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Episode 8 – The Value of Due Diligence and Fieldwork in the era of Big Data

Site Selectors Guild
Episode 8 - The Value of Due Diligence and Fieldwork in the era of Big Data

Rick Weddle: Welcome to Site Selection Matters, where we take a close look at the art and science of site selection decision making. I’m your host, Rick Weddle, president of the Site Selectors Guild. In each episode, we introduce you to leaders in the world of corporate site selection and economic development. We speak with members of the Guild, our economic development partners, and corporate decision makers to provide you with deep insight into the best and next practices in our profession.

In this episode, we have as our guest, Bob Hess, vice chairman and managing principal for Consulting with Newmark Global Corporate Services. In his professional career, Bob has, over the last 30 years, conducted over 250 assignments of all asset types on a global basis, including major projects in Asia, in Europe, and in Mexico. Today, Bob will talk with us about due diligence efforts in the era of big data. Join me as we welcome Bob Hess to Site Selection Matters.

Bob, before we get into the specifics of today’s topic, the true value of due diligence in the era of big data, perhaps we should start with some definitions for the benefit of our audience. What exactly is the era of big data? What does big data mean?

Bob Hess: Big data is the terabytes and terabytes times 10 of all the information out there that everybody’s exposed to. Everything’s becoming digital. It’s the digital age. There’s just a plethora of information, whether it’s descriptive, predictive, prescriptive, those are the three categories of big data. And there’s just more and more information on the cloud. Think about what’s generated on your phone.

So, for me, big data, the big data era, it’s just more information. It’s times 50, to the power of 50. And the question is, is how do you actually draw insights from that data? How do you categorize it, look for behaviors, look for patterns? And this gets into whole issues of other areas like machine learning and, you know, AI, robotics, all those types of things and what you do with that information. That’s the big challenge these days. You’ve got lots of data. You need more data scientists. What do you do with that data? And how does it help us make better decisions? How does it help us look for patterns, behaviors? How does it really improve our quality of life and how we do business?

Rick: So, Bob, you know, it strikes me that all this data’s been out there all along. I mean, the amount of information has been there, but we just didn’t have the computing power, I guess, to…once it’s collected, to analyze it and deal with it. I guess the advent of all this computing power that we have now has been kind of the enabling backbone for this big data. Is that right?

Bob: That’s right, Rick. Yeah. I had to go to a Northwestern for 10 days. I took a big course on big data and analytics for executives. It was very revealing. And really, it’s about, you know, there has been information out there. Of course, it’s just getting more and more, but now, it’s like, how do we draw the power out of that data? What kind of deep learning can we learn for that information? Where does it have trade-offs and where does that deep learning lead us in maybe to the wrong directions? Machine learning and how we just draw inferences, there’s deductive and inductive logic.

I don’t wanna get too technical, but now, it’s like, okay, how can we, you know, apply these, you know, these machines and computing capabilities and platforms to again, the so what? What if, so what, and that’s what’s needed in the boardroom these days. There’s information all over the place, especially in the business we’re talking about today, you know, the site selection business.

Rick: So, you know, it’s really just part of the whole Internet of Things that everything has data embedded in it. It’s really interesting. But let me ask you this, with all this information out there, doesn’t it beg the question as to whether it’s really necessary to utilize primary data and field work or due diligence anymore when it comes to gathering this information to make a corporate decision? Isn’t there just an app that we could use for this, or couldn’t it all be done in the cloud?

Bob: Well, you know what, that is a great question. And that question is being debated in all of the boardrooms of all major businesses, of all digital companies, and all service providers in government, in things like GDPR in Europe, in terms of like what we do with the data and privatization. Let me tell you, it is at the forefront. Is it an app? An app, basically, it’s basically a window to the information and insights that’s drawn from the data. The data is uploaded into a place and the app overlay…an app is a business intelligence window overlaid on the data. So, you can actually look at, you know, categorization, summaries, things of that sort.

So, an app is display of the information, but there’s still the perspective is judgment and discernment and what that information means to a decision you’re trying to make about a technology, an expansion, a capital investment, something that’s gonna impact your people, your culture, your workforce. That still requires very strong experiential, you know, deep learning type of inferences, that both these worlds have to co-exist.

The data scientists and the experience people, they shouldn’t be not working together. They should be working together to find the right problem. But that’s what I spent almost 10 days at Northwestern learning. Organizationally, all these companies are sending people like, how do we get these two worlds working together? If you bring those two together, Rick, it’s a very powerful decision-making tool.

Rick: Interesting. You know, we’ve been in this business a long time. I remember back in the old days, when a site selection team would come to town, it was really pre-internet, pre-big data, and they didn’t have a lot of information. Oftentimes, their community visit was the first experience to kind of test the information in that regard, and now, you start out on third base almost because you already have the ability to get a lot of this information analyzed before you ever get into market.

Bob: Yeah, don’t get me wrong. There’s some excellent analytics information out there, whether they’re government sources, other specialty firms creating new indices and types of information about places and people that are very powerful. And there’s a lot of math and analytics. So, like, look at one thing, linear programming, linear programming for supply chain planning has been around for decades. So, where you’ll be able to look at fixed and variable costs and be able to use math, look at inventory spaces and, you know, geocodes, and longitude, latitude, and come up with an optimized place to put a warehouse, right? This is a good example.

So that information has been around. It’s even more powerful these days, there’s even more powerful tools, you know, than these linear programming tools. But when you go to that location, you might be able to look at the data, but what’s the quality of the labor there. The productivity? Does it fit the culture of the company? Somebody just moved there that the data lag won’t catch in the information. These are very critical decisions on whether you wanna plop down a warehouse next to that optimized centroid, right? And there’s just a lot of physical, social, spatial, cultural, competitive type issues that have to be evaluated in the field.

So again, you’re not hearing Bob Hess here talking about being anti-big data and analytics. The power is there. It’s incredible, some of the platforms we’re using in our own business, but we gotta bring both of those things together to make the best decisions. The art of decision-making is still art and science, Rick.

Rick: Yeah. So, it’s really just a tool set that’s important to you to use, but you gotta use it right. You know, it’s clear that there’s a lot riding for companies on getting the facts right and making the right decision. A mistake is a mistake, whether it’s data mistake or personal mistake. Does the absence of old-fashioned tire-kicking and field work, over-reliance maybe on some of this large data assessment analytics, does that lead to some wrong or even bad decisions from time to time or could it?

Bob: I wish I had a database where I could track all of these decisions over the year. All I’ll explain to you is what I’ve seen as a practitioner and a professional and short answer is yes, I believe there are some bad decisions, poor decisions, or maybe it is good decisions that aren’t great. Good to great, there’s a big difference. Remember that book, ”Good to Great?” Is a good decision good enough? Let’s get going. Let’s start producing.

I’ll give you an example. Just a couple of years ago, we did a national screening study, lots of data and information, and we got down to Phoenix and Louisville, Kentucky, for this back office operations. There’s gonna be about 800 to 1,000 people. And, you know, the situation in Phoenix, by the way, great town. We put other projects there. This is not any statement against Phoenix, but long story short, the paper said Phoenix was preferred over the other location. But once we went to Phoenix, and believe me, the executives, it was like pulling teeth, like, ah, the data looks great. The CEO from Australia says, “Let’s go to Phoenix. There’s another business unit there.” They have an empty floor, like the real estate, “Oh, let’s make the real estate optimize.” I said, ”No, we need to go spend a couple of days there, guys.” And we did.

And about a day and a half into the meeting, the COO leans over to me and he says, ”Bob, this would have been a huge mistake. I mean, Phoenix is a great town and met all our fatal flaws, but our brand can’t compete here. We can’t compete with the other logos that are here. We’re not…our culture doesn’t fit this area. We feel better in a smaller place where we can have a bigger splash, be more part of the community. We don’t wanna be part of a sub-market or a sector.”

So how do you get that out of numbers, Rick? There’s a good example. And today, this company in the other location, super successful, their hiring has been much better. That came from the field work. That did not come from the numbers. It came from the field work.

Rick: You know, Bob, you mentioned the term hiring, I almost think about this in terms of like a resume or a candidate profile. And oftentimes, we all had experiences where we saw someone…we looked at resumes, we said someone looked, absolutely looked really good on paper, but for a lot of whole different reasons, when you got into the interview process, the discussion, that maybe they turned out to not be the best candidate. I guess the community could be the same way.

Bob: Exactly, exactly. You know, communities have personalities. There’s things that you just can’t quantify. Actually, I think a lot of people in the site selection profession, my colleagues take pride of that. We can quantify almost every qualitative factor, but there’s just certain things we can’t around the culture and the spatial dynamics of the region and, you know, intervening opportunities and just the quality of leadership, like from your past and whether you’re wanted or not. Do they want me? How do you actually quantify that? Unless you go and spend time in the field and, you know, meet the people and kick the tires. I mean, again, both these worlds got to coexist power these days, but we gotta be able to go in the field, bring these two worlds of art and science together for great decision-making, not just good decision-making.

Rick: You know, you shared the Phoenix/Louisville experience. Are there other case studies or real-life examples that might come to mind that you could share where the data said one thing, the field work really helped get to a better decision?

Bob: Yeah. I’ll bring up a project in Asia where…this was maybe a few years back, but even before the big data era where it was working with the large steel plant project and we were brought in after the fact. I’ll tell you the mistake that was made, where the company actually did the work themselves. And they actually put a…bought a $500-million investment in certain areas around Shanghai, because that’s where the government told them to go. At that point, everything was about the incentives, that was where they wanted to develop, etc., etc. So that’s how decisions were made back then.

Well, fast forward about 10 years, where I got a call from this client and say, ”Bob, we’d like to talk to you about something confidential.” ”What is that?” ”Well, we were informed by the mayor of this place in Shanghai that they wanna do the World Expo here, and they don’t want our plant there anymore. They want us to move.” Like it’s hard to move a $500-million facility. And it’s kind of like…so just looking at the location that they chose, you can see the writing on the wall, the zoning, residential development. The future plans were to make this, you know, much more office and commercial.

So, you gotta be able to understand, you know, things like that. What is zoning and master planning and economic gardening and targeted industries, what do they really need? You gotta be able to read those tea leaves before you make these decisions. I think it was one of the best examples I could bring up. All [inaudible 00:13:03] said this and 10 years later, you need to leave.

Rick: You made a point earlier in our discussion about something going on that didn’t show up in the numbers. You know, big data, large data, analytics, it’s still based on data that is largely out of date the day that you pull it down from the file, because it’s only as current as the present and it can’t pick up something that maybe hasn’t made it into the numbers yet, a decision that’s in the process of being made, another competitor for workforce that’s in the process of locating in that same area but hasn’t really done that yet. It seems to me that that only…you can only unpack that through kind of real-time probing and discussion with the people on the scene.

Bob: Yes, sir. Good examples. In this profession of location strategy and site selection, we still like communities and regions and ecosystems and metros to allow us to come in and do true business intelligence, business intelligence gathering or benchmarking with existing employers. And what do you find out what those employers are issues around the quality of the workforce. It could even get into drug testing. We could get into the fact that we’re going to change our entire skill mix recruiting. We’re gonna hire 1,000 people in this skillset, and you’re gonna locate next door. You can’t get that from the data, Rick.

So now, you might say, that’s very confidential information. How do you get that? Well, that still can be under a company A or company B. It’s still the job of economic developers, our partners. Site selectors, economic developers, suppliers and customers, it’s their responsibility to make sure we make good decisions for them too. So, we get into those companies. We can do that benchmarking with integrity and professionalism. That’s harder and harder to do, but it’s still, the communities that are winning more are the ones that get us that information, that true business intelligence. So, we can make these nuanced decisions.

Rick: And you don’t get that if you don’t ask and you can’t ask if you’re not there. So that’s important. Let’s turn now a little bit into the business of professional site selection. It seems to me, and I could be wrong, but I’ll just probe on this question, that corporate executives might begin to have kind of a confirmation bias towards big data and an app for this, an app for that, or just show me the numbers and do that. How does the site selector industry, how do you, in your role, help corporate executives, when they’re in this process, really understand and be willing to pay for the investment of time and resources to get onsite, to, you know, be like the gum shoe detective, and really get out there and kick the tires? How do you play a role in helping corporate decision-makers reach that right kind of balance?

Bob: Well, there’s a number of dimensions to that. One is I would call it the strategic nature of the investments. So as this investment, let’s say it’s another branch and you’ve done it 20 times before, yeah, there’s probably a pretty good cookie cutter process or a portfolio approach to finding the real estate, you know, operationally putting that in place, you know, working with HR and get that up and running. So, let’s be real about that. But there’s other assignments that are more strategic. They can involve the new technology. It could be a totally new geography in a market that you’re entering in, you’re unfamiliar with. There could be a lot of divergence of decision-making within the company. That’s a big thing. It’s, you know, HR and IT, and ops, supply chain and marketing and sales all agreeing on how to, you know, make this new corporate asset or this investment successful.

Sometimes you need like a third party, an objective resource to come in there and bring that all together, establish priorities, weight the priorities. Actually, I think most of the work and high-value job of a site selector, with all this information out these days, is to actually get the thing started right. What are we trying to solve for? What’s the true problem? How are we gonna solve for it? What scenarios should we look at? How are we gonna weight the scenarios? What’s out of scope, what’s in the scope? What’s dead on arrival and what’s real in terms of pushing the boundaries of being a pioneer in a new geography?

And then, of course, there’s all the other issues like workforce and talent and business climate, standard types of things. There’s lots of information. So that’s a big job. And I didn’t say the one word in there, real estate. Those are all business issues that are important to the executive suite to make sure that these decisions are sustainable actionable decisions, because you only wanna do it once and you wanna do it right. Again, especially if it’s a strategic higher capital investment and something you don’t make a decision every year or two. It’s a 10, 15, 20-year decision for the company.

Rick: You know, Bob, I heard you go through that set of questions. And it strikes me that a big part of the value-add of the professional site selector to the corporate executive in this era of big data is making sure you’re asking the right questions. You know, you asked the wrong question, or you don’t ask the right question, the data won’t…no matter how big it is, no matter how analytical it is, no matter how much machine learning there’s there, it’s not gonna give you the right answer if you haven’t asked the right question. And I guess that’s the one of the roles of professional place.

Bob: Absolutely. And those are quantitative variables, qualitative variables. Another other way to say they’re strategic, operational financial dimensions of that and there’s variables under that. And then there’s intangibles. There are things like windows of opportunity. Many years ago, I had a client who had a single plant in the United States, and then their competitor came over from Asia and put a plant in the West Coast. They were shipping to the West Coast, very heavy product, water-based product. And basically, their response was, “Oh my God, Bob, we have to do something. We have to respond so we can get a plant on the West Coast.”

So, there’s a window of opportunity and we needed fast. So, you gotta bring on the right resources that can do it fast and not make mistakes along the way. Make sure you understand trade-offs and risk. Risk is a big part of corporate site selection. So again, it’s not really site selection. This is really sound decision-making regarding consolidations, expansions, relocations, new technologies. That’s the true value of the profession. It’s and exciting part of the profession.

Rick: Yeah, it really is. You know, Bob, you’ve given us so much to think about. Big data, but this has been a big conversation, what a great conversation. But unfortunately, that’s all the time we have today. So, let me say thanks to Bob for talking with us today on this episode of ”Site Selection Matters” about big data and good decision-making.

Bob: Thanks, Rick. Appreciate it.

Rick: Thanks for listening to this episode of Site Selection Matters and a special thanks today to Bob Hess for helping us understand the impact that big data is having on corporate site selection and just how important good field work is and good due diligence is to making good business decisions. What an informative discussion we’ve had today.

Again, I’m Rick Weddle, president of Site Selectors Guild. We hope you’ll subscribe to the Site Selection Matters podcast on Apple podcast, on Stitcher, on Spotify, or wherever you listen to your podcast. We look forward to bringing you some great discussions in the year ahead. Until next time, good day.