim, aim, aim, aim, fire. By the time they fire their target's so long gone it's probably worthless. That's the six- month decision- maker. Then you've got the pendulum that swings to the other side that goes ready, fire, aim. And they're in such a hurry to do something that they don't even know what they're trying to solve for. So bringing that right balance between the two, and I believe in the pandemic, the most important part, at least in our organization, was the ability to get scrappy to get things done, the ability to not shoot for perfection, but to find that imperfection and get it out there and get it scrappy. Now, scrappy is not a standard operating procedure that I lean into. However, when you're on 10- yard and goal, do you want to punt or do you want to try for that two- point conversion?

I think Winston Churchill said this, is, " Don't let a good crisis go to waste." And sometimes it's about putting the Lego blocks of the pieces you have together and create something rather than trying to reinvent the wheel. So a lot of decision making and doing things fast is making sure you're adopting and using what you have. There's two camps and cultures. In most organizations, as Kevin was just talking about, you've got the camp that goes, ready, aim, aim, aim, aim, aim, aim, aim, fire. By the time they fire their target's so long gone it's probably worthless. That's the six- month decision- maker. Then you've got the pendulum that swings to the other side that goes ready, fire, aim. And they're in such a hurry to do something that they don't even know what they're trying to solve for. So bringing that right balance between the two, and I believe in the pandemic, the most important part, at least in our organization, was the ability to get scrappy to get things done, the ability to not shoot for perfection, but to find that imperfection and get it out there and get it scrappy. Now, scrappy is not a standard operating procedure that I lean into. However, when you're on 10- yard and goal, do you want to punt or do you want to try for that two- point conversion?

So, how do you capture the new ideas? How do you create the organizational culture that embraces the change in the creative destruction, that rapid decision- making process embodies?

So, how do you capture the new ideas? How do you create the organizational culture that embraces the change in the creative destruction, that rapid decision- making process embodies?

The thought behind it is pretty simple. I think the execution around the culture, I think takes years to develop. First step is the who versus the what is right. Organizations that are probably more hierarchical are probably still hung up on the, " Who is right?" When you have the "What is right?" And that usually comes with seasoned leadership. So how do you take that and embedded it into an organization and maybe even a large organization? If the culture of the organization is one of vulnerability and humility, which means you just don't know everything and it's okay not to know. Leaders that can show that they understand that it's okay for them not to know everything and they put on their listening hat first before they open their mouth, are organizations that usually have the ability to make these decisions. The second thing which I've talked a lot about is the concept of intellectual wrestling. When you're in a room, you leave your stripes at the door and you leave your ego at the door. Get into the room and you put the right concepts out there to make a decision, you wrestle it down to the ground and so you're making the right decision for the organization. It could be a knock- down drag- out fight in the room and then you can walk out and have dinner with a person because that was not the intent of the conversation. But to generate that culture, that's not something you flip the switch off.

The thought behind it is pretty simple. I think the execution around the culture, I think takes years to develop. First step is the who versus the what is right. Organizations that are probably more hierarchical are probably still hung up on the, " Who is right?" When you have the "What is right?" And that usually comes with seasoned leadership. So how do you take that and embedded it into an organization and maybe even a large organization? If the culture of the organization is one of vulnerability and humility, which means you just don't know everything and it's okay not to know. Leaders that can show that they understand that it's okay for them not to know everything and they put on their listening hat first before they open their mouth, are organizations that usually have the ability to make these decisions. The second thing which I've talked a lot about is the concept of intellectual wrestling. When you're in a room, you leave your stripes at the door and you leave your ego at the door. Get into the room and you put the right concepts out there to make a decision, you wrestle it down to the ground and so you're making the right decision for the organization. It could be a knock- down drag- out fight in the room and then you can walk out and have dinner with a person because that was not the intent of the conversation. But to generate that culture, that's not something you flip the switch off.

Kevin, you were talking about a culture. You were part of where the decision- making seemed to have ossified a bit. How do you contrast what Nimesh is discussing and what you experienced?

Kevin, you were talking about a culture. You were part of where the decision- making seemed to have ossified a bit. How do you contrast what Nimesh is discussing and what you experienced?

Well, I think going back to some of these more hierarchical organizations that Nimesh talked about, part of it isn't just who's right, it's who has the right data sometimes, or what is the right data. I've seen a number of decision- making tiger teams, where we're focused on a very specific thing we've got to fix or continuously improve. Six people show up in the meeting and all six of them have their own version of the data. So then it becomes the intellectual wrestling match becomes not who has data, but who has data that's most relevant, most timely. I still see that challenge. We have one client, the comment they made was they built 5, 000 AI models running in all their plants but the problem they're having is they're all using different data they're not all consistent in the data they're using. So this model over here, it might be trying to do the exact same thing, but because it's based on different data, it's actually giving you a different outcome. When I talk about the Future of Operations, when I talk about resilient, decision- making the next words out of my mouth are always operational data governance. And that is giving people the right data. I actually, Lean a plant that was completely dysfunctional and when we went through Lean, what we realized is exactly that we were wasting a lot of time going looking for data. So we invested a lot of money in making sure the data was available, consistent and we had a couple of people responsible for making sure that data stayed that way. It was kind of the embryo for what I now call digital engineering and that is, a dedicated team who are focused on data governance, security, things like that so that operations has this consistency around its technology.

Well, I think going back to some of these more hierarchical organizations that Nimesh talked about, part of it isn't just who's right, it's who has the right data sometimes, or what is the right data. I've seen a number of decision- making tiger teams, where we're focused on a very specific thing we've got to fix or continuously improve. Six people show up in the meeting and all six of them have their own version of the data. So then it becomes the intellectual wrestling match becomes not who has data, but who has data that's most relevant, most timely. I still see that challenge. We have one client, the comment they made was they built 5, 000 AI models running in all their plants but the problem they're having is they're all using different data they're not all consistent in the data they're using. So this model over here, it might be trying to do the exact same thing, but because it's based on different data, it's actually giving you a different outcome. When I talk about the Future of Operations, when I talk about resilient, decision- making the next words out of my mouth are always operational data governance. And that is giving people the right data. I actually, Lean a plant that was completely dysfunctional and when we went through Lean, what we realized is exactly that we were wasting a lot of time going looking for data. So we invested a lot of money in making sure the data was available, consistent and we had a couple of people responsible for making sure that data stayed that way. It was kind of the embryo for what I now call digital engineering and that is, a dedicated team who are focused on data governance, security, things like that so that operations has this consistency around its technology.

So Nimesh, how do you, as part of the process within your organization, have a uniform data set so that people are making decisions from the same starting point. Is that something you focus on?

So Nimesh, how do you, as part of the process within your organization, have a uniform data set so that people are making decisions from the same starting point. Is that something you focus on?

Absolutely. In fact, Kevin just said this and I couldn't agree with him more, data is a big part of decision- making. I've been in meetings at least some time back where people would show up with five different reports that pretty much had the same data set that said five different things. How do you make a decision when your data is telling you and leading you in five different directions, it becomes an instinctive gut- feel because you don't know what it's telling you. The second concept in that room, which also is interesting, and people say data driven decision- making you have to learn to do that. A lot of times you'll notice people make a decision and then go look for data to prove their decision. It's not about looking at the data to figure out what decision they need to make. So in the organization, if you truly want to be a operationally resilient organization, you have to have a single source of the truth. So you can't have people running around and picking off the datasets that they think are valuable. You have to use the consistent datasets, everybody looking at the same thing at the same time in order to make a decision because time stamping is also really important in operations. If I'm looking at the same data set from three days ago, it's a very different story than probably two minutes ago.

Absolutely. In fact, Kevin just said this and I couldn't agree with him more, data is a big part of decision- making. I've been in meetings at least some time back where people would show up with five different reports that pretty much had the same data set that said five different things. How do you make a decision when your data is telling you and leading you in five different directions, it becomes an instinctive gut- feel because you don't know what it's telling you. The second concept in that room, which also is interesting, and people say data driven decision- making you have to learn to do that. A lot of times you'll notice people make a decision and then go look for data to prove their decision. It's not about looking at the data to figure out what decision they need to make. So in the organization, if you truly want to be a operationally resilient organization, you have to have a single source of the truth. So you can't have people running around and picking off the datasets that they think are valuable. You have to use the consistent datasets, everybody looking at the same thing at the same time in order to make a decision because time stamping is also really important in operations. If I'm looking at the same data set from three days ago, it's a very different story than probably two minutes ago.

We've been talking about single sources of truth for 20 years. We've gotten maybe not to the single source of truth, but we've narrowed down the number of places the truth exists, do you think we're at a point where we are able to get to that single source of truth?

We've been talking about single sources of truth for 20 years. We've gotten maybe not to the single source of truth, but we've narrowed down the number of places the truth exists, do you think we're at a point where we are able to get to that single source of truth?

I think we're getting there. The important part that we have is created data governance frameworks. So data started first, governance came second and it always happens in the world this way. When we first built cars, we weren't worried about traffic lights and seatbelts. They came later when there was actually a volume of cars and we were worried about accidents. So in this case, I think the data came first and the single source wasn't there. Now that we have the volumes figured out and there's enough cars and data on the road now we're trying to figure out all the governance behind it to make sure we've got the traffics lights and the seatbelts in place. And that brings the single source of the truth. So getting people to make sure that they're using the right sources, because not everything is centralized in the right way, but getting it to the right place so they're all using the same thing at the same time.

I think we're getting there. The important part that we have is created data governance frameworks. So data started first, governance came second and it always happens in the world this way. When we first built cars, we weren't worried about traffic lights and seatbelts. They came later when there was actually a volume of cars and we were worried about accidents. So in this case, I think the data came first and the single source wasn't there. Now that we have the volumes figured out and there's enough cars and data on the road now we're trying to figure out all the governance behind it to make sure we've got the traffics lights and the seatbelts in place. And that brings the single source of the truth. So getting people to make sure that they're using the right sources, because not everything is centralized in the right way, but getting it to the right place so they're all using the same thing at the same time.

You know, Nimesh you were talking about evolving the culture to actually use the data to make decisions. And when you said that it brought me back to a point in my career when I was in the financial services industry, I've been asked to do a financial analysis on a multi- billion dollar problem and I had toiled away and built all these models and analyzed it from 72 different perspectives and there I am in front of the management committee making my presentation, I get through the whole presentation and the senior person in the room looks at me and says, " So, Joe, what does your gut tell you?" I was speechless because I had just done this exhaustive logical analysis and the person was asking me for my opinion, which I guess was a reasonable question, but it brought me back to the moment about when do you use the data to inform the decisions as opposed to your intuition?

You know, Nimesh you were talking about evolving the culture to actually use the data to make decisions. And when you said that it brought me back to a point in my career when I was in the financial services industry, I've been asked to do a financial analysis on a multi- billion dollar problem and I had toiled away and built all these models and analyzed it from 72 different perspectives and there I am in front of the management committee making my presentation, I get through the whole presentation and the senior person in the room looks at me and says, " So, Joe, what does your gut tell you?" I was speechless because I had just done this exhaustive logical analysis and the person was asking me for my opinion, which I guess was a reasonable question, but it brought me back to the moment about when do you use the data to inform the decisions as opposed to your intuition?

I think that's such a great question, Joe, because as we walk into so many of these realistic situations, data's data but data is not always complete. I think the pendulum swings from all the way to the left to all the way the right and there's two extremes where people make decisions without any data on total gut and the others lean so much on the data that their gut's telling them, " Don't do it," and they'll still do it. When you look at the data and it's telling you a story and your gut's, not agreeing with you, you've got to be able to listen to others to be able to make that decision. Because when you try to make a decision in a vacuum and in an isolation, it probably ends up being the wrong one.

I think that's such a great question, Joe, because as we walk into so many of these realistic situations, data's data but data is not always complete. I think the pendulum swings from all the way to the left to all the way the right and there's two extremes where people make decisions without any data on total gut and the others lean so much on the data that their gut's telling them, " Don't do it," and they'll still do it. When you look at the data and it's telling you a story and your gut's, not agreeing with you, you've got to be able to listen to others to be able to make that decision. Because when you try to make a decision in a vacuum and in an isolation, it probably ends up being the wrong one.

Well, yeah. I think this whole concept around data... So this is something I talk about almost every single engagement I have with our end- user clients. I call them blind spots. One of the worst decisions you can make is making a decision based on incomplete data. There are people that will wait forever for all the data to show up and then there's people who will just make decisions on one piece of data they got. And this is what I talked about with the resilient decision- making framework is, that needs to be your continuous improvement program, closing up those blind spots.

Well, yeah. I think this whole concept around data... So this is something I talk about almost every single engagement I have with our end- user clients. I call them blind spots. One of the worst decisions you can make is making a decision based on incomplete data. There are people that will wait forever for all the data to show up and then there's people who will just make decisions on one piece of data they got. And this is what I talked about with the resilient decision- making framework is, that needs to be your continuous improvement program, closing up those blind spots.

Well, guys, I'm already inspired and we've only just begun. Please stay with us. After a quick break, we'll discuss whether it's time to retire the concept of right- to- left planning. I'm Joe Pucciarelli and you're listening to Future Enterprise, a forward- thinking podcast series proudly presented by the thought leaders at IDC. It's about helping your organization succeed by arming you with insights about how data and technology are reshaping the workplace, applied intelligence and software. Please don't forget to like or follow wherever you get your podcasts. We're also on Twitter and LinkedIn, just search for IDC.

Well, guys, I'm already inspired and we've only just begun. Please stay with us. After a quick break, we'll discuss whether it's time to retire the concept of right- to- left planning. I'm Joe Pucciarelli and you're listening to Future Enterprise, a forward- thinking podcast series proudly presented by the thought leaders at IDC. It's about helping your organization succeed by arming you with insights about how data and technology are reshaping the workplace, applied intelligence and software. Please don't forget to like or follow wherever you get your podcasts. We're also on Twitter and LinkedIn, just search for IDC.

Today we're talking about the Future of Operations with IDCs Kevin Prouty and Nimesh Mehta, the senior vice president and CIO at National Life Group. Next, let's dig into the concept of right- to- left planning. Nimesh. Do you believe it's time to retire that concept or replace it with a more relevant one?

Today we're talking about the Future of Operations with IDCs Kevin Prouty and Nimesh Mehta, the senior vice president and CIO at National Life Group. Next, let's dig into the concept of right- to- left planning. Nimesh. Do you believe it's time to retire that concept or replace it with a more relevant one?

I think it's a different concept today, and I'm not going to get into the world of agile planning because that could be a full new podcast for you. Do you think about left- or- right and right- to- left planning? Both of those deal with figuring out a date and trying to backtrack from there to make something happen. And there's always two schools of thought, and I think there's applications for each in either place. If I'm building a town, I don't need to know everything about it and I can do left- to- right planning. I don't need to understand how every house needs to look and I can work left-to- right, and figure out as I go, learn as I go, fail as I go, fail forward, fail fast and build a city out. And as we mature, the city will start to look differently over time. On the flip side, the right- to- left planning, if I'm going to build a skyscraper I better know how many stories this thing is going to be before I pour the foundation because if I say it's going to be a 60- story building and I build 30 stories and I say, " Well, I need it to be a hundred- story building, I got to start all over again." So there's a left-to- right, there's a place for right- to- left. You got to figure out what it is that you're trying to accomplish. Again, I get back to what is the problem you're trying to solve?

I think it's a different concept today, and I'm not going to get into the world of agile planning because that could be a full new podcast for you. Do you think about left- or- right and right- to- left planning? Both of those deal with figuring out a date and trying to backtrack from there to make something happen. And there's always two schools of thought, and I think there's applications for each in either place. If I'm building a town, I don't need to know everything about it and I can do left- to- right planning. I don't need to understand how every house needs to look and I can work left-to- right, and figure out as I go, learn as I go, fail as I go, fail forward, fail fast and build a city out. And as we mature, the city will start to look differently over time. On the flip side, the right- to- left planning, if I'm going to build a skyscraper I better know how many stories this thing is going to be before I pour the foundation because if I say it's going to be a 60- story building and I build 30 stories and I say, " Well, I need it to be a hundred- story building, I got to start all over again." So there's a left-to- right, there's a place for right- to- left. You got to figure out what it is that you're trying to accomplish. Again, I get back to what is the problem you're trying to solve?

Kevin? Comments? Thoughts? Reactions?

Kevin? Comments? Thoughts? Reactions?

Well, I guess the way I look at it when you... This is part of resiliency and that is where the agility has to come in is your actual goal. You may set a goal, but you have to understand that the world isn't static and that goal has to be able to adjust, it has to be able to move, it has to be able to move left right up down, and you have to bring that agility to tracking that goal. I think that's part of this resilient decision- making process, is that it has to be ongoing and continuous. You can't just make a decision, leave the decision there and then move on to the next decision. You constantly have to be looking at, how are those decisions aligning? Do I need to go back and revisit that decision because something changed up ahead?

Well, I guess the way I look at it when you... This is part of resiliency and that is where the agility has to come in is your actual goal. You may set a goal, but you have to understand that the world isn't static and that goal has to be able to adjust, it has to be able to move, it has to be able to move left right up down, and you have to bring that agility to tracking that goal. I think that's part of this resilient decision- making process, is that it has to be ongoing and continuous. You can't just make a decision, leave the decision there and then move on to the next decision. You constantly have to be looking at, how are those decisions aligning? Do I need to go back and revisit that decision because something changed up ahead?

Any thoughts or comments or guidance to our listeners as to what's the appropriate time to shift, pivot?

Any thoughts or comments or guidance to our listeners as to what's the appropriate time to shift, pivot?

I don't think there's any guidance I can give you a that's set in stone. What it is is the process you have to have of constantly revisiting those decisions and being able to look back and see the impact. A big part of it is to be able to simulate or do scenario planning of what the outcome of that decision is going to be, or your projected outcome. That's where you need to be constantly adjusting and looking at what happens if I do this, what happens if I use this vendor who's a little more expensive, but much more reliable versus this vendor who was cheaper but less reliable. That's where you start to make those resilient decisions that are sometimes counter- intuitive. If you look at the typical procurement officer today, procurement, officer's typically going to say, " The cheaper one, and then we'll deal with the outcome, we'll have to be a little more agile in how we deal with it." But a more forward- thinking better planning procurement officer will actually collaborate with engineering, will collaborate with production and say, " Yeah, in the longterm, it's going to be better to work with that more reliable, more resilient supplier."

I don't think there's any guidance I can give you a that's set in stone. What it is is the process you have to have of constantly revisiting those decisions and being able to look back and see the impact. A big part of it is to be able to simulate or do scenario planning of what the outcome of that decision is going to be, or your projected outcome. That's where you need to be constantly adjusting and looking at what happens if I do this, what happens if I use this vendor who's a little more expensive, but much more reliable versus this vendor who was cheaper but less reliable. That's where you start to make those resilient decisions that are sometimes counter- intuitive. If you look at the typical procurement officer today, procurement, officer's typically going to say, " The cheaper one, and then we'll deal with the outcome, we'll have to be a little more agile in how we deal with it." But a more forward- thinking better planning procurement officer will actually collaborate with engineering, will collaborate with production and say, " Yeah, in the longterm, it's going to be better to work with that more reliable, more resilient supplier."

Kevin, if resources weren't a constraint, what additional digital capabilities would you advise other CIOs to explore right now?

Kevin, if resources weren't a constraint, what additional digital capabilities would you advise other CIOs to explore right now?

I would say as much as it's probably people expect it in every discussion about technology today in digital, as I think looking at AI and AI support tools around decision- making, I think companies are way under- invested in simulation and scenario planning at the tactical level. I think they do a pretty good job at the high end business level, but when you get to tactical decision- making and operations, I think there's a real lack of investment in those types of technologies and that digital capability. And I also think investing in the people that understand that and how they can use it is also lacking.

I would say as much as it's probably people expect it in every discussion about technology today in digital, as I think looking at AI and AI support tools around decision- making, I think companies are way under- invested in simulation and scenario planning at the tactical level. I think they do a pretty good job at the high end business level, but when you get to tactical decision- making and operations, I think there's a real lack of investment in those types of technologies and that digital capability. And I also think investing in the people that understand that and how they can use it is also lacking.

I think I completely agree with a lot of things. And I think one of the mistakes, when you talk about what advice can we give your listeners, I think people tend to put a stake in the ground when they do right-to- left planning and the path to that stake in the ground also is a concrete path. I think that's where the mistakes happen. We have to think about is even if you stuck the stake in the concrete or in semi- form jello, for that matter, the path to get to it has to continuously evolve over time. It doesn't mean that your end goal's changing but how you get there has to change. Because if you're not going to think about that continuously, and you're rigid in your approach, then you're probably not going to learn and you're probably going to end up either costing too much, picking the wrong tools, picking too many tools maybe, or just going about it the wrong way.

I think I completely agree with a lot of things. And I think one of the mistakes, when you talk about what advice can we give your listeners, I think people tend to put a stake in the ground when they do right-to- left planning and the path to that stake in the ground also is a concrete path. I think that's where the mistakes happen. We have to think about is even if you stuck the stake in the concrete or in semi- form jello, for that matter, the path to get to it has to continuously evolve over time. It doesn't mean that your end goal's changing but how you get there has to change. Because if you're not going to think about that continuously, and you're rigid in your approach, then you're probably not going to learn and you're probably going to end up either costing too much, picking the wrong tools, picking too many tools maybe, or just going about it the wrong way.

So Nimesh, I'd like to switch gears a little bit. You've spoken about the art of decision- making, our ability to gather and dissect data has grown by leaps and bounds in recent years, I'm wondering how your approach to decision- making, resilient decision- making has evolved along with it.

So Nimesh, I'd like to switch gears a little bit. You've spoken about the art of decision- making, our ability to gather and dissect data has grown by leaps and bounds in recent years, I'm wondering how your approach to decision- making, resilient decision- making has evolved along with it.

I think when we think about decision- making, we also always talk about data we talk about different parts of the decision process itself, but at the end of the day, a lot of decisions are made by humans in the space of the understanding they have of the problem with the data that they have in front of them, 80% directional, or maybe they're missing the 20%, don't know at that point in time. So they have to make that call. So my evolution of thought has been that you making a decision with the right people. And when I say the right people, it's the right mix of people at the right time with the right skillsets in the right place. That magic formula has to come through if you want to make a good decision.

I think when we think about decision- making, we also always talk about data we talk about different parts of the decision process itself, but at the end of the day, a lot of decisions are made by humans in the space of the understanding they have of the problem with the data that they have in front of them, 80% directional, or maybe they're missing the 20%, don't know at that point in time. So they have to make that call. So my evolution of thought has been that you making a decision with the right people. And when I say the right people, it's the right mix of people at the right time with the right skillsets in the right place. That magic formula has to come through if you want to make a good decision.

So, Kevin, what do you think? Talk about the distinction between science- based decisions and art- based decisions.

So, Kevin, what do you think? Talk about the distinction between science- based decisions and art- based decisions.

I think Nimesh is spot on with that. And I think it's something we, especially as technology analysts, we lose sight of, and that is that there is still a human factor here, no matter how much AI, no matter how much data you collect, you still have a human factor in making decisions, that's the whole point. Otherwise we'd have these companies full of robots. What Nimesh said about the skill- set is really, really important. I've been part of and I've led organizations where we don't have enough, we just call them just devil's advocates, but people who will step up and have no problem saying, " I don't think that makes sense, can we look at it a different way?" Coming back to the digital side of it, data is somewhat non- partisan but people look at the data differently. They each have their lens, they each have their own... Even how you incentivize your employees can bias how they're going to look at that data. So I think you always, as a leader, especially when you're getting the opinions from your people, you have to think of the perspective they're bringing to it. I mean, it sounds like you're having a giant psychotherapy session, but it's really, this is part of being a leader and that is having a little bit of empathy and putting people on the spot because you want their raw perspective on that data and no matter how much digital technology we put in place, you still need leaders who understand that.

I think Nimesh is spot on with that. And I think it's something we, especially as technology analysts, we lose sight of, and that is that there is still a human factor here, no matter how much AI, no matter how much data you collect, you still have a human factor in making decisions, that's the whole point. Otherwise we'd have these companies full of robots. What Nimesh said about the skill- set is really, really important. I've been part of and I've led organizations where we don't have enough, we just call them just devil's advocates, but people who will step up and have no problem saying, " I don't think that makes sense, can we look at it a different way?" Coming back to the digital side of it, data is somewhat non- partisan but people look at the data differently. They each have their lens, they each have their own... Even how you incentivize your employees can bias how they're going to look at that data. So I think you always, as a leader, especially when you're getting the opinions from your people, you have to think of the perspective they're bringing to it. I mean, it sounds like you're having a giant psychotherapy session, but it's really, this is part of being a leader and that is having a little bit of empathy and putting people on the spot because you want their raw perspective on that data and no matter how much digital technology we put in place, you still need leaders who understand that.

So, Nimesh, Kevin has just talked about the role of human beings in these new decision- making processes but given that some decisions are more valuable than others, how do you prioritize?

So, Nimesh, Kevin has just talked about the role of human beings in these new decision- making processes but given that some decisions are more valuable than others, how do you prioritize?

Prioritization, that's the name of the game and probably the most difficult thing we do as leaders all day long. Everybody's talking about this. Data's the new oil and there's the volumes of data. In fact, I was just reading about this and it's the most interesting stat I've ever read. We've created from the beginning of time until 2017, about five exabytes worth of data, whatever an exabyte is, that's a lot of data. And today we're creating that amount of data every two days, it's overwhelming. That's why the AI concepts and all of those other things are coming into play. Are we refining that data? If this is the oil, it needs to be refined to a point I can put it in the car and do something with it. I can't pump crude oil into my car and have the car run. And as you think about decision- making and we think about the humans, are we using humans at the right time to make the decision once we've exhausted the part that data can provide us versus going the other way around and trying to use the humans to parse through the data? We've got the automation, we've got the digital, we've got the concepts, let it do its thing. And again, machine learning, there's a very key word in there, it's called learning. We forget that. We think about machine and it's going to spit something out and you're going to have to rely on it. The machine learns over time just like a kid does and a human being does. And once you get to the point where you are satisfied with that, using the humans to figure out that last 20%, I think that's the name of the game.

Prioritization, that's the name of the game and probably the most difficult thing we do as leaders all day long. Everybody's talking about this. Data's the new oil and there's the volumes of data. In fact, I was just reading about this and it's the most interesting stat I've ever read. We've created from the beginning of time until 2017, about five exabytes worth of data, whatever an exabyte is, that's a lot of data. And today we're creating that amount of data every two days, it's overwhelming. That's why the AI concepts and all of those other things are coming into play. Are we refining that data? If this is the oil, it needs to be refined to a point I can put it in the car and do something with it. I can't pump crude oil into my car and have the car run. And as you think about decision- making and we think about the humans, are we using humans at the right time to make the decision once we've exhausted the part that data can provide us versus going the other way around and trying to use the humans to parse through the data? We've got the automation, we've got the digital, we've got the concepts, let it do its thing. And again, machine learning, there's a very key word in there, it's called learning. We forget that. We think about machine and it's going to spit something out and you're going to have to rely on it. The machine learns over time just like a kid does and a human being does. And once you get to the point where you are satisfied with that, using the humans to figure out that last 20%, I think that's the name of the game.

So Kevin, I'm going to cue up on one of the comments you made. What did Nimesh say that you didn't agree with? Give me your raw unfiltered response.

So Kevin, I'm going to cue up on one of the comments you made. What did Nimesh say that you didn't agree with? Give me your raw unfiltered response.

I'm not sure I disagree with anything he said directly, mainly because I'm looking at him face- to- face and I won't say it to his face. And that's part of the decision- making too, being able to tell people they're wrong. I talk a lot about data being the foundation, but there's a layer above that data and that's the AI. I was talking to one of our very large manufacturing clients who said that they did a survey of their data scientists. The data scientists were spending between 85 and 90% of the time fiddling with data, trying to get it ready and then the rest of it was drawing insights. And to me, it's those, I call them micro decisions. There are micro decisions that are being made in the data about what pieces to include, what not to include. Just simple decisions like scale. That's where AI in the initial stages should be having its most value. To me, if you're applying AI to fairly mature business processes today, you're probably not applying your resources in the right place. You should be taking a step back and looking at the data and say, " How can I use the AI to clean the data so that the human being can make a clean decision or a timely decision." You want the humans in the big, important decisions and let the AI make those commodity micro- decisions. And I think that aligns somewhat with what the message is saying in that. But again, I want to reinforce that in the end result, the human decision's probably going to be the most important one but you need to support that human decision- making.

I'm not sure I disagree with anything he said directly, mainly because I'm looking at him face- to- face and I won't say it to his face. And that's part of the decision- making too, being able to tell people they're wrong. I talk a lot about data being the foundation, but there's a layer above that data and that's the AI. I was talking to one of our very large manufacturing clients who said that they did a survey of their data scientists. The data scientists were spending between 85 and 90% of the time fiddling with data, trying to get it ready and then the rest of it was drawing insights. And to me, it's those, I call them micro decisions. There are micro decisions that are being made in the data about what pieces to include, what not to include. Just simple decisions like scale. That's where AI in the initial stages should be having its most value. To me, if you're applying AI to fairly mature business processes today, you're probably not applying your resources in the right place. You should be taking a step back and looking at the data and say, " How can I use the AI to clean the data so that the human being can make a clean decision or a timely decision." You want the humans in the big, important decisions and let the AI make those commodity micro- decisions. And I think that aligns somewhat with what the message is saying in that. But again, I want to reinforce that in the end result, the human decision's probably going to be the most important one but you need to support that human decision- making.

Well, I hate to say this, but we're almost out of time. As our regular listeners of this podcast will know, it's time for the ' Lightning Round'. I'm going to give you each 60 seconds, that is one minute, to highlight the three key pieces of advice you would share with your colleagues, tackling this challenge of building a resilient, decision- making framework upon operational data. Kevin, let's start with you.

Well, I hate to say this, but we're almost out of time. As our regular listeners of this podcast will know, it's time for the ' Lightning Round'. I'm going to give you each 60 seconds, that is one minute, to highlight the three key pieces of advice you would share with your colleagues, tackling this challenge of building a resilient, decision- making framework upon operational data. Kevin, let's start with you.

You have to really look at whether you need, what we call digital engineering group. And that's that group of people who understand operations tied with people, understand the technology. Eventually they'll all become one person, you won't have to have two separate ties, but I'd say that's one. The operational data governance, working with IT to build an enterprise- wide data governance model that includes operations. And I think the third one is probably a view of how you're going to use AI. You need to start using AI to fix your data governance problem and less focused on using it to make those big decisions.

You have to really look at whether you need, what we call digital engineering group. And that's that group of people who understand operations tied with people, understand the technology. Eventually they'll all become one person, you won't have to have two separate ties, but I'd say that's one. The operational data governance, working with IT to build an enterprise- wide data governance model that includes operations. And I think the third one is probably a view of how you're going to use AI. You need to start using AI to fix your data governance problem and less focused on using it to make those big decisions.

Okay, Nimesh, now, it's your turn. Looking back over your organization's incredible 170 year journey, what three insights can you share with other business leaders in terms of the future of their operations?

Okay, Nimesh, now, it's your turn. Looking back over your organization's incredible 170 year journey, what three insights can you share with other business leaders in terms of the future of their operations?

So, I won't to start by saying I'll disagree with Kevin and that's my response. But I think that's a really great question. On 60 seconds, I would say the three things I'd start with is resilient decision- making needs a clear problem definition. If we don't have a clear problem definition, you're going to chase down a path and I'm not sure what the decision we're all going to end up making. So we usually have solutions looking for problems in those stages. Secondly, is making sure that you're listening. Listening to people, listening to opinions, that whole part of intellectual wrestling when you're trying to make and narrow down a decision is really important. And the third thing I would leave listeners with is, there's no business, there's no IT there's no silos when you're making a decision. It has to be a one- team- approach with diversity of thought. If you're not getting diversity of thought in your decision, you're probably going to make a wrong one.

So, I won't to start by saying I'll disagree with Kevin and that's my response. But I think that's a really great question. On 60 seconds, I would say the three things I'd start with is resilient decision- making needs a clear problem definition. If we don't have a clear problem definition, you're going to chase down a path and I'm not sure what the decision we're all going to end up making. So we usually have solutions looking for problems in those stages. Secondly, is making sure that you're listening. Listening to people, listening to opinions, that whole part of intellectual wrestling when you're trying to make and narrow down a decision is really important. And the third thing I would leave listeners with is, there's no business, there's no IT there's no silos when you're making a decision. It has to be a one- team- approach with diversity of thought. If you're not getting diversity of thought in your decision, you're probably going to make a wrong one.

This has been a great, informative, and frankly, inspirational conversation. My sincere thanks to both of you for being part of it.

This has been a great, informative, and frankly, inspirational conversation. My sincere thanks to both of you for being part of it.

Okay. Thanks, Joe and thanks, Nimesh for participating.

Okay. Thanks, Joe and thanks, Nimesh for participating.

Thank you, Kevin. Thank you, Joe. This has been enlightening and I think I learned a lot in this session.

Thank you, Kevin. Thank you, Joe. This has been enlightening and I think I learned a lot in this session.

My guests today have been Kevin Prouty, Group Vice President, Energy and Manufacturing Insights here at IDC and Nimesh Mehta, the Senior Vice President and Chief Information Officer at National Life Group. I'm Joe Pucciarelli and this is Future Enterprise brought to you by IDC. We still have two more Future of Research areas to dig into before the end of the season. So please like or follow wherever you get your podcasts to make sure you don't miss an episode. Talk to you soon.

My guests today have been Kevin Prouty, Group Vice President, Energy and Manufacturing Insights here at IDC and Nimesh Mehta, the Senior Vice President and Chief Information Officer at National Life Group. I'm Joe Pucciarelli and this is Future Enterprise brought to you by IDC. We still have two more Future of Research areas to dig into before the end of the season. So please like or follow wherever you get your podcasts to make sure you don't miss an episode. Talk to you soon.