Complete Intelligence

Login

Tag: startup

Categories
Visual (Videos)

A Mission-Critical Focus to Enable Growth

This article originally published at https://www.admentus.com/podcast/a-mission-critical-focus-to-enable-growth-with-tony-nash-of-complete-intelligence/ on March 26, 2021.

 

 

Every company wishes they have a crystal ball when it comes to making business decisions, and while a physical iteration of that wish is not possible, Tony Nash has developed the next best thing for his clients at his startup, Complete Intelligence.

 

Tony is the CEO and Founder of Complete Intelligence. Before founding Complete Intelligence, Tony was the global head of research for The Economist and the head of Asia consulting for IHS Markit.

 

Complete Intelligence is a fully automated and globally integrated AI platform for smarter cost and revenue proactive planning. Using advanced AI, they provide highly accurate cost and revenue forecasts fueled by billions of enterprise and public data points.

 

Key Takeaway: As a growing, scaling business, you must know what you are good at, what you do, and what you do not do. Maintain your mission-critical focus on the most important aspects of your business and outsource the parts that you are simply not good at or are outside of your mission.

 

Lessons Learned:

 

• Put Significant Thought into Your Senior Hires – hire low first, then hire the upper levels as they will be the ones that have to share your mission and must be the right hire.

• Know what You Do Not Do – Knowing what you don’t do is just as important as knowing what you do do.

• Define Your Culture – Define the culture you are building and continually and intentionally reinforce it.

 

Show Notes

 

JC: Hello everybody, Jeff Chastain here with the building to scale podcast again, where I get the opportunity really to speak with entrepreneurial business leaders growth-minded leaders who are working to grow and scale their own companies. And some of the we’ll discuss some of the challenges. Some of the successes as they’ve had over the years working through that.

 

Today’s guest with me here is Tony Nash with Complete Intelligence out of the Houston, Texas area. So first off Tony welcome to the show and thank you for taking a few minutes out of your busy day to join us here.

 

TN: Thanks, Jeff. I appreciate the opportunity.

 

JC: So give us a little bit about what Complete Intelligence is and what you guys have got going on there?

 

TN: Sure. We run an artificial intelligence platform. We use it to forecast market activity say currencies, commodities, equities for investors. We also help people companies understand their costs and their revenues which are really important on the budgeting side. So we help people de-risk their future business decisions by understanding where their costs are going to go and where their revenues will likely go.

 

JC: Okay, so I’ve got a background in technology and we kind of talked about AI and stuff beforehand but if we were to bring that down. And say okay I put you on the spot here but it was well the networking questions I’ve heard before like. Okay, if you describe that to a five-year-old what do you really do? So I know we kind of talked beforehand that this is typically big enterprise focus but for those that are not into that industry or not dealing with 9 10 figure dollar budgets, kind of a thing. Proactive budget planning. What does that really mean from a obviously from a company your size or your perspective?

 

TN: Sure, if I have to describe it to a five or ten year old. It’d say look, if you run a lemonade stand you have to understand how much the lemons are going to cost. How much the water is going to cost. How much the sugar is going to cost you. Also want to understand how many customers you’re going to have. How much money they’re going to spend. How much money you’re going to take in through the lemonade stand, right?

 

So we work with customers to understand all of those things. Now when companies themselves forecast this stuff and we know this from talking to our clients. They typically have 30 error rates or worse, even for raw materials costs. So their planning is way off, okay? When you look at industry experts investment banks economists, industry experts, these sorts of things. Their error rates are typically 20% off, okay? Our error rates are typically about around 4.6 percent, okay? And that’s on an absolute percent error basis. So we’re not gaming the pluses and minuses, okay?

 

So if you’re buying those lemons and that sugar and that sort of thing you can pay a dollar 20 for it. For us maybe a dollar five or something like that, right? So we’ll help you save 15 cents a lemon, okay? And you’ll understand where those costs are going. And so when you scale that up to very large customers who have you know 2 billion, 5 billion, 20 billion dollars in turnover or more. They’re buying in tens and hundreds of millions of dollars.

 

So let’s say a 17% improvement in their ability to forecast things, those are very large numbers. And so we’re working with enterprise scale data in the cloud and helping them understand where their business is going. And I would say probably better than just about anybody else out there. And so it doesn’t have to be the biggest company in the world doing this stuff. We work with mid-sized companies as well, okay? Because we’ll take data out of their enterprise planning system or something like that. And we’ll use it on our platform to help them make better decisions. We’re not telling them what to do, we’re just telling them where the data tell us that things are going to go.

 

So the real problem we’re solving aside from the obvious of what’s going to happen in their markets and their costs. Every company has a very painful budgeting process, okay? Some companies it takes a month or two or three months. Some companies some of our customers it takes six or seven months. And they’re going through in a very meticulous way of proactive planning their budgets. And there are hundreds of people involved and at the end of the day it goes up to the CFO and the CPO the chief procurement officer or the CFO and the head of sales and it’s a verbal agreement on what’s actually going to happen. This is actually one of the CFO pain points.

 

Not all that data driven, right? And so what we do is we give them a straw man to base it on so they can a very meticulous and detailed straw man. So that seven month process is taken down to a couple days, okay? From data transmission processing to sending back. And they also get a continuous budgeting exercise, okay? Every month we’ll reforecast their budgets for them so if something like Covid happens as it did last March, April. We help them understand what’s likely to happen uh in their business.

 

JC: Now that makes sense and that’s really one of those things that regardless of the business side that it’s like, okay having actual real data not seven month old data actually having it on a monthly basis or even closer kind of a thing. You can actually make real decisions on it at that point rather than just thinking like you said one code would happen. Everybody had their budget set January, February for what 2020 was going to be. And now two months later they’re completely invalidated that either the like you said earlier some some businesses are up, some are down, some are pulling back the the expenses. So it may have turned out okay but all the proactive planning they did initial on is completely out of window at that point.

 

TN: Right and most of those guys their revenue budgets were blown out like they had no idea what was going to happen there. They were saddled with their cost budgets that they had to continue paying for all this stuff. They didn’t know what was coming in on the top line. And so they then had to be very reactive on the on the cost side. And initially it was just a lot of you know arbitrary cost cutting and no disrespect to anybody. They were doing the best they could right but a lot of these big companies initially were just like, we don’t know what what we’re going to be in three months.

 

We were initially told covered was four to six weeks. And you know it’s still going on right and so what we saw is a lot of companies cut costs in the second quarter and the third quarter and by the end of the third quarter the management views looked up and said, well we’ve cut it as much as we can through the first three quarters let’s not release any more budget in Q4. So that just helped them on the income side so that they you know their bottom line looked better than it probably would have if they would have been a status cooperation.

 

JC: Yeah

 

TN: But still what we’re doing is using actual live data to help clients make the actual decisions that they need to make to run their businesses.

 

JC: Yeah and that’s really to me the key whether you’re got the small business that you simply just don’t have that much data to be processing all the way up to the enterprise. It’s still the same thing of saying, okay making those decisions on the numbers rather than, like you said with with Covid where it’s almost an immediate knee-jerk panic reaction of, hey we’ve got to cut things or hey everything’s going to be down. It’s like okay let’s look at the numbers and hopefully by a Q2 Q3 et cetera we’ve got some actual real data that we can start looking at.

 

So but yeah that’s that’s interesting so going back to Complete Intelligence then take us back. And say I think you said it 6 to 7 years old for the company itself. So how did this how did this kind of come about from a entrepreneurial standpoint.

 

TN: Sure, yeah, I used to run global research for a company called The Economist based in the UK, publishing company. And then I moved to a company called IHS Market which was just bought by S&P about six months ago. I was their Asia head of consulting. I was working with clients on a lot of data-driven decisions. And what clients were telling me were two things first the forecast that everyone was doing not just stuff, us were wrong and there was no accountability for that, okay?

 

The second is they could never get a forecast for their exact decisions. Forecasts were always too high level or not the right thing or something. So I rolled out of IHS market saying I want to have a data driven company that actually helps people make real decisions about their business. And so we started as a consulting firm for our first few years we were a consulting firm. And I was trying to understand the types of decisions that people needed to make I knew it from my consulting days with bigger firms but I wanted to understand what we could actually do.

 

About three years in we decided to turn into a product firm. Which is a very different type of business and so you know we built an initial platform that was very customizable but then to productize it out to build it to scale really is a very different skill set. Aside from a little bit math and a little bit of code it’s a very different same marketing and sales operation. It’s a very different you know infrastructure and all that stuff, right?

 

So a couple years ago we decided to productize with some subscription online subscription data products. And then we’ve got more specific with say cost and revenue products. So, I started the company in Asia in Singapore and then in 2017 we moved to Texas. So part of our, my calculation there was the talent in my mind is better here in the US. The customers are much easier to access here in the US and the business environment is pretty friendly. So it was a pretty easy decision for us to decide to come to Texas.

 

JC: Interesting. Okay. So what kind of challenges or what did you face in going from I guess I don’t necessarily know what your role was when you were saying with the economist except I’m assuming you’re you’re managing a team but you’re not necessarily managing a company. At that point to now owning and running your own company here with you said what 10 11 something employees up to now?

 

TN: Yes, that’s right that’s right, I think. So you know first is always the administrative part of it, right. I mean I think every new business owner just isn’t aware of the administrative stuff. And also the fear of missing something, right. What have I not done. what what tax filing have I not done or you know something like that, right? So there’s always that which was not a major issue but it was an additional burden.

 

When I think the biggest part of it was, I was just doing everything. And you come as a as a business owner you come to a point where you’re doing everything and you’re involved in everything. And then you’ll come to a point where you have to delegate stuff. And finding the right balance of when to do that and how to do that is I would say it’s more art than science. And other things like scaling RIT infrastructure that’s never really a decision I’d make before. I’m a math nerd and economics and data nerd, right.

 

So you know those types of decisions were really new but also on the customer side. Although, I had been customer facing when and this is kind of a no-brainer of course but when you don’t have a big brand behind you. Getting to the right people is a much more difficult process. And so we, I knew that coming out of the gate but I underestimated how hard it would be.

 

We started talking with some of our sales partners right away. Knowing that they wouldn’t give us a yes, right away but starting the relationship so guys like oracle guys like Bloomberg, Microsoft, Refinitive Tompson, Reuters these guys are all major partners for us now. Major sales channel partners and it took us four to five years to get those relationships moving and commercialized. So for a small business owner who is looking at channels as a major part of their business strategy. I would recommend you have to start talking to those partners right now like a year or two or three before you intend on getting your first dollar.

 

And so the other part as we’ve grown is we’ve had to think through, what do we do well as a company. And what’s best for us to outsource so things like HR. You know what, we don’t have an HR team. We have an outsourced HR firm, right, that’s a no-brainer but you know I can’t do it all myself. I don’t know the laws and stuff so we have outsourced HR. As I said with our channels we are scaling up our sales force but to have that as a kind of a force multiplier is huge for us, right. And things like marketing we have a marketing team in the Philippines and we have some marketing here but where can we get great skills at the best price really, right. And so we have to look around to find out you know what that stuff looks like.

 

We don’t have any of our data science team or any of our developers offshore. They’re all here in the US and part of that is for our client base. We don’t want things going to Eastern Europe or Asia or whatever but where we can push things off and make sure that we keep our core business. We’re happy to push things off. And so what I mean is we are a technology company, okay. We are not a human resources company we are not a marketing company and we’re not a consulting firm. And so we partner or outsource so that we can stay small and scale but do it very very well.

 

JC: Yeah and really even still that’s giving you the ability to scale because you’re not having to hire in like you said a whole team of HR. It’s a lot more cost effective especially for a smaller business to say hey we’re going to go pay a much smaller fraction of that to an outsourced group still allows you to scale and grow the business but at a much slower cost at that point.

 

TN: Right.

 

JC: So kind of what was that did you just walk into that and say day one we’re just not going to do HR. We’re just not going to do marketing etc. or was that kind of a a transition process because I know a lot of people will try to do some of it before they finally throw up their hands. And say okay, yeah this is not us or how quickly did you make that handoff there.

 

TN: That was immediate. I knew we didn’t want to do that from the start. Just from my corporate experience I knew that that wasn’t something I knew that we would spend a lot of money there not necessarily get good value. And so when somebody is a vendor you can you know you need some output, you need some outcomes. And so we just chose to make some of those guys vendors instead of making them full-time employees.

 

JC: So I’m curious since obviously you’re a numbers driven company accounting stuff like that. What does your relationship with some of these vendors look like how much of a numbers kind of basis relationship are you doing with them or are they is that more free flowing?

 

TN: Well, U think when you say numbers basis what what do you mean by that? I’m sorry.

 

JC: A lot of times. I’ll work with companies to sit here and say okay we’ve still got to measure our return on ROI kind of a thing on everything. So do we have specific numbers do we have specific milestones measurables et cetera tied to outside vendors the same way as we’d have tied to an employee?

 

TN: Oh, yeah absolutely. So like with our HR you know our outside stage our vendor. What we get from them on a monthly basis, I would probably have to hire a couple people to do internally. It just doesn’t make sense for us the the fully loaded FTE costs are just way too much. On the marketing side, unless somebody has absolutely stellar marketing skills, a lot of the direct marketing campaigns, social media marketing all that stuff for a firm our size at least it just doesn’t make sense to hire somebody. We can direct that activity manage it every day that sort of thing but the execution of it is better outsourced because we can do better with an outsourced vendor like dramatically better than we can by hiring those people directly, right. And so and so and we’re not talking a small kind of we’re saving 20% we’re saving a lot more than that by hiring marketing people directly.

 

JC: Yeah, that makes sense.

 

TN: Yeah and so I think again with most of the decisions we make. We really question how core is that to our business does it add to the technology, does it add to the customer relationship? And that’s really what it comes down to so I think we’re you know we’re at a place with things like video calls. And with a lot of the other technology that’s come around over the last 10 years. Where you don’t necessarily need that you don’t need everything in house it’s just not necessary. And if I have a vendor then I don’t necessarily have to pay for them to learn. If somebody is on staff I have to pay for them to learn. And so it’s not necessarily all fully productive time, right. And so again we’re very results oriented company. And so again we think through all that stuff. So for the guys who are watching your podcast. I would say look you know if you’re growing a company you really need to think through what your head count expectations are. What are they doing can you get that outsourced do you absolutely need to hire that person or can you turn it into an invoice.

 

JC: Yeah and that’s that’s really the the key because I see a lot more today of having a lot more availability and options of those outsourcing kind of a thing. That it’s not just necessarily the one big accounting firm that you had to be local face to face meeting somebody with the technology these days. I can have my account on the other side of the country kind of a thing and it’s just no big deal or I can have a marketing firm like you said all the way over the Philippines. It’s no big deal at that point so it’s almost it’s driven competition in those fields for sure. So it’s really almost like you said a no-brainer that okay why would you why would you want to go build your own in-house marketing firm when you’re a technology company or when you’re a financial services company something like that. It’s like that’s not your core business but still really identifying that core business is obviously the key there.

 

TN: Right.

 

JC: So talking about that core business you said you kind of made a an evolutionary change there with within your own company of saying okay consulting to now today being the the 100 product focus. What did that process look like or I guess for that matter? Why did you necessarily say because a lot of people I was that was my own background coming out of corporate America was, okay we’re going to be a consultant kind of thing. So how did you go from the consultant to saying okay we need to do something different or something transitioning towards the product side?

 

TN: Yeah, it’s very simple. As a consultant my upside is limited. I only have so many hours in the week and I can only bill against those hours. And if I hire people the upside is limited for them, right. So and if I want to grow a large revenue base I then have to hire a lot of people and then add x percent on top of their cost. And you know if their time isn’t sold then I can’t hire them anymore, right.

 

So I just got really tired of being the main guy consulting and you know billing against my hours. And so we productized because you know I wanted to make sure we could scale the kind of intellectual property that was in my head. And build that out as much as possible. Now that process was a it took a lot longer than I thought and a lot longer than I had hoped. That transition really took 18 months to two years. So you because you know, I had resources that were helping us on client engagements. I had to take them off of client engagement so they weren’t revenue generating to develop the IP around our product business because they can’t do both, okay. They can’t serve clients and develop IP because the development of  IP always gets put off. And so I had to make as a business owner, I had to make a very hard decision to say we’re going to stop you know selling, right now, okay.

 

And I’m going to pay the cost on these resources to develop this capability so that we can then productize it in 18 months time. And that was a very very hard decision but we did it because we had to otherwise I would have been flying all over working you know 90 hours a week, all that stuff. And we did it we bit the bullet and we came out with some pretty amazing capability.

 

JC: Oh and that’s really the key to me of saying, yes it’s a longer term vision you’re playing the longer game there even like you were talking about with the channel partners. Okay, you gotta start investing in things now looking towards that that longer term goal. And if you’re only looking towards next quarter, next month even next year. You might not necessarily have made that change to go product because you’re just looking at okay how can we get more billable revenues here in the next quarter.

 

So yeah it’s looking at that so kind of going down that direction. What does what does the vision look like for Complete Intelligence? Well how do you define vision from a company perspective and what’s your what’s your bigger picture vision there since it obviously sounds like you’re one to look longer term than just focusing on the immediate short term?

 

TN: Yeah I think so so our focus is really to continue to build out what we’ve started to do which is licensing sales for our core capability and aligning with other products. So how do we get built into core let’s say core erp software or core e-procurement software or you know something like that. So that a client doesn’t even have to think about working with us it’s just all baked into that software, right. And so that’s part of the vision.

 

The other part of the vision is how do we ensure that the results of our efforts are easy for a client to work into their internal processes. So just producing data or just producing something. If it’s an extra step then it’s a hassle for people, right. So how do we make sure and part of this is integration with other software that sort of thing but how do we make sure what we’re doing is really really easy for our customers to use. So that it helps them rather than adds more tasks to their day.

 

JC: Makes sense. So a lot of times I’ll see this where the the company owner. I’m not saying you are but the company owner has the vision there the ideas going forward how do you bring that down or how how do you bring that down in your own company to the team to say okay there. How do you get them bought into that vision or them understanding that vision internally?

 

TN: I think anybody doing that has to be comfortable with a lot of kind of a lot of mistakes and ongoing iteration of processes. I may have a short-term view of things that may not be right my team may be doing stuff that ends up wrong. I have to be okay with that and we have to learn. So and it’s not that’s not a luxury if you’re doing something like we’re doing we have to be a learning organization that is always seeing things that aren’t just right. And say okay that’s not right let’s take a couple days fix it. And then we’ll you know we’ll roll it out again or something like that, right. So as a software company we can do that. If we were making something physical it could, it would be different.

 

JC: Yeah.

 

TN: But as a software company we can iterate as we’re going, right. And so I think delivering that vision is really helping people understand on an ongoing basis. What the original vision is but then adjusting incrementally on a regular basis. And those regular adjustments they may be technology issues where we can’t actually do what I want to do, okay but that’s fine we iterate and we move along toward that path.

 

JC: Makes sense. So running a little long here running out of time. I always like to kind of come back and we we’ve talked about a bunch of different things over time but still what is kind of the best tip the best strategy that hey if I had known this six years ago. When we started the company or if I had this in mind this path in mind things might have been easier? What comes to mind as being your kind of your top idea here that wish I’d known this or thought about this or done this earlier?

 

TN: I think you know the biggest thing that I would have done is really thought through what I needed in a management team. If you’re scaling and you’re building the people who you put in place in a management team are really really critical. So what I would say is higher lower levels first and then make sure that the senior level management team that you’re hiring is somebody that you can really trust and someone who can really manage a team.

 

So put off those senior hires as long as possible. And it’s going to be painful and it’s going to mean you’re going to have to work a lot. And you know that sort of thing but higher low first then higher the upper levels, okay. And that’s almost the opposite of what say a venture capital investor or something would tell you. They want to see a management team but the fact is you need execution and then you need to build into those senior people that you can really trust to execute on the vision.

 

JC: That makes sense that’s interesting since we hadn’t touched on that one yet. I was figuring you’d go different directions but yeah I know a lot of times I’ll see that especially with the small ones if you’re don’t not having to do venture capital or stuff like that because I do agree there but a lot of times it is. Still it’s almost more the challenge that was what I run into of you start building out the lower levels. And you’re still trying to wrap your arms around it for honestly too long before you start introducing that management but yeah it’s doing that lower level and really understanding what’s going on first. And making sure you’ve got to keep handle on it before you can start bringing in people and really focusing at that point on.

 

Okay, what even going back to like what you were saying. Okay, what’s our core focus in the business this turns into. Okay, what’s your core focus as a leader to say. Okay, what are the aspects that I don’t want to do that I don’t enjoy doing that I don’t do well etc to hire on but yeah I like that from the focus on on building out the lower level team first that makes a lot of sense because a lot of times you’ll see startups said hey here’s our full sweet sea level
suite all these people we brought in it’s like. Okay, who’s actually doing the work at this point so yeah very cool, right?

 

TN: That’s right.

 

JC: So the listener wants to learn more about uh your company about Complete Intelligence about yourself where can they go find some more information here?

 

TN: Sure, so you can find us on on the web at completeintel.com. On social media on twitter we’re @complete_intel and you know just look us up online and we have a lot of interviews. A lot of resources on our website to find out more.

 

JC: Okay, we really appreciate it so thank you for taking time out.

 

TN: Thanks Jeff.

 

JC: Thank you.

 

TN: Thanks have a great day.

 

Categories
Podcasts

Fixing terrible forecasts and the lack of context

Tony Nash joined Geoffrey Cann in Digital Oil and and Gas podcast to talk about his revenue forecasting and predictive intelligence analytics startup company Complete Intelligence — how does the company solve the problem of terrible forecasts and the lack of context around data?

 

Geoffrey Cann joined us in QuickHit: 2 Things Oil & Gas Companies Need to Do Right Now to Win Post Pandemic.

 

This podcast originally appeared at https://digitaloilgas.libsyn.com/159-interview-with-tony-nash-of-complete-intelligence?utm_campaign=interview-with-tony-nash-of-complete-intelligence

 

Digital Oil and Gas Description

 

 

Jul 22, 2020

Today’s podcast is an interview with Tony Nash, CEO and founder of Complete Intelligence. Specializing in revenue forecasting and predictive analytics, Complete Intelligence develops artificial intelligence solutions. In this interview, we discuss predictive intelligence analysis, how Complete Intelligence works, and what value these forecasts can generate. 

 

Show Notes

GC: Welcome back to another episode of Digital Oil and Gas. My name is Geoffrey Cann, the host of the podcast. And I’m joined today by Tony Nash, who is the CEO and co-founder of Complete Intelligence. Tony, welcome to the podcast.

 

TN: Thanks, Geoffrey. It’s good to be here.

 

GC: You and I met probably a bit of a month ago. We did a short video exchange, and it was so much fun, we agreed that we should probably do something a little more involved, and here we are today. Of course, my interest is how digital innovation and digital strategic toolkit are transforming how the oil and gas world operates.

 

Your area of interest and expertise, the focus of your startup is in the application of smart technologies in agile budgeting and forecasting and market modeling. And that’s a big area of interest for oil and gas. That’s the reason why I thought you’d be a terrific guest to come on the show today and talk a little bit about that.

 

TN: Thank you very much.

 

GC: What’s your background? You were with The Economist, is that right?

 

TN: I was with The Economist. I led their global research business for a while. And I built what’s called the Custom Research business. It was a small niche business when I joined. It was a pretty sizable revenue by the time I left. Great organization. Had a lot of fun there. I then moved to a company called IHS MarkIt. Information services firm. I led their Asia consulting business. And from there, we started Complete Intelligence. I’ve been in information services off and on for way too long, since the late 90s.

 

GC: And what’s your education background? Did you start out in computer science or something?

 

TN: I was a graduate at Texas A&M in business and my grad work was in Boston at a school called The Fletcher School, which is a diplomacy school. So I was trained to be a diplomat, although I’m not very diplomatic at the moment. I have my moments.

 

Part of the reason I started going down this road is because in grad school, I had a trade economics professor who was amazing, great guy. I started my career after undergrad at a freight forwarder and customs broker. I didn’t have a glamorous first job. I was actually working the night shift in a warehouse at a freight forwarder, receiving exports and typing out airway bills and all that stuff. I got to know the nuts and bolts of world trade pretty specifically and pretty firsthand. I don’t know of any other trade economists who have started the way I have. I look at trade data differently than almost every other economist that I know of. I look at it somewhat skeptically. It’s that skepticism that I realized in grad school with this fantastic professor that my skepticism was an asset. My skepticism was an asset within statistical, mathematical models, within economic discussions and so on, so forth.

 

I had used it in business before that, but I didn’t think that I necessarily had the ability to apply it in this big world before I had this experience in grad school. So I then took it and I joined The Economist. I kind of conned them into hiring me, which was great, and then within a year or so, I was heading their global research business. From there, we just kind of took off.

 

GC: What are some of the products out of The Economist? Because I buy the magazine every week. And The Economist publishes an occasional handbook of global statistics, GDP by country and balance of trade and so forth. Were you involved in those kinds of products or were the products you were involved in much more specific to a client or customer requirement?

 

TN: I wasn’t. A lot of those are extracts from, say, IMF data. That’s part of The Economist publishing, which is a slightly different business to what I was doing. A lot of what I was doing was really applied work with clients. Solving real problems, figuring real things out. Some of this was corporate forecasting, looking at costs, looking at revenues, those sorts of things. Some of this was doing work for example the World Health Organization in places like Cambodia, comparing different treatments for mother-child transmitted HIV.

 

We had all kinds of cool, different approaches. And from my perspective, we could really play with different methodologies. We could really understand what was working and what wasn’t working. It was a huge sandbox for me. Again, really great smart people. That really started a lot of this kind of true love for me, which is what I’m doing now.

 

GC: What is the business problem that you saw that was sufficiently vexing that you decided to devote a lifetime in a career to trying to solve?

 

Because your career builds you to a point and then you say, “You know what? This is the problem I think I’m going to aim to solve.“ And you know what? You may go on to solve other problems, but at that moment, why would you become a founder to go solve something unless it was so big and so vexing, it was worth your time?

 

TN: I think I became a founder because I underestimated how hard it would be to build a business. Almost every founder will tell you that. When I was with both The Economist and IHS Markit, I had two really consistent feedback points that people gave me.

 

First is the quality of forecasting within information services, within corporate, say, strategy, finance, forecasting units, is pretty terrible. Most people forecast through, let’s say, a moving average approach. Some of the largest companies in the world will forecast using a moving average. If they are super sophisticated, they’ll use a very small maybe regression model or something like that.

 

But what mostly happens is one of two things. Either they look at last year’s and add a small percentage. “We’re just gonna have three percent on this year.“ That’s pretty common. The other one is really just a gut feel like, “I really think it’s going to be X this year.“ If a Wall Street analyst understood how unscientific the way outlooks are done within large companies, they’d be pretty shocked.

 

I mean, there is a belief that there is a lot that goes into the sausage machine. Traditional forecasting is terrible. Any forecast you buy off the shelf? Pretty terrible. Any forecast you’d get within a company? Pretty bad. Even the data scientists that are on staff with a lot of these big companies, really brilliant people, but they’re not necessarily fine tuning their forecasts based on error. And this is the key.

 

Companies who forecast should be required to disclose their error for every forecast they’ve done historically. That’s what we do for our clients. Because the number one problem was the quality of forecasts. So we spent our first two and a half years focused on that problem. We continue our approach to that.

 

GC: When you say “publish the error,” do you mean error in hindsight? How bad were we last year or do you mean here’s what we think our forecasted error is likely to be this year?

 

TN: Every year, any forecaster on planet Earth should say, this is what we forecast last year and this was our error rate. When we look at consensus forecast, for example, for energy like crude oil, natural gas, industrial metals, consensus error rates are typically double digits. Typically double digits. We just did a calculation. When I talk about error rate, I’m talking about absolute percent error. I’m not talking about gaming off pluses and minuses because that’s really convenient. But you look at a plus 10, you look at a minus eight, and that becomes a nine instead of a one.

 

People who forecast should be required to publish their error rates. Companies, especially energy companies, are paying hundreds of thousand dollars, if not seven figures to buy data. Those guys [forecasters] know they’re between 15 and 30 percent off in their forecasts regularly. Businesses are making decisions based on these data.

 

That’s the thing that, as someone who’s run businesses, not just analysts businesses, but run real proper businesses in different spaces, seeing planning people make decisions with a 30 percent error rate or 50 percent error or whatever it is, but no accountability from the information services provider? That’s a problem.

 

That’s a 1990s business model where you could play with the opacity around data. But in 2020, that should not be the case at all. We regularly show our prospects and our clients our error rates because they deserve it. They deserve understanding what our error rates are line by line.

 

GC: In oil and gas, when I’m building up a forecast, particularly for, say, an oil project, I’m having to forecast currency exchange rates, interest rates for my borrowings, the price of certain critical commodities like cement and steel. I’m having to forecast project delivery timeline and schedule. I’m having to forecast future market demand like, where’s my product likely to go? If each of these has a 15 to 30 percent error rate built into them and I’ve added them all up to get to a :here’s my forecasted economics for the year.“ Have I built in and basically had an accumulated error rate that makes my forecast pretty unreliable at that stage? Or these different errors, all sort of stand alone?

 

TN: That’s the budgeting process.

 

GC: I’ve been in that process. Right.

 

TN: Anybody who’s worked on a budget like that, they understand it. Maybe they don’t want to admit it, but we talk to people all the time who tell us. We have a client in Europe who admitted to us that some of their core materials that they buy, they know internally that their forecasts typically have a 30 percent error. And when we say that to people, to other companies, that’s feedback we get consistently that the people who actually know, the data know that their companies have error rates that are 20 to 30 percent or in some cases worse. They’re that far off.

 

When you think about it from a finance perspective, you’re over allocating resources for the procurement of something and that resource could have been used for something else. That’s one of the reasons why it’s really important for us to help people really narrow that down.

 

We check ourselves all the time and we looked at some industrial metals and energy stuff based on a June 2019 forecast for the following twelve months through the COVID period, comparing some consensus forecasts and our forecast. On average, we were 9.4 percent better than consensus. This is a Complete Intelligence forecast. It’s an aggregate looking at one of our manufacturing clients.

 

When you look at the different horizons, we look every three months, what was the error every three months, even up to the COVID period. On average, we were 9.4 percent better on a MAPE (Mean Absolute Percent Error) basis. If you’re buying off the shelf forecasts from some of the typical service providers, you’re looking at a pretty large disadvantage. They’re not using machine learning. They’re not using artificial intelligence. If they are, it’s typically very, very simple.

 

Now, part of what we’ve done through the process is we’ve removed the human process, human involvement in every aspect of data and forecast. From the data sourcing to the validation to anomaly detection to processing, to forecasting, we do not have human analysts who are looking at that and going, “that just doesn’t look right.“

 

GC: OK. It’s all done by machine?

 

TN: Right. We have machines that apply the same rules across assets. Because if we have human beings who gut check things, it just inserts bias and error through the whole process. And with no human intervention, we have a massive scale in terms what we do. We forecast about 1.1 million items every two weeks. Our forecast cycles are every two weeks. And we do it very, very quickly.

 

GC: And nine percent less error rate or a lot lower error?

 

TN: For the ones that we checked for that one client, yes. I would say in general, that’s probably about generally right. In some cases, it’s better.

 

GC: So a few things. One is the huge range of things that you can forecast when you remove all the humans out of it gives you these scale-ups. And then the fact that you can do it over and over and over again in much tighter cycle times than someone who just does it annually, once for a budget. And third, you’re testing your accuracy constantly to improve your algorithm so that you’re getting better and better and better over time.

 

TN: Exactly. When you consider something like crude oil, there are hundreds of crude forecasters who know that, they know that they know the six things that drive the crude oil price, right? And I guarantee you those crude oil forecasters who know what they know, what they know what those six things are, manually change their output once their models run. I guarantee you.

 

GC: I remember working for an oil company in Canada where the coming of the oil sands, but it was the monthly oil sands production expectation and would come into the finance function, where I was working, and the numbers would come in the spreadsheet and the finance people go, “add five percent to that.” Because they would say, they’re wrong every month, we’re tired of being embarrassed about being wrong. And they’re wrong because they undersell their performance. So just add five percent. And that was the number that would go to the market.

 

TN: And then that’s the error, right?

 

GC: And was that even the final error? There may or may not be on top of that?

 

TN: Probably not. And there are very few companies, we have some German clients, so they’re pretty good about doing this. But there are very few companies who actually track their error. And so most companies Are not even aware of how far off they are, which is a problem.

 

Here’s the second problem. The first one is forecasting quality is terrible. So we’ve developed a fully automated process. We measure our error, that sort of stuff. The second one is the context of the forecast. What I mean by that is, let’s say you’re making a specific chemical. You can go to some of these professional chemical forecasters, but they’re not making the chemical exactly where you make it. They don’t have the proportion of feedstocks that you have. Because we’ve built this highly iterative forecast engine that does hundreds of millions of calculations with every run, we can take a bill of material for that microphone in front of you or a chemical or a car, And we can forecast out the cost of every component to that every month for the next 12 to 24 months.

 

GC: Really? So, at any scale or any, I mean, you do it for a phone, you can do it for a car?

 

TN: That’s right. So if you look at a bill of material with, say, a thousand levels in it. Not a thousand components. But, you know, if you look at the parent-child relationship within a bill of materials, these things get really sophisticated really quickly. Some of the largest manufacturers have this data. They have access to it and we can tap into that to help them understand their costs, the likely trajectory of their costs over time. What does that help them? Helps them budget more accurately. It helps them negotiate with their vendors more accurately.

 

If you’re a, let’s say you’re a 20-billion dollar company and you have one percent on your cogs, how additive is that to your valuation if you’re trading at 15 or 20 times EBITDA.

 

GC: Yeah. And just right to the bottom line at that level.

 

TN: Exactly. This is what we’re finding. For the high context as of the second kind of business problem that we’re solving, and so we do this on the cost side. We do this on the revenue side. For that second problem, which is high context, again, the platform that we’ve built allows the scale, because if we had analysts sitting there scratching their head, rubbing their beard for every single thing we’re forecasting, there’s no way we could do this scale.

 

But because it’s automated, because it’s scalable, we can actually do this. And so it adds a whole level of capability within major manufacturing clients and it adds a whole level of risk protection or error mitigation to those guys as well.

 

GC: Just think about the current year that we’re in, which would include, at least in Canada, a pipeline constraints and the potential for rail expansion activity south of the border to either curtail production, the behavior of OPEC. When you think about getting into forecasting world of commodity prices… I can understand a manufacturer bill of materials and get into cost of goods sold and forecasting quite precisely what their forward manufacturing cycle will look like. I can use the same thing, though, in the oil industry, though, and probably gas, too, I would suspect.

 

TN: Yeah, yeah. Absolutely.

 

GC: And what’s the industry’s reaction to it? Because there’ll be people inside oil and gas who are doing forecasting today and they’ll be fairly proud of the models that they built that delivering a forecast. You’re walking in and saying ”I’ve got a whole new way to do this that is so many more cycles faster than what you can manually do, looking at many more products than you practically can. And if I show you that you’re nine percent off, 10 percent off with it.” I can imagine a negative reaction to this. I can also imagine for some organizations, pretty positive reaction on balance. How companies react when you told I can sharpen up your numbers?

 

TN: OK. So I’ll tell you a story about a gas trader. October of 2018, we went into a natural gas trader here in Houston. We showed them what we do. Gave a demo, give them access for a couple weeks so they could poke around. And we went back to them later and they said, “Look, you are showing a like a 30, 31 percent decline in the price of natural gas over the next 6 months. There’s no way that’s going to happen. So thanks, but no thanks.”

 

GC: This was your data telling them? All right. Refresh my memory. What was going on in October of 2018?

 

TN: Nothing yet. But Henry Hub prices fell by forty one percent within six months. So these guys were completely unprepared. The kind of conventional wisdom around natural gas prices at that time were unprepared for that magnitude of fall. But we were showing that that was going to happen. And so when you look at that, we had an 11 percent error rate at that point, which seems kind of high. But conventional wisdom was a 30 percent error rate.

 

We don’t expect to be the single go to source when we first go into a client. That’s not our thought. We know we’re a new vendor. We know we’re offering a different point of view. But we’re in a period of history where you have to think the unthinkable. And this is 2018, ‘19.

 

With the volatility that we’re seeing in markets, you really have to be thinking the unthinkable, at least as a part of your possibility set. It’s really hard. I would think it to be really hard for really anybody who’s trading any magnitude of oil and gas product to put something like this outside of their arsenal of strategic toolkit that they use.

 

GC: Well, certainly, if you had that gap in expectation of gas prices, the gas producer should have been thinking about hedging at that moment. And if their conclusion was, you’re completely wrong and I’m not going to bother with hedging, then shame on them really, because they should have done a far better job of managing to the curve. That’s a great story because it illustrates the challenge.

 

TN: That’s normal. It’s kind of the “not invented here” approach. And I see a lot of that within oil and gas.

 

We see a bit more interest in chemicals. They have to understand the price of their feedstocks. They have to understand their revenues better. And so we see a bit more on the downstream where there is a lot more interest. But midstream, upstream, it’s just not really there.

 

GC: What’s the untapped potential here to sharpen up forecasting? If you’re talking with a company and you say, “I can sharpen up your forecasts and your estimates and tighten up your variability and your business plan.” How does that translate to value and how do you extrapolate that to here’s the the slack, if you like, that’s built up economically within the system and as a whole that we stand potential to extract out and it’s going back to the misallocation of capital, the inadequate negotiations with suppliers, the margin left on the table because of the numbers aren’t just that reliable.

 

TN: We just went through this exercise with a manufacturer with about 20 billion dollars of turnover to help them understand. If you look at, say, the nine percent difference that we had in that exercise that I told you. So let’s say we’re working with the manufacturer with a 20 billion dollars and a PE ratio around 20, which is kind of where they’re trading. If instead of a nine percent or even four and a half percent improvement, let’s just say we had a one percent improvement in their materials. That one percent improvement in their costs translates to a three percent improvement in their net income. That’s three percent improvement in their net income translates to a 1.1 billion dollar improvement on their market capitalization.

 

We’re not going out there saying, “hey, we’re gonna help you save 10 percent of your costs.“ We’re not going out with statements that are that bold. We’re saying, “OK, let’s run a scenario where we help you with a quarter of a percent,“ which would help them add 280 million dollars on to their market capitalization. So procurement management and planning is kind of that tightly calibrated that if we helped this company with 0.25 percent improvement in their costs, keep in mind we’re nine point four percent better than consensus, that actually helps them add 280 million dollars onto their market cap. It’s just exponential.

 

GC: Well, it’s the leverage effect of earnings per share as you drop those earnings to the bottom line. And so anybody who’s actually measured on EPS or stock price should take a very interested look at this because you’re not selling a hardware, big capital investment, stand up a big department, not stuff. This is about taking the current process, that’s their budgeting, and squeezing out the variability or the error rate and trends that translates directly to value. When you think about it, it’s a complete no-brainer. Like, why would you not do this?

 

TN: It is. And we’re not going to charge them 280 million dollars to do it. But we could charge for this agile budgeting and forecasting. But we’re not going to.

 

GC: What you would do is you’d say, we’ll take shares in your company.

 

TN: I mean, that’s been suggested many, many times.

 

GC: Yeah, no, I totally get It. I say to oil companies, I’ll sell my services to you based on the price of oil. But the shareholder actually values the volatility on oil pricing. So they’re not prepared to give that away. And I’d be the same. I wouldn’t do that. But on the other hand, the back to this question of untapped potential. The ship, the bulk of the economy is operating off of wildly inaccurate consensus estimates. I think that’s fair to say, I don’t know if that’s accurate or not, but that would be my my conclusion. The bulk is operating off of inaccurate assessments. And so over time, what should happen is we should see a considerable improvement in that, which in turn translates into much better performing economy, allocation of capital and supply chains and so forth.

 

So you’ve been an entrepreneur now for how long’s it been three years?

 

TN: It’s five. We started as a consulting firm. It’s been about five years now. We actually started the company in Singapore. I moved it to Texas at the end of 2018. I couldn’t really find the coding talent and the math talent in Asia. I know this sounds really weird, but I couldn’t. And so I relocated the business to Texas in 2018.

 

GC: Yeah. And the talent pool is rich enough in the United States to fulfill this ambition?

 

TN: Yeah, yeah. Yeah. Totally fantastic.

 

GC: And what lessons have you taken away from all of this experience? Would you do it again?

 

TN: I would do it again. But I would do It differently. Anybody who starts a business has to realize that markets aren’t necessarily ready for radical new thinking. And it really takes a long time to get an idea of this out there. The kind of AI industry and the talk about automation has been around for a long time. But things like this, companies aren’t really ready to just let go of. It takes a lot for them to consider letting go of this stuff.

 

If your idea is pretty radical, it’s probably to take a while to socialize with an industry. But I would say it’s also, we as a company, we had a staff issue about a year ago, actually, that really shook us. And out of that, we developed our principles and our values. For anybody who wants to do this, you really have to understand what your own principles and values are from early on. It’s not something you wait until you’re 100 people to develop.

 

That issue a year ago was a very clarifying moment for us as a company. It really forced us to think about what kind of business we wanted to build. And I’m grateful for it, although it was really terrible at the time. I’m grateful for it because we have our values. It’s actually posted on our website. Whenever we recruit new people, that’s one of the first things I send to them and say, “Look, this is who we are. If you’re not comfortable with this, then this is not the right place for you. I’m sure you’re talented, all that kind of stuff. But we really live by this stuff and and those things are important.“

 

The other thing I would recommend for anybody who’s doing this is you’ve got to play nice with everyone on the way up and you got to play nice with everyone on the way down. It’s easy for tech entrepreneurs to really think a lot of themselves. And I think that’s fun. But it’s also not really helpful in the long run.

 

There’s a lot that I’ve learned about recruiting leadership teams, finding fit, looking for investors. I have the Asia experience. I have the U.S. experience. The math and the tech around A.I. is almost the easiest issue to solve. With technology, as long as you think big but retain humility, you can do a lot. You have to be bold, but be comfortable with mistakes.

 

The trick is getting the right team and the right investors who are comfortable with that environment. And if you get the right team and the right investors who are comfortable with that, then it can be much more fun. You actually have a chance at being successful because so many startups just fail. They don’t last a year or two years, much less for five. It’s really, really critical to get the right people.

 

GC: Yeah, I completely agree. The people and the money, it’s both sides. If the investors don’t have the patience or they’re marching to a different drum like they want short term results, and that’s as much of a death knell for for many startups as a talent talent deficit.

 

Tony, this has been excellent. Thank you very much for taking the time to join me today on Digital Oil and Gas. And if people want to learn more about Complete Intelligence, where do they go? What’s your website?

 

TN: Our website is completeintel.com. And we’re on Twitter. We’re on LinkedIn. There’s a lot of information there. And like you did about a month ago, we have a lot of five-minute interviews we do with industry experts and a weekly newsletter. There are a lot of ways to get to learn about us.

 

GC: Fantastic. Tony, thank you very much. This has been another episode of Digital Oil and Gas. And if you like what you’ve heard, by all means, press the like button and the share button and add a comment, and that helps other people find the show. And meanwhile, tune back in next week, Wednesdays, when we’ll issue another episode of Digital Oil and Gas. This is Canada Day week. So happy Canada Day to my all my Canadian listeners.

 

And Saturday is Independence Day. It’s July 4th. So, Tony, have a great time on Independence Day. Be socially distant and be safe out there. Thanks again.

Categories
News Articles

Houston startup uses artificial intelligence to bring its clients better business forecasting calculations

This article is originally published at https://houston.innovationmap.com/houston-based-complete-intelligence-changing-the-business-forecasting-game-2643180609.html

 

The business applications of artificial intelligence are boundless. Tony Nash realized AI’s potential in an underserved niche.

 

His startup, Complete Intelligence, uses AI to help on how to make better business decisions, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions.

 

“The problem that were solving is companies don’t predict their costs and revenues very well,” says Nash, the CEO and founder of Complete Intelligence. “There are really high error rates in company costs and revenue forecasts and so what we’ve done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate.”

 

Founded in 2015, Complete Intelligence is an AI platform that forecasts assets and allows evaluation of currencies, commodities, equity indices and economics. The Woodlands-based company also does advanced procurement and revenue for corporate clients.

 

“We’ve spent a couple years building this,” says Nash. “We have a platform that is helping clients with planning, finance, procurement and sales and a host of other things. We are forecasting equity markets; we are forecasting commodity prices, currencies, economics and trades. We built a model of the global economy and transactions across the global economy, so it’s a very large, very detailed artificial intelligence platform.”

 

That platform, CI Futures, has streamlined comprehensive price forecasting and data analysis, allowing for sound, data-based decisions.

 

“Our products are pretty simple,” says Nash. “We have our basic off the shelf forecasting application which is called CI Futures, which is currencies, commodities, equities and economics and trade. Its basic raw data forecasts. We distribute that raw data on our website and other data distribution websites. We also have a product called Cost Flow, which is our procurement forecasting engine, where we build a material level forecasting for clients.

 

“Then we have a product that we’ll launch next year called Revenue Flow, which is a sales forecasting tool that will use balance of both client data and publicly available data to forecast client sales by product, by geography and so on and so forth. So we really only do three things: revenues, costs and raw data forecasts.”

 

Forecasting across industries

Complete Intelligence’s Cost Flow and Revenue Flow products are specific to direct clients. They are working with clients in the food and beverage sector, the energy sector, the chemical sector, and the technology sector.

 

“Anybody that manufactures a tangible good, should use our product,” says Nash. “Because we can take their historical data we can configure their bills of material and they can see the exact cost and exact revenue of those products by month over time.”

 

CI is not a consulting firm, so they offer their clients an annual license, which allows them to receive updated forecasts every month to understand how markets will iterate over time.

 

“We’re integrating with the client’s enterprise data,” says Nash. “Whether it’s their ERP system or their procurement system or their CRM, we’re integrating with client’s enterprise data, and we’re creating forecast outlooks that are perfectly contextually relevant for client buying decisions.”

 

Called out by Capital Factory

 

As a business solution, CI has garnered widespread industry confidence and accolades, such as Capital Factory’s coveted “Newcomer of the Year” award, which recognizes innovative companies from a pool of 110 startups in Texas.

 

“Honestly, I couldn’t believe it because with a startup like ours, there’s so much hard work that goes into it, there’s so much time, there’s so much persistence,” says Nash.

 

“And the types of startups that Capital Factory attracts are very competitive startups, so for us to receive this award, it’s given us a huge amount of credibility in the market and it’s really encouraged the team inside the company to understand that what we’re doing is being recognized, it’s meaningful and we’re really going places.”

 

From consulting to billions of monthly calculations

 

Nash is no stranger to going places. Before setting up shop in his native Texas, he lived in Singapore for 15 years where he started his career in sourcing and procurement for American retail firms.

 

“I became very sensitive to costs, cost inflections and I got very involved in global sourcing and international trade and then I did a couple of corporate turnarounds and start ups and so with that you see costs as an issue with those types of firms,” Nash says.

 

He then worked with the Economist running their global research business. There, he grew familiar with how clients and customers use data. At IHS Markit, a global information provider.

 

“When I was working with those firms, those firms helped companies with planning,” says Nash. “The problem is that those firms have very large errors in their forecasts. It is not just the internal forecasts that have a 30 percent or higher error rate in their forecasts, even the industry forecasters typically have around a 20 percent error rates in their forecasts.

 

“Even the people who should actually know where prices are going are not very good forecasters. With Complete Intelligence, we wanted to use data and use artificial intelligence to machine learning to create a better way to identify where costs and revenues will go for companies.”

 

Every month, CI runs billions of calculations. They test their error rates and record them for clients that request them. With 700 assets that they show publicly, CI their average error rate is 3.7 percent, which is dramatically lower than both corporate procurement professionals and industry experts.

 

“With us doing billions of calculations, it allows us to run simulations and scenarios that your average analyst just can’t do and most companies haven’t even thought of. We’re able to run a comprehensive view of activities in the world to understand how things directly and indirectly affect a cost. In Houston, for example, that could be crude oil or natural gas or something like that.”

 

Proving its value

Last year, the company tested its platform with a natural gas trader. After reviewing the data, CI revealed to the client that natural gas would fall by 40 percent over the next year.

 

“They looked at our forecast and said they couldn’t work with us because it didn’t make sense,” says Nash. “A 40 percent fall didn’t make sense, so they didn’t subscribe to us. That was 2018. What has happened over the past 12 months? Natural gas prices had fallen by 49 percent. You would look at our forecasts and say, ‘Wow, that’s a dramatic drop over 12 months.’ But reality was even more dramatic than that and there weren’t analysts out there saying what our model was telling us.”

 

That natural gas trading company never admitted its faux pas, but if they had listened to CI, they could have positioned themselves to negotiate their vendors down for their cost base, which helps the margin of their business.

 

“Nobody ever admits mistakes,” says Nash. “But when you think about the numerous materials that require natural gas, especially things that are manufactured in Houston, it affects a lot of costs.”

 

Houston roots — by way of Asia

The missed opportunity with the natural gas trader notwithstanding, Nash is happy that he brought Complete Intelligence to Houston.

 

“I went to Texas A&M and grew up in Texas, so I moved back to Texas knowing how good Americans are with planning, with math and with data. I like Houston because people make stuff in Houston,” Nash says. “We just found Houston to be perfect after spending 15 years in Asia given the global centrality of Houston. The industry’s here and there’s a lot of diversity in Houston.”

 

Nash’s expectation was that he would be able to work with Western multinationals to improve their analytics and their artificial intelligence processes because he has learned that there is a lot of pressure in American financial markets and analysts communities to really know what is happening within companies.

 

“We want companies to be able to really tightly plan their costs so they can better improve their profitability,” says Nash. “That’s what I wanted to do when we moved to the U.S. and we’re finding that there’s a lot of interest from companies.”