The concept of personal customer service is nothing new. Good bartenders have always remembered their regular patrons’ favorite drinks. Shopkeepers are trained to anticipate their customers’ needs.
The skill of identifying customers’ wants and desires to drive sales remains important to businesses, and now there is a raft of high-tech tools to help them gain new insights.
Here’s how companies in the Oracle for Startups program are helping their clients deep dive into analytics so they can understand customer needs better than ever before.
Helping companies listen to customers
How we speak gives listeners all sorts of clues about what we really want. DataKlout uses Voice AI to analyze customers’ intentions. Its next generation analysis software provides consumable insights for decision making and results. For example, it can be used to identify customers’ positive reactions to marketing and sales calls, allowing a sales team to focus on closure, or be used to train employees to deliver more delightful customer service, among other use cases.
Using the tool helped a client cut the cost of customer acquisition by 75%, leading to a 500% increase in opportunities for closure in a tele-sales and tele-marketing campaign, while it also increased opportunities for a car insurance company, which used the tool to identify prospects from a cold calling campaign, resulting in a jump from 2% to 8%.
While DataKlout’s Voice AI gives its customers a new technique to understand their customers’ deepest desires, it intends on going further, by equipping its clients with another in-depth tool by integrating the facial expressions in a video calls.
Understanding the whole customer
Customers are complex creatures, making predicting our actions and needs difficult. FirstHive uses a machine-learning driven algorithm, allowing its platform to ingest data from nearly every kind of customer interaction and transaction, including ERP, CRM, website, social, PoS, app, and customer care groups.
It can even absorb offline and unstructured data like social comments. The tool then builds unified customer identities and makes recommendations on what the next best action should be to enhance the customer’s experience.
The startup has worked with companies like Singapore Airlines and Unilever and has shown its tool can help enterprises earn a sixfold increase in their marketing ROI, with the right content being sent to razor sharp customer segments at just the right time.
Similarly, Pryon helps employees of enterprises find important information easily so they can do their jobs, including customer service. The startup behind the technology that powers Alexa allows users to ask an assistant a colloquial question and receive an answer in just a second. The solution applyies natural language processing to unstructured content automatically ingested from a vast range of content types.
Using AI for super forecasting
As any good service provider knows, the best way to meet a customer’s needs is to anticipate them. (Just think of that brilliant bartender or stellar hotel worker.)
Complete Intelligence runs more than 15 billion data points through an AI platform, making trillions of calculations across 1,400 industry sectors. This allows it to provide its customers in industrial manufacturing as well as the oil and gas, chemicals, electronics, food and beverages industries with a fully automated, globally integrated artificial intelligence platform to help purchasing, supply chain planning, and revenue teams make accurate forecasts.
Helping startups meet customer needs
Oracle for Startups exists to support growing companies and help them serve their customers’ needs. We know startups need reliable cloud services; that’s why we offer them a 70% discount on OCI.
We know young companies need to embrace new tech tools and scale, which is why we have a dedicated CAT team to help with migration and other goals.
We also know marketing support and introductions to enterprise customers are invaluable, and we strive to make these perks of our program a reality.
We’re also hoping to know even more about our customers by launching a global customer survey. After all, who better to inform our strategy than the startups we serve?
MG: The Lead Lag Report joining us for the hour here is Tony Nash of Complete Intelligence has found a lot of people that I respect following. Tony, I saw a few people saying they were excited to hear what Tony has to say. So hopefully we’ll have a good conversation here.
Tony for those who aren’t familiar with your background talk about who you are how’d you get involved in the data side of markets and forecasting in general. And what you’re doing with Complete Intelligence.
TN: Sure, Michael. First of all, thanks for having me. I have followed you for probably 10 or 15 years.
MG: I am very sorry for that I am very very sorry for that.
TN: But yeah so, I got involved in data way back in the late 90s when I was in Silicon Valley and I built a couple of research firms focused on technology businesses. I then took about probably eight years to become an operator. I did a turnaround in Asia of a telecom firm. I built a firm in Sri Lanka during the Civil War and then I started down the research front again. I was the Global Head of Research for the Economist and I was the Asia Head of Consulting for a company called IHS Markit which is now owned by S&P and then after that I started Complete Intelligence.
So, you know my background is really all about data but it’s also all about understanding the operational context of that data. And I think it’s very hard for people to really understand what data means without understanding how people use it.
MG: Okay. So that’s maybe a good direction to start with that point about context with data because I think part of that context is understanding what domains data is more appropriate for forecasting and others. Right? So, I always made this argument that there are certain domains in particular when it comes to, I would argue investing that have sort of a chaotic system element to them. Right? Where small changes can have ripple effects. So, it’s hard to necessarily to sort of make a direct link between a strong set of variables and the actual outcome because there’s always a degree of randomness. Whereas, something that’s more scientific right that doesn’t have that kind of chaos theory element is it’s clearer.
So, talk about that point about context when it comes to looking at data. And again, the kind of domains where data is more appropriate to really have more conviction in than others.
TN: Yeah. Okay. So, that’s a great place to start. So, the first thing I would say is take every macro variable that you know of and throw it out the window. It’s all garbage data 100 of it. Okay? I would never trade based on macro data.
We’ve tested macro data over the years and it’s just garbage. It doesn’t matter the country. You know we hear people saying that China makes up their data. Well, that may be true you can kind of fill in the blank on almost any country because I don’t know how much you guys understand about macro data. But it is not market clearing data. Okay? Like an equity price or a commodity price.
Macroeconomic data is purely academic made-up data that is a proxy for activity. It’s a second or third derivative of actual activity by the time you see, say, a CPI print which is coming out tomorrow. Right? And it’s late and it’s really all not all that meaningful. So, I wouldn’t really make a trade or put a strategy together based on macro data even historical macro data. Every OECD country revises their data by what four times or something.
So, you see, a print for CPI data tomorrow that’s a preliminary print and that’s revised several times before it’s put on quote-unquote actual. And so, you know, you really can’t make decisions using macroeconomic data beyond a directional decision. Okay? So, if you follow me on Twitter, you see I’m very critical macro data all the time. I’m very sarcastic about it.
I think the more specific you can get… You know if you have to look at say national data or macroeconomic data, I would look at very low-level data the more specific you can get the better. Things like household surveys or you know communist and socialist countries. Chinese data at the very specific level can be very interesting. Okay? Government data the high-level data in every country I consider it garbage data in every country. So, you’re looking at very low-level very specific government or multilateral data, that’s interesting.
The closer you get to market clearing data the better because that’s a real price. Right? A real price history on stuff is better and company data is the best. And of course, company data is revised at times but that really helps you understand what’s happening at the kind of firm level. And what’s happening at the transaction level. So, you know, those are the kind of hierarchies of data that I would look at.
MG: So, okay this is a great. That’s a great point you mentioned that it’s you said very these variables is macro variables they’re proxies for activity. Right? They’re really more proxies for narratives. Right? Because and that’s where I think… You mentioned sarcasm almost 99 of my tweets at this point are sarcasm because when Rome is burning, what else I’m not going to do except joke about it. Right? Because I can’t change anything. Right?
So, and to that point I share a lot of that cynicism around data that people will often reference in the financial media that sounds really interesting, sounds like it’s predictive but when you actually test it to your point, you throw it out because it doesn’t work. Right? There’s no real predictive element to it.
So, we’ll get into some of the predictive stuff that you talk about but I want to hit a little bit on this market clearing phrase you kept on using. Explain what you mean by market clearing.
TN: Data is where there is a buyer and a seller.
MG: To actual prices of some asset class or something like that.
TN: Yep. That’s right.
MG: Okay. So, that makes sense. Okay. Now again I go back to the certain domains that data is more clear in terms of cause and effect and getting a sense of probabilities the challenge with markets. As we know is that the probabilities change second by second because not only does that mean meaningless data change second by second but the market clearing data changes second by second. Right? Going back to that point.
So, with what you do with Complete Intelligence, talk us through a little bit. What are some of the variables that you tend to find have some predictive power? And how do you think about confidence when it comes to any kind of decision made based on those variables?
TN: Sure. Okay. So, before I do that let me get into why I started Complete Intelligence because if none of you have started a firm before don’t do it. It’s really really hard so…
MG: From the people in the back because I got to tell you I’m an entrepreneur, I’m going through. And all you got is people on Twitter kicking you when you’re down when it’s the small sample anyway.
TN: Absolutely. So, I was where I had worked for two very large research firms The Economist and IHS Markit. And I saw that both of them claimed to have very detailed and intricate models. Okay? Of the global economy industries, whatever. Okay? For all of the interior models. And I have never spoken with a global research firm a data firm that is different from this. And if I’m wrong then somebody please correct me. But at the end of that whole model pipeline is somebody who says “no that’s a little bit too high” or “a little bit too low” and they change the number. Okay? To whatever they wanted it to be in the first place. So, and I tell you 100% of research firms out there with forecasts today have a manual process at the end of their quote-unquote model. A 100% of them. Again, if there’s somebody else that doesn’t do that, I am happy to be corrected. Okay? But I had done that for a decade and I felt like a hypocrite when I would talk to clients.
So, I started Complete Intelligence because I wanted to build a 100% machine driven forecasts across economics, across market, across equities, across commodities, across currencies. Okay? And we’ve done that. So, we have a multi-phase, multi-layer machine learning process that takes in billions of data items. We’re running trillions of calculations every week when we reforecast our data. Right? Now the interval of our forecast is monthly interval forecast. So, if people looking at daily prices that’s not what we’re doing now. Okay? We will be launching daily interval forecasts. I would say probably before the end of the year to be conservative but we’re doing monthly interval forecasts now.
Why is everything I’ve said is meaningless unless we measure our error. Okay? So, for every forecast that we do. And if you log into our website, you can see whether it’s the gold price, the S&P 500, USD, JPY, molybdenum or whatever. We track our error for every month, for everything that we do. Okay? So, if you want to understand your risk associated with using our data it’s there right in front of you with the error calculations. Okay? It’s only fair, If I’m gonna say sell you a forecast, you should be able to understand how wrong we’ve been in the past, before you use that as a decision-making input.
MG: Well, maybe just add some framework on that because I think that’s interesting. So, what you call error I call luck. Right? Because luck is both good or bad. I always make that point that with any equation any set of variables you’re going to have that error is the luck component that you can’t control. And that doesn’t necessarily mean that the equation is wrong. Right? It’s just means that for whatever reason that error in that moment in time was higher or lower than you might otherwise want. Okay?
TN: There is no such thing as zero error. And anybody who tells you that they have zero error is obviously they’re an economist and they don’t understand how markets work. So, there is always error in every calculation.
So, the reason we track error is because that serves as a feedback loop into our machine learning process. Okay? And we have feedback loops every week as we and what we’re doing right now is every Friday end of day. We will download global data process over the weekend have a new forecast on Monday morning. Okay? And so all of that error whether it’s near-term error, short-term error or say medium-term error, we feed that all back in to help correct and understand what’s going on within our process. And we have like I said, we have a multi-phase process in our machine learning platform. So, error is simply understanding the risk associated with using with using our platform.
MG: Right, which is basically how apt is a thing that you’re forecasting to that error which is again luck good or bad. I’m trying to put in sort of a qualitative framework also because I think… Yeah, there’s errors in life obviously, too. Right? And so, when they’re good or bad. But you know those elements.
TN: Right. But here’s what I would and I don’t know, I don’t want to dispute this too much but I think there is. So, you use the word luck and that’s fine but I think luck has a bit to do with the human element of a decision. Okay? We’re using math and code there’s zero human interaction with the data and with the process. And so, I wouldn’t necessarily call it luck. I mean, it literally is error like our algorithms got it wrong. So, if you want to call luck that’s absolutely fine but I would say luck is more of a human say an outcome associated with a human decision. More than something that’s machine driven that’s iterating. Again, we’re doing trillions of calculations every week to get our forecasts out there.
MG: Yeah, no that’s fair and maybe for the audience, Tony. Explain what machine learning is now.
MG: I once developed an app called “How Edition”. I was having dinner with the head developer once and he said he just came back from a conference about machine learning and he was just basically well, having drinks with me laughing and joking saying everybody use this term machine learning but it’s really just regression analysis. Right? So, talk about machine learning what is actual machine learning? How important is recent data to changes in the regression? Because I assume that’s part of the sort of dynamic nature of what you do just kind of riff on that for a bit.
TN: Okay. So, when I first started Complete Intelligence, I was really cynical about AI. And I spoke to somebody in Silicon Valley and asked the same question: what is AI? And this person said “Well AI is everything from a basic I say, quadratic equation upward.” I’m not necessarily sure that I agree that something that simple would be considered artificial intelligence. What we’re really doing with machine learning is there are really three basic phases. Okay? You have a preprocess which is looking at your data to understand things like anomalies, missing data, weird behavior, these sorts of things. Okay? So, that’s the first phase that we look at to be honest that’s the hardest one to get right. Okay?
A lot of people want to talk about the forecasting methodologies and the forecasting algorithms. That’s great and that’s the sexy part of ML. But really the conditioning and the pre-process is the is the hardest part and it’s the most necessary part. Okay? When we then go into the forecasting aspect of it, we’re using what’s called an ensemble approach. So, we have a number of algorithms that we use and let’s say they’re 15 algorithms. Okay? That we use we’re looking at a potential combinatorial approach of any individual or combination of those algorithms based on the time horizon that we’re forecasting. Okay?
So, we’re not saying a simple regression is the way to go we’re saying there may be a neural network approach, there may be a neural network approach in combination with some sort of arima approach. We’re saying something like that. Right? And so, we test all of those permutations for every historical period that we’re looking at.
So, I think traditionally when I look back at kind of quote-unquote building models in excel, we would build a formula and that formula was fairly static. Okay? And every time you did say a crude oil forecast you had this static formula that you set your data against and a number came out. We don’t have static formulas at all.
To forecast crude oil every single week we start at obviously understanding what we did in the past but also re-testing and re-weighting every single algorithmic approach that we have and then recombining them based upon the activity that happened on a daily basis in that previous week. And in the history. Okay?
So, that’s phase two the forecasting approach and then phase three is the post process. Right? And so, the post process is understanding the forecast output. Is it a flat line? Right? If it’s a flat line then there’s something wrong. Is it a straight line up? Then that there’s something you know… those are to use some extremes. Right? But you know we have to test the output to understand if it’s reasonable. Right? So, it’s really an automated gut check on the reasonableness of the outcome and then we’ll go back and correct outliers potentially reforecast and then we’ll publish. Okay?
So, there are really three phases to what we do and I would think three phases to most machine learning approaches. And so, when we talk about machine learning that’s really what we’re talking about is that that really generally three-phase process and then the feedback loop that always goes back into that.
MG: Yeah. No that makes sense. Let’s get…
TN: That’s really boring after a while.
MG: No, no, no but I think that’s it’s part of what I want to do with these spaces is try to get people to understand you know beyond sort of just the headline or the thing that is thrown out there. As a term to what does that actually mean in practice you don’t have to know it fully in depth the way the that you do. But I think having that context is important.
TN: I would say on the idea generation side and on the risk management side right now. Okay? Now the other thing that I didn’t cover is obviously we’re doing markets but we also do… we use our platform to automate the budgeting process within enterprises. Okay? So, we work with very large organizations and the budget process within these large organizations can take anywhere from say four to six months. And they take hundreds of people. And so, we take that down to really interacting with one person in that organization and we do it in say less than 24 hours. And we build them a continuous budget every month.
Once accounting close happens we get their new data and then we send them a new say 18-month forward-looking forecast for them. So, their FPA team doesn’t have to dig around and beg people for information and all that stuff. So, some of this is on the firm event could be on the firm evaluation side, as well. Right? How will the firm perform? Nobody’s using us for that but the firms themselves are using that to help them automate their budgeting process. So, some of that could be on this a filtering side and the idea generation side, as well.
So, we do not force our own GL structure onto the clients. We integrate directly with their SAP or Oracle or other ERP database. We take on their GL structure at whatever levels they want. We have found that there is very little deterioration from say, the second or third level GL to say the sixth or seventh level GL, in terms of the accuracy of our forecast. And when we started doing this it really surprised me. We do a say a team level forecast for 10, 12 billion organizations, six layers down within their GL. And we see very little deterioration when we go down six levels than when we do it at say two levels. Which is you know it really to me it speaks to the robustness of our process but would we consider Anaplan a competitor not really, they’re not necessarily doing the kind of a budget automation that we’re doing at least, that I’m aware of. I know that there are guys like Hyperion who do what we’re doing but again their sophistication isn’t necessarily. What we’re doing and they do a great job and Hyperion is a great organization. I think Oracle gave them a new name now but they’re not necessarily using the same machine learning approaches that we’re using. And our clients have told us that they don’t get the same result with using that type of say ERP originated or ERP add-on budgeting process.
Yep. So, I would say we can’t we can do company-specific information for a customer if that’s what they want. Okay? We don’t necessarily have that on our platform today aside from say individual ticker symbols. Okay? But we’re not forecasting say the P&L of Apple or something like that or the balance sheet of Apple. Something we could do in a pretty straightforward manner but we do that on a customer-by-customer basis.
So, what we’re forecasting right now are currency pairs, commodities about 120 commodities and global equity indices. Okay? We are Beta testing individual equity tickers and we probably won’t introduce those fully on the platform until we have our daily interval forecast ready to go to market. But those are still we’re still working some kinks out of those and we’ll have those ready probably within a few months.
MG: Okay. So, let’s talk about commodities here for a bit tonight. Obviously, this is where a lot of people’s attention has gone to. What kind of variables and I know you said you have a whole bunch of variables that are being incorporated here but are there certain variables in particular when it comes to oil and other commodities that have a higher predictive power than others.
TN: There are I think one of the stories that I tell pretty often and this really shocks people is when we look at things like gold. Okay? I’m not trying to deflect from your oral question but just to you know we’ve spoken with the number of sugar traders over the years. Okay? And so, we tell them that say the gold price and the sugar price there may not necessarily be a say short term say correlation there but there is a lot of predictive capability there and we talk them through why. And I think the thing that we get out of the machine learning approach and we cast a wide net. We’re not forcing correlations is that we’ll find some unexpected say drivers. Although drivers implies a causal nature and we’re not trying to imply causality anywhere. Okay?
We’re looking at kind of co-movement in markets over time and understanding how things work in a lead lag basis with some sort of indirect causality as well as say a T0 or current state movement. So, with crude oil you know there are so many supply side factors that are impacting that price right now, that I can’t necessarily point to say another commodity that is having an impact on that. It really is a lot of the supply side and sentimental factors that are impacting those prices right now.
MG: That makes a lot of sense. And I’m curious how did you mention it’s I think the intervals once a month. Right? So, given the speed with which inflation has moved and yields have moved how does a machine learning process adapt to sudden spikes or massive deltas in in variable movement. Right? Because there’s always a degree of randomness going back to error. Right? And you can make an argument that the larger move is the that may actually be more error but I think that’s an interesting discussion.
TN: So, I’ll tell you where we were say two years ago when 2020 hit versus today. Okay? So, in March of 2020, April 2020 everything fell apart. I don’t think there were any models that caught what was going to happen. It was an exogenous event that hit markets and it happened very quickly. So, in June, I was talking with someone who is with one of the largest software companies in the world and they said “Hey has your AI caught up to markets yet because ours is still lost” And you guys would be shocked if I told you who this was because you would expect them to know exactly what’s going to happen before it happened. Okay? I’ll be honest I think it was all of them but the reality is you know Michael you where you were saying that ML is just regression analysis.
I think a lot of the large firms that are doing time series forecasting really are looking at regression and derivatives of regression as kind of their only approaches because it works a lot of the time. Right? So, we had about a two-month delay at that point and part of it was because… So, by June we had caught up to the market. And we had started in February to iterate twice a month, we were doing once a month; I hope you guys can understand with machine learning two factors are we’re always adjusting our algorithms. Okay? We’re always incorporating new algorithms. We’re always you know making sure that we can keep up with markets because you cannot be static in machine learning. Okay? The other thing is we’re always adding capacity why? Because we have to iterate again and again and again to make sure that we understand the changes in markets. Okay?
So, at that time we were only iterating twice a month and so it took us a while to catch up. Guys like this major technology firm and other major technology firms they just couldn’t figure it out. And I suspect that some of them probably manually intervened to ensure that their models caught up with markets. I don’t want to accuse any individual company but that temptation is always there. Especially, for people who don’t report their error. The temptation is always there for people to manually intervene in their forecast process. Okay?
So, now, today if we look for example at how are we catching changes in markets. Okay? So, if I look at the S&P 500 for April for example, our error rate for the S&P 500 for April I think was 0.6 percent. Okay? Now in May it changed it deteriorated a little bit to I think four or six percent, I’m sorry I don’t remember the exact number offhand but it deteriorated. Right? But you know when there are dramatic changes because we’re iterating at least once a week, if not twice a week we’re catching those inflections much much faster. And what we’re having to do, and this is a function of the liquidity adjustments, is where in the past you could have a trend and adjust for that trend and account for that trend. We’re really having to our algorithms are having to select more methodologies with recency bias because we’re seeing kind of micro volatility in markets. And so again…
MG: So, kind of like the difference between a simple moving average versus like an exponential moving average. Right? Where you’re waiting the more recent data sooner.
TN: It could be. Yeah.
TN: Yeah. That’s a very very simple approach but yeah it would be something like that, that’s right. Yeah. What so when we work with enterprise customers that level of engagement is very tight because when we’re getting kind of the full set of financial data from a client obviously, they’re very vested in that process. So, that’s different from say a small portfolio manager subscribing to RCF futures product where we’re doing forecasts and they have their own risk process in place. And they can do whatever they want with it. Right? But again, with our enterprise clients we are measuring our error so they can see the result of our continuous budgeting process. Okay?
So, if we’re doing let’s say, we launch with a customer in May, they close their mate books in June get them over to us redo our forecast and send it over to them and let them know what our error rate was in May. Okay? So, they can decide how we’re doing by department, by team, by product, by whatever based upon the error rates that we’re giving at every line item. Okay? So, they can select and we’re not doing kind of capital projects budgets we’re doing business as usual budgets so they can decide what they want to take and what they don’t want to take. It’s really up to them but we do talk through that with them and then over time they just start to understand how we work and take it on within their own internal process.
MG: So, back a little bit Tony. So, you mentioned you do this machine learning forecasting work when it comes to broad economics, markets and currency; of those three which has the most variability and randomness in other words which tends to have a higher error? Whenever you do any kind of machine learning to try to forecast what comes next?
TN: I would say it depends on the equity market but probably equity markets when there are exogenous shocks. So, our error for April of 2020 again, we don’t hide this from anybody it was not good but it wasn’t good for anybody. Right? And so, but in general it depends on the equity market but some of the emerging equity markets, EM equity markets are pretty volatile.
We do have some commodities like say rhodium for example. Okay? Pretty illiquid market, pretty small base of people who trade it and highly volatile. So, something like rhodium over the years our air rates there have not necessarily been something that we’re telling people to use that as a basis to trade but obviously, it’s a hard problem. Right? And so, we’re iterating that through our ML process and looking at highly volatile commodities is something that we focus on and work to improve those error rates.
MG: Here, I hope you find this to be an interesting conversation because I think it’s a part of the of the way of looking at markets, which not too many people are themselves maybe using but is worth sort of considering. Because I always make a point that nobody can predict the future but we all have to take actions based on that unknowable future. So, to the extent that there might be some data or some conclusions that at least are looking at variables that historically have some degree of predictive power. It doesn’t guarantee that you’re going to necessarily be better off but at least you have something to hang your hat on. Right? I think that’s kind of an aspect to investing here.
Now, I want to go a little bit Tony to what you mentioned earlier you had lived abroad for a while in Europe. And when I was starting to record these spaces to put up on my YouTube channel the first one, I did that on was with Dan Arvis and the topic of that space was around this sort of new world order that seemed to be shaping up. I want you to just talk from a geopolitical perspective how you’re viewing perhaps changing alliances because of Russia, Ukraine. And maybe even dovetail that a little bit into the machine learning side because geopolitics is a variable. Which is probably quite vault in some periods.
TN: Yeah, absolutely. Okay. So, with the evolving geopolitical order I would say rather than kind of picking countries and saying it’s lining up against x country or lining up with x country or what country. I would say we’ve entered an era of opportunistic geopolitics. Okay? We had the cold war where we had a fairly static order where people were with either red team or blue team. That changed in the 90s of course, where you kind of had the kind of the superpower and that’s been changing over the last say 15 years with say, China allegedly becoming kind of stronger and so on and so forth. So, but we’ve entered a fairly chaotic era with say opportunistic macroeconomic relation or sorry, geopolitical relationships and I think one of the kinds of top relationships that is purely opportunistic today is the China-Russia relationship.
And so, there’s a lot of talk about China and Russia having this amazing new relationship and they’re deep. And they’re gonna go to war together or whatever. We’ve seen over the past say three, four months that’s just not the case. And I’ve been saying this for years just for a kind of people’s background. Actually, advised the Chinese government the NDRC which is the economic planning unit of the central government on a product or on an initiative called the belt and road initiative. Okay? I did that for two years. I was in and out of Beijing. I never took a dime for it. I never took expense reimbursement just to be clear, I’m not a CCP kind of pawn. But my view was, if the Chinese Government is spending a trillion dollars, I want to see if I can impact kind of good spend for that. So, I have seen the inside of the Chinese Government and how it works and I also in the 80s and 90s spoke Russian and studied a lot on the Russian Government and have a good idea about how totalitarian governments work.
So, I think in general if we thought America first was offensive in the last administration then you really don’t want to learn about Chinese politics and you really don’t want to learn about Russian politics because they make America first look like kindergarten. And so, whenever you have ultra-ultra-nationalistic politics, any diplomatic relationship is an opportunistic relationship. And I always ask people who claim to be China experts but say please tell me and name one Chinese ally. Give me one ally of China and you can’t, North Korea, Pakistan. I mean, who is an ally of China there isn’t an ally of China. There is a transactional opportunistic relationship with China but there is not an ally with China.
And so, from a geopolitical perspective if you take that backdrop looking at what’s happening in the world today it makes a whole lot more sense. And a lot of the doomsayers out there saying China is going to fall and it’s going to have this catastrophic impact. And all this other stuff, the opportunism that we see at the nation-state level pervades into the bureaucracy. So, the bureaucracy we hear about Xi Jinping. And Xi Jinping is almost a fictional character. I hate to be that extreme on it but there is the aura of Xi Jinping and there is the reality of Xi Jinping, just a guy, he’s not Mao Zedong. He doesn’t have the power that supposed western Chinese experts claim that he has. He’s just a guy. Okay?
And so, the relationships within the Chinese bureaucracy are purely transactional and they are purely opportunistic. So again, if you take that perspective and you look at what’s happening in geopolitics, hopefully you can see things through a different lens.
MG: Now, I’m glad you’re framing that in those terms because I think it’s very hard for people to really understand some of these dynamics when it’s almost presented like a like the story for a movie. Right? For what could be a conflict to come by the media because and it’s almost overly simplified. Right? When you hear this type of talk. So again, I want to go back into how does that dovetail into actual data. Right? Maybe it doesn’t at all. When you have some of these dynamics and you talk about market clearing data, you’re going to probably see mark movement somewhat respond off of geopolitical changes. Talk about anything that you’ve kind of seen as far as that goes and how should investors consider geopolitical risk or maybe not consider geopolitical risk?
TN: Yeah, I think, well when you see geopolitical adjustments today all that really is… I don’t mean overly simplified but it’s a risk calibration. Right? So, you know Russia invades Ukraine, that’s really a risk calibration. How much risk do we want to accept and then what opportunities are there? Right?
So, when you hear about China, you have to look at what risk is China willing to accept for actions that it takes? Keeping in mind that China has a very complicated domestic political environment with COVID shutdown, lockdowns and all of this stuff. So, having worked with and known some really smart Chinese bureaucrats over the years, these guys are very concerned with the domestic environment. And I don’t although there are idiot you know generals and economists here and there who say really stupid stuff about China should take over TSMC and China should invade Taiwan, these sorts of things. My conversations over the years have been with very pragmatic and professional individuals within the bureaucracy.
So, do I agree with their policies? Not a lot of them but they are well thought out in general. So, I think just because we hear talk from some journalist in Beijing who lives a very sheltered life about some potential thing that may happen. I don’t think we necessarily need to calibrate our risk based on the day-to-day story flow. I think we need to look at like… so there’s a… I’m sure you all know who Leland Miller is in China beige book like?
MG: Yeah, he’s not too long ago.
TN: Yeah. He has a proxy of the Chinese economy and that’s a very interesting way to look at an interesting lens to look through China or through to look at China or whatever. But so, I think that the day-to-day headlines, if you follow those, you’re really just going to get a lot of volatility but if you try to understand what’s actually happening, you’ll get a clearer picture. It’s not necessarily a connection of a collection of names in China and the political musical chairs, it’s really asking questions about how does China serve China first. What will China do to serve China first and are some of these geopolitical radical things that are said do they fit within that context of China serving China first? So, that’s what I try to look at would I be freaked out if China invaded Taiwan? Absolutely. I think everybody would right but is that my main scenario? No, it’s not.
MG: In terms of the data inputs on the machine learning side how granular is the data meaning? Are you looking at where geographically demand might be picking up or is it simply this is what the price is and who cares the source? Because again with hindsight if you knew that the source of China and kind of had a rough sense of the history of Russia-Ukraine maybe that could have been an interesting tell that war was coming.
TN: Yes or No. To be honest it had more to do with the value of the CNY. Okay? And I’ll tell you a little bit about history with the CNY. We were as far as I know, the only ones who called the CNY hitting 6.7 in August of 2019 with a six-month lead time. And so, we have a very good track record with USD-CNY and I would argue that China’s buying early in 2022 had a lot more to do with them from a monetary policy perspective needing to devalue CNY. So, they were hoard buying before they could devalue the CNY and I think that had a lot more to do with their activity than Russia-Ukraine. Okay? And if you notice they’ve made many of their buys by mid-April and once that happened you saw CNY, go to 6.8. Right? It’s recovered a little bit since then but China has needed to devalue the CNY for probably at least nine months. So, it’s long overdue but they’ve been working very hard to keep it strong so that they could get the commodities they needed to last a period of time. Once they had those commodities, they just let the parachute go and they let it do value to 6.8 and actually slightly weaker than 6.8.
MG: The point of the devaluation is interesting. I feel if I had enough space but we were talking about the Yen and what’s happened there. And this observation that usually China will start to devalue when they see the end as itself going through its own devaluation.
How does some of those cross correlations play out with some of the work that on machine learning you’re doing? Because there’s a human element to the decision to devalue a currency. Right? So, the historical data may not be valid I would think because you might have kind of a more humanistic element that causes the data to look very different.
TN: Well, they’re both export lab economies. Right? And we’ve seen a number of other factors dollar strength and we’ve seen changing consumption patterns. And so, yes when Japan devalues you generally see China devalue as well but also, we’ve seen a lot of other activities in on the demand-pull side and on the currency side especially with the US dollar in… I would say over the last two quarters. So, yes, that I would say that the correlation there is probably pretty high but there are literally thousands of factors that contribute to the movement of those of those currencies.
MG: Is there anything recently Tony in the output that machine learning is spitting out that really surprises you? That you know… And again, I understand that there’s a subjective element which is our own views on the world and of course then the pure data. But I got to imagine it’s fascinating sometimes if you’re sitting there and seeing what’s being spit out if it’s surprising. Is there anything that’s been kind of an outlier in in the output versus what you would think would likely happen going forward?
TN: Yeah. You know, what was really surprising to me after we saw just to stick on CNY for a minute because it’s the first thing that comes to mind, when we saw CNY do value to 6.8. I was looking at our forecast for the next six months. And it showed that after we devalued pretty strong it would moderate and reappreciate just a bit. And that was not necessarily what I was hearing say in the chatter. It was kind of “okay, here we go we’re going to go to seven or whatever” but our data was telling us that that wasn’t necessarily going to happen that we were going to hit a certain point in May. And then we were going to moderate through the end of the year. So, you know we do see these bursty trends and then we see you know in some cases those bursty trends continue for say an integer period. But with CNY while I would have on my own expected them. I expected the machines to say they need to keep devaluing because they’ve been shut down and they need to do everything they can to generate CNY fun tickets. The machines were telling me that we would you know we’d see this peak and then we would we would moderate again and it would kind of re-appreciate again.
So, those are the kind of things that we’re seeing that when I talk about this it’s… Oh! the other thing is this: So, in early April we had a we have people come back to us on our forecast regularly who don’t agree with what we’re saying and they complain pretty loudly.
MG: So, what do you say I talk when I hear that because whenever somebody doesn’t agree with the forecast, they are themselves making a fork.
TN: Of course. Yeah. Exactly. Right? Yeah, and so this person was telling us in early April that we’re way wrong that the S&P was going to continue to rally and you know they wanted to cancel their subscription and they hated us and all this other stuff. And we said okay but the month’s not over yet so let’s see what happens this was probably a week and a half in April. And what happened by the end of April things came in line with our forecast and like I said earlier we were like 0.4 and 0.6 percent off for the month. And so that person had they listened to us at the beginning of the month they would have been in a much better position than they obviously ended up being in. Right? And so, these are the kind of things that we see on a… I mean, we’ve got hundreds of stories about this stuff but these are the kind of things that we see on a regular basis. And we mess up guys I’m not saying we’re perfect and but the thing that we when we do mess up, we’re very open about it. Everything that we do is posted on our on our website. Every call we make, every error we have is their wars and all. Okay? And so, we’re not hiding our performance because if you’re using our data to make a trade, we want you to understand the risk associated with using our data. That’s really what it comes down to.
MG: It reminds me of back in 2011 and in some other periods I’ve had similar situations, where I was writing and I was very adamant in saying the conditions favored a summer crash. Right? I was saying that for the summer and the market should be going up and people would say oh where’s your summer crash and I would say this summer hasn’t started. Like it’s amazing how people, I don’t know, what it is, I don’t know if it’s just short-termism or just this kind of culture of constantly reacting as opposed to thinking but it is it is remarkably frustrating.
Going back to your point at the very beginning being entrepreneur don’t do it, that you have to build a business with people and customers who in some cases are just flat out naïve.
TN: That’s all right though. That’s a part of the risk that we accept. Right?
MG: Yeah, the other thing right now that happens with every industry but from the entrepreneur’s standpoint. It’s what you’re doing the likely outcome of your product of your service. You’re trying to communicate that to end clients but then in the single role of the die the guy the end client who comes to you exactly for that simply because they disagree with you know the output, now says I want out.
TN: Oh! Yeah! Well, your where is your summer call from 2011 the analogy today is where is your recession call. Right? So, that’s become the how come you’re not one of us calls right now. So, it’s just one of those proof points and if you don’t agree with that then you’re stupid.
So, I would say you never finish with that there is always a consensus and a something you’re you absolutely, must believe in or you don’t know what you’re talking about.
MG: Yeah, well, thankfully. What you’re talking about so appreciate everybody joining this space Tony the first time you and I were talking. I enjoyed the conversation because I think it said on investing and I encourage you to take a look at Tony’s firm and follow him here on twitter. So, thank everybody. Thank you, Tony and enjoy.
“What you do makes a difference, and you have to decide what kind of difference you want to make,” said Dr. Jane Goodall.
From high tech waste management solutions to tools that make the making of consumer products more ethical, startups are innovating solutions for a greener future. To empower their sustainable solutions, many tap into the benefits of Oracle for Startups to gain exposure, more customers, and stronger infrastructure.
Startups and enterprises working together can help the business world lead the change toward greater sustainability. While some businesses are naturally greener than others (and not all of them are focused on combating climate change), every company can make adjustments to reduce its carbon footprint or mitigate negative environmental effects.
If everyone makes small changes, they end up making a big difference, or as Sir David Attenborough puts it: “If working apart, we are forces powerful enough to destabilize our planet, surely working together we are powerful enough to save it.”
This Earth Day, we’re celebrating how some of the more eco-focused members of Oracle for Startups help other businesses make a positive impact on the planet.
Waste not, want not
One of the simplest ways to make a positive impact is to waste less. Startups help other businesses minimize waste and maximize efficiency in several ways. Complete Intelligence, for example, uses AI and strong predictive analytics to help companies waste less – whether that’s materials, time, or money. “It might not be as intuitive a sustainability play as, say, building solar panels, but it is important nonetheless,” said analyst Jeremy Cox in a report about the startups bringing sustainability to energy and utilities.
Tracifier created a blockchain-based traceability application to reduce food fraud and, therefore, food waste. “Blockchain allows for an accurate and transparent record of each of several certification processes, making forgery nearly impossible,” said Mina Kordi, CEO and cofounder of the startup, which is based in Hamburg.
Faradai (formerly Reengen) turned rooting out energy waste in stores, offices, and other commercial properties into a global business. Their hardware agnostic IoT platform analyzes sensor data to uncover energy and operational insights. One of our favorite success stories involves the company’s work with a bank that found that ATMs with high energy usage often had outdated exterior lighting. A simple change in lighting reduced the site’s energy consumption by 59%.
More consumers are making purchases based on ethics and environmental concerns, and startups are serving up clever tech to the companies selling us greener products from clothes to cars.
Circulor makes it easier for automobile giants and other businesses to spot the weakest links in their supply chains so they can improve them and attract conscientious consumers. The London-based startup specializes in tracking raw materials using blockchain and artificial intelligence. It provides ‘traceability-as-a-service’ to verify responsible sourcing, underpin effective recycling, and improve efficiency, so consumers can buy new products with confidence.
When it comes to the fashion industry, green is the new black.
Fashion houses are keen to embrace the public’s appetite for everything eco-friendly. The blockchain-based supply chain transparency platform offered by retraced gives fashion brands a boost for their inventory efficiency and sustainability credentials. The German startup uses the Oracle Blockchain Platform to create a supply chain management tool, enabling companies to map and verify their data, including certified details about raw materials, textile manufacturers, fabric dyers, designers, craft people, factories, and sewers. As retraced gathers information, two things happen: brands can collect and analyze supply chain data, and a QR code is automatically generated, which consumers can scan to discover information about ethical sourcing and sustainability.
Startups are playing a vital part of cleaning our planet. Oceanworks is one of them. The startup is intent on banishing plastic from the ocean and is doing its bit by creating an online marketplace for recycled plastic materials and products. It has more than 100 customers and a supply capacity of more than 190,000 tons of ocean plastic a year from collection sites across six continents.
Based in Los Angeles, the cloud startup runs a track-and-trace application to certify that the plastic that manufacturers source really is recycled ocean plastic so their customer base (which includes Fortune 500 companies) can prove their eco credentials.
Calling climate crusaders
If your startup business is on a mission to save the world, Oracle for Startups can help. We offer the technical tools and one-on-one mentoring startups need to make the world a better place. From free cloud credits and access to Oracle Blockchain to introductions to customers, Oracle for Startups offers the support your startup needs to make a real difference.
Analyst Jeremy Cox was watching the United Nations Climate Change Conference news while researching his recent report on startups offering sustainability solutions for the energy and utility sector. The timing was especially fortuitous for another reason: Cox was about to welcome a grandchild.
Sustainability is top of mind when you start thinking about future generations. Cox took notes and quoted environmentalist Sir David Attenborough to lead off the report, “If working apart we are forces powerful enough to destabilize our planet, surely working together we are powerful enough to save it.”
What is more powerful than startups harnessing the cloud and enterprise expertise to bring their novel sustainability ideas to the market?
The idea for Evreka came after the founders noticed empty garbage trucks driving back to the depot. The drivers drove scheduled routes regardless of whether there was garbage to collect, wasting gasoline and staffing. Evreka’s first product was a sensor that attaches to garbage collection carts to show the cart’s position – ready to be emptied or not at the curb. The information helps waste collectors orchestrate efficient pickup schedules.
The founders have since expanded their vision to include tackling inefficiencies in the entire waste management lifecycle, looking to extract materials (other than the obvious recyclables) that could be sold to manufacturers and reused as raw ingredients for different industries. They offer the data as a SaaS solution. The platform runs on Oracle Cloud Infrastructure (OCI), a choice the company made because of “(Oracle’s) global reach, second-generation OCI technology, and expansion of highly secure data centers.”
Why predictive analytics is vital to powering sustainability
Complete Intelligence joined Oracle for Startups because founder Tony Nash looked to the Oracle cloud to help power his machine learning platform designed for smarter revenue, expense, cost, and investment planning decisions.
Being super agile is critical in today’s global business world, including as it relates to understanding, measuring, forecasting for sustainability. “What I discovered from talking with Tony was that strong predictive analytics helps waste less – whether that’s materials, time or money,” Cox says. “It might not be as intuitive a sustainability play as, say, building solar panels, but it is important nonetheless.”
An AI-powered global intelligence platform for strategic and tactical procurement and investment decisions
Watch our interview with founder Tony Nash and check out Cox’s deep dive on Complete Intelligence:
Cloud storage opens many opportunities for enterprises and startups to run more efficiently but sending all that data to the cloud and instantly making it available is an energy hog. Danish startup GroenSky is meeting that challenge with an approach that makes less energy-intensive archived storage just as appealing and easy to access as live storage.
Writes Cox, “GroenSky allows customers to choose how they store their files. Those that are only rarely accessed, typically around 80% or more, can be placed in archived storage that doesn’t consume power except when accessed or moved to regular, live storage. A real-time calculator allows customers to see how much CO2 they can save.”
GroenSky founder Pierre Bennorth Cox he chose Oracle Cloud Infrastructure because of its security features, global data centers, and commitment to power its cloud with 100% renewable energy by 2025.
Combing millions of data points to find the energy wasters
The founders of Faradai (also known as Reengen) have turned rooting out energy waste in stores, offices, and other commercial properties into a global business. Their hardware-agnostic IoT platform analyzes sensor data to uncover energy and operational insights.
One of our favorite success stories involves the company’s work with a bank that found that ATMs with high energy usage often had outdated exterior lighting. A simple change in lighting reduced the site’s energy consumption by 59%.
The company joined Oracle for Startups and told Cox how they’ve reaped the benefits. “Apart from the performance and security advantages of the Oracle Cloud Infrastructure,” Cox writes. “Oracle has been instrumental in opening doors to its large enterprise customers throughout the world. Faradai has also benefitted from further exposure by speaking at Oracle conferences in the Middle East. As (the founder) said, ‘we get great leverage in industrial B2B sales and have had a very positive reception from the Oracle sales teams and now work even closer with them.”
We ask Cox to interview startups because he often finds insights that we haven’t discovered. This project was no different. “Each of the startups highlighted in this report is making a significant difference that benefits customers and society, providing real hope that we can all make a difference collectively.”
Are you building the next great sustainability solution? Join us to save 70% on cloud and scale your business with global connections.
Complete Intelligence – a fully automated and globally integrated AI platform for smarter cost and revenue planning.
Complete Intelligence provides actionable, accurate, and timely data to make better investment and procurement decisions.
The platform provides an integrated global model to ensure that actions in one market, country, or sector of the economy are reflected elsewhere in markets, industries, and the global economy. International trade, economic indicators, currencies, commodity prices, and equity indices are all factored in to create a proxy of the global economy. Over 1200 industries in more than 100 countries are covered!
Based on interviews with Tony Nash, founder, CEO, and Chief Data Scientist, this brief report introduces Complete Intelligence, one of a growing number of highly innovative companies supported by the Oracle for Startups program. The company, founded in 2019, is already significantly improving the forecasting and budget planning of a variety of large corporations through its advanced AI-driven intelligence platform. The theme for this month is around startups in the energy and utility sector and how they are innovating, changing the competitive landscape, and contributing to sustainability. CX-Create is an independent IT industry analyst and advisory firm, and this report is sponsored by the Oracle for Startups program team.
The business context for Complete Intelligence
Commodity price volatility and a post-pandemic surge in demand drive the need for more timely and accurate forecasting Businesses coming out of lockdown have increased demand for commodities, from energy supply to raw materials for their products. In Europe, benchmark prices for natural gas to power their factories and heat their buildings have risen from €16 megawatt-hour in January 2021 to €88 in October. This, in turn, has sent electricity prices soaring. (Source: Euronews). While some have locked in prices through forward-buying, others have been exposed and seen profit margins plummet, unable to pass on price hikes to their customers.
But it is not just energy prices that are volatile. Semiconductor chip shortages have impacted many industries that depend on them, from automotive to electronic household goods manufacturers, putting a brake on their post-pandemic recoveries despite strengthening demand.
The growing demand for clean and sustainable energy sources and precious metals, like copper and lithium that power batteries have also seen tremendous volatility. As major industrial companies digitally transform their organizations and business models seeking elusive growth, the importance of data and AI are increasingly recognized as fundamental to success.
Forecasting and budgeting needs data science, not spreadsheets The ability to sense change, respond quickly and adapt rapidly relies on a synthesis of massively increased volumes and varieties of data, both from operational and external sources. Data volumes are too complex for manual approaches and spreadsheets and require AI to extract insight and meaning from this complex array of external demand and supply signals. The old industrial-age planning approaches can’t cope. They are too slow, involve armies of accountants and analysts, and political wrestling between departmental heads, and are often based on opinion and inaccurate forecasts leading to erroneous budgeting decisions.
Complete Intelligence provides the accurate evidence base for budgeting and forecasting decisions
When markets are relatively calm and stable, the cycle of annual planning and budgeting makes sense. But amidst continual volatility and dramatic accelerated change, the planning cycle is too slow. It fails to mitigate the risks unfolding at such speed and is impacted by a confluence of so many variables, like extreme weather, scarcity of raw materials, pandemics, and weakened supply chains. An array of intelligent internal and external feedback loops is needed to mitigate risks and optimize resources in pursuit of the company’s goals. This is what Complete Intelligence provides with its integrated and modular intelligence platform.
• Complete Intelligence provides the accurate evidence base for budgeting and forecasting decisions • The Complete Intelligence Platform consists of three modules – CI Futures, RevenueFlow and CostFlow • Forecast accuracy has rapidly improved, and error rates are now around 2%, which compares favorably with traditional methods and error rates of 35% or more
Complete Intelligence, the story so far
Tony Nash, founder, CEO, and Chief Data Scientist, is steeped in market intelligence. A former VP of market intelligence firm IHS (now IHS Markit), and The Economist Intelligence Unit, where he was Global Director Consulting and Custom Research. He observed that large international companies he had supported typically followed an annual budgeting cycle based on often inaccurate or opinion-based data. It was not unusual to find large teams of people, sometimes several hundred involved in the process and heavily reliant on gathering data from multiple departments in complicated spreadsheets. The process could last several months, and the variance between forecasts and actuals was often above 35%, which could erode profits or tie up resources unnecessarily.
Trial, error, and persistence As a data scientist familiar with cloud technologies, he developed algorithms to improve forecast accuracy and a complete process from data ingestion to forecasting and testing the results. He started developing the machine learning ML algorithms in 2017 while still consulting in Asia from his base in Singapore. His first iteration failed to produce a level of accuracy that would provide a sufficiently compelling proposition. He wanted to get down to an error rate of no more than 5%-7%. He adopted the ‘ensemble’ approach covering thousands of different scenarios layering external data on commodities such as the copper price with a customer’s actual costs, identified in their general ledger.
Ready for launch late 2019 In 2019, Nash returned from Singapore and set up his company in The Woodlands, near Houston, Texas. He continued his work on the algorithms and developed a commercial product ready to launch in early 2020. And then Covid-19 struck.
Through Covid-19, companies first tried to understand the changing environment, then remained risk-averse until public health, business environment, and supply chains became more stable. This has been a challenge for a cutting-edge machine learning firm like Complete Intelligence. It is only as the environment has begun to stabilize that enterprises have sought new solutions to legacy problems. With that has come a renewed interest in Complete Intelligence and deployment at a large scale.
Solution overview The Complete Intelligence Platform consists of three modules The Complete Intelligence Platform hosted on Oracle Cloud Infrastructure (OCI) consists of three forecasting modules:
•CI Futures – to forecast market trends. Covering over 1,400 industries in more than 100 countries and a database of over 16 billion data points from proprietary and publicly available data. Millions of learning algorithms are used, which factor in the most recent global events.
• RevenueFlow – provides accurate results for demand and forecast sales and revenue projections.
• CostFlow – to enhance product line profitability and improve supply chain and procurement outcomes.
Figure 1. provides a diagram of the Complete Intelligence Platform
Figure 1: Complete Intelligence Platform by Complete Intelligence.
Market data is ingested from multiple trusted data sources like national statistical agencies, multilateral banks, multilateral government bodies, commodities exchanges, bilateral trade bodies and combined with the client’s data from their general ledger. A multi-layer testing and validation process used to ensure the accuracy of the data to be used in any forecast. Third-party data is gathered via internet spiders and APIs.
The platform provides an integrated global model to ensure that actions in one market, country, or sector of the economy are reflected elsewhere in markets, industries, and the global economy. International trade, economic indicators, currencies, commodity prices, and equity indices are all factored in to create a proxy of the global economy.
A comprehensive list of futures, currencies, and market indices is covered and accessed through a highly graphical and easy-to-use interface. Almost 1,000 assets, with historical data from 2010 and forecasts over a one-year horizon, are provided. More assets are being added all the time.
The platform is designed around three attributes: • A globally integrated model • A data-driven process without human intervention in the output • A simple means of interfacing with the platform.
The platform can be connected to existing ERP systems and automatically upload pricing data from the general ledger at a very granular level for each item.
The Complete Intelligence Platform supports a variety of use cases: • Supply Chain & Purchasing Optimization – help lower costs, anticipate risks, and provide input to sourcing strategies. • Sales and market entry strategies – by identifying higher growth markets and optimizing resources • Strategic Financial Planning – identifying growth markets and fine-tuning resource allocations in each market to minimize exposure to currency fluctuations. • Mergers and acquisitions – provide a snapshot of cost structures and projections of future costs and profitability of target acquisitions.
Forecast accuracy has rapidly improved, and error rates are now around 2% Nash’s persistence has resulted in significant levels of forecasting accuracy. A twelve-month forecast now sees error rates around 2%, which gives users considerable confidence compared with traditional methods, where the error rates are often above 35%.
As well as dramatically improving forecast accuracy on markets, revenues, and costs, the onboarding process to going live is a matter of a few weeks. After that, forecasting takes hours, not months.
Successes to date
While still a relatively new company, Complete Intelligence has already proved its value to several large companies.
• A major petrochemical company wanted to improve its predictive intelligence capability for feedstocks and refined products. They asked Complete Intelligence to examine nine categories across crude oil, gasoline, diesel, natural gas, and gas-to-liquid (GTL) products. Monthly forecast averages are provided by category with extremely low differences from actual results on the order of 3% or less.
• A global furniture company wanted a more explicit link between their sales and revenue planning and their sales teams in China. Complete Intelligence built a sales forecasting model that more clearly identified and utilized market demand drivers and connected these directly to their business. An analytics-based approach to identify the drivers of sales by city and industry. Complete Intelligence built a city and industry-level forecasting tool that determined the company’s growth trajectory and provided recommendations to support the direction and transition of their sales teams. • A global chemicals company needed a better understanding of the trends for costs in their supply chain and a more precise way to manage margin expansion and contraction at the bill of material level. Complete Intelligence was commissioned to forecast factor inputs and currencies for the key categories. The forecasts were calibrated based on the component make-up of the bill of materials. This enabled the client to identify the direction of the materials pricing and the impact on their BOM. Through the process, the client learned how to anticipate cost movements and protect margins.
Current go-to-market model
Complete Intelligence sells directly to large organizations, mainly targeting CFOs and COOs with a broad view of their companies and strategic decisions.
The company also has strategic partnerships with Microsoft and is listed on the Azure Marketplace and with Oracle as part of the Oracle for Startups program and hosted on OCI.
Other partnerships with Bloomberg and Refinitiv allow for exchanging financial and market data and connection to their platforms.
More transparent accuracy reporting so customers can view accuracy/error for every line item
More robust and flexible data visualization for clients to utilize Complete Intelligence forecasts within their visual narratives
More sophisticated data science to account for detailed sentiment and other qualitative factors
Do-it-yourself forecasts for customers to do ad hoc forecasts for any data at any time. This will enable teams within a company to do their own sophisticated, reliable forecasts without waiting on their in-house market analysis or forecasting team with complicated macros and massive spreadsheet workbooks
Embedding Complete Intelligence forecast APIs into ERP and accounting software.
Oracle Cloud Infrastructure and the Oracle for Startups program prove their value to Complete Intelligence When asked what he felt about the relationship with Oracle and the Oracle for Startups program, Nash said, “Oracle Cloud Infrastructure is very flexible and secure. The Oracle for Startups team has been great. Oracle has been the most responsive and helpful of all our partnerships, connecting us to the right people to help with marketing, sales, or technical questions. I really feel that they want us to succeed. I’m a huge advocate of the Oracle for Startups program.’’
CX-Create’s viewpoint The Complete Intelligence Platform addresses a fundamental business need
Providing a global proxy model on markets, commodities, currency fluctuations, and many other aspects and making this easily accessible for business people will significantly improve strategic investment and procurement decisions. The emphasis on accurate and timely data supported by ML models will make it easier for business people to make informed decisions, stripped of personal bias. Digital transformation should lead to a more agile and responsive organization. The more progressive organizations will want highly attuned external signals that are constantly updated, enabling them to de-risk investment decisions and optimize resources for growth. Complete Intelligence provides for that.
How can leaders and finance teams enable business growth, innovation, and resilience through supply chain management (SCM) and digital transformation? And, how does sustainability affect supply chains? To answer these questions, we spoke with Jon Chorley, Chief Sustainability Officer and Group Vice President of Oracle, and Tony Nash, CEO & Founder of Complete Intelligence.
Jon Chorley is group vice president of product strategy for Oracle’s supply chain management (SCM) applications and leads the team responsible for driving the business requirements and product roadmaps for these applications. Chorley is also the chief sustainability officer for Oracle.
Tony Nash is the CEO and Founder of Complete Intelligence. Previously, Tony built and led the global research business for The Economist and the Asia consulting business for IHS (now IHS Markit).
Michael Krigsman: We’re discussing supply chain innovation and transformation and sustainability with Jon Chorley of Oracle and Tony Nash of Complete Intelligence. Jon, tell us about your role at Oracle.
Jon Chorley: I run the supply chain management strategy group at Oracle, responsible for our overall investment priorities and directions for our supply chain solutions. I also have the chief sustainability officer role at Oracle where I help coordinate all of our sustainability policies and practices for the Oracle Corporation and help drive some of those ideas and thoughts into the products and services we deliver to the market.
Michael Krigsman: Tony Nash, tell us about the focus of your work.
Tony Nash: Complete Intelligence, we’re a globally integrated and fully automated artificial intelligence platform for cost and revenue proactive planning. We do forecasting for enterprises and markets in areas like continuous cost budgeting, continuous revenue budgeting, automation of certain, say, forecasting tasks. We also offer agile budgeting and forecasting.
We measure our error rates, so that’s important that someone is planning, especially around supply chain. We’re trying to help people reduce the risks around their future costs.
Supply chains are very complex: time, cost, quality, all sorts of considerations. Our focus is on the cost element of it, and there are many other things and why we’re working with Oracle. They have so many other things to bring to the table that try to complement them on that side.
Michael Krigsman: You met Jon through the Oracle startup program. Just briefly tell us about that.
Tony Nash: Oracle for Startups program is a fantastic way for early-stage companies to integrate with the Oracle ecosystem. There is the Oracle technology product side of it, but there is also meeting people like Jon, meeting people like his colleagues, and the Oracle marketing team, Salesforce, and product teams. Amazing opportunities to understand how an organization like Oracle works and how a company like Complete Intelligence can come alongside them and enhance Oracle’s end customer experience for the better.
How did supply chains function during the disruptions of 2020?
Michael Krigsman: Jon, during the last year, supply chain became a household topic for pretty much everyone.
Jon Chorley: Yes.
Michael Krigsman: What did the last year tell us about the nature and the reality of supply chains?
Jon Chorley: Well, that they’re central to everything that makes the modern world. When you see an empty shelf and realize it’s an issue with the supply chain. Or you see a run on a product as some shortage or some challenge in some way. People now understand that the complicated infrastructure that brings those products to them is the supply chain.
As we’ve gotten into the more recent months where we’re looking at the vaccine distribution, people understand that yes, it’s a technical problem to produce the vaccine, but it’s also a supply chain problem to get it in people’s arms.
All of those things, I think, have helped take the supply chain from the back office, from the folks like Tony and I who work in it day-to-day, into the board room, which I think is very important. But also into the dining room. People now understand the importance and centrality to efficient supply chains.
Michael Krigsman: Jon, give us some insight into the kinds of weaknesses that this last year exposed in how we handle supply chains.
Jon Chorley: I think that there are a couple of areas there that I’d point out. One is we had a very uncharacteristic demand shock. There was a real change in short-term demand.
Some of that was upside. A lot of charcoal sold to power the grill. A lot of toilet paper.
Some of it was downside. Restaurants challenged, hospitality, and so on.
Those demand shocks forced people to look at different ways to look at their traditional forecasts. That is supportable by the kind of technology Tony and I can help deliver, but it does require people to look carefully at how they’re forecasting their demands. That’s one angle.
Another angle, I would say, is the overall concern about resiliency. A lot of folks looked at ways of single sourcing, for example. Maybe relying on goods out of Western China, for example.
All of those things had a lot of challenges, and that forced people to look at, was the single-sourcing strategy driven by cost only the right answer? Did they need to look at A) maybe simplifying their product lines a little bit, so they had more flexibility, and B) looking at alternate sources of supply? I think resiliency came a lot more to the fore.
Tony Nash: We’ve had even companies like semiconductor companies (who have been based in Asia) start to build facilities in the U.S. so that they can regionalize some of those supply chains and de-risk the downturn impacts of future shocks like this. Electronics manufacturers, other people who are assembling goods, or even some primary goods, are regionalizing their supply chains so that they don’t see huge impacts or any future issues like COVID or other shocks.
There’s at least a little bit of a buffer by region, which saves. It’s greener in terms of saving on the sea freight fuel and that sort of thing, but it also helps cushion any shocks on the supply side so consumers can get what they need when they need it.
Challenges associated with overseas manufacturing operations
Michael Krigsman: Jon, I’ve heard you talk in the past about the inherent challenge of manufacturing goods overseas (in China, for example) and the timeliness of getting them here in the U.S.
Jon Chorley: It has a lot of advantages in terms of costs, scale, and so on. But it does bake into your supply chain a certain fixed amount of time. That is fine if you have predictable demand. But if you have variable demand, it becomes a lot trickier to manage.
The same is true really of the innovation cycles. The speed with which you may want to innovate can be constrained by working those things from points of consumption (let’s say Europe, North America) and points of production (let’s say the East, China, Vietnam, and so on). Those are factors folks are considering.
I think, in some areas, certainly advances in things like automation and technologies like 3D printing, rapid prototyping, those things are changing the equation a little bit in terms of what constitutes the most cost-effective or the most efficient, or the most responsive approach to manufacturing. I think you’re going to see those factors gradually have more and more of a play as people develop new ways to balance those equations.
Tony Nash: Michael, that’s interesting because, as we look at how the history of supply chains have evolved from keeping POs on 3×5 notecards 30 years ago to the digitization of that, it started with EDI (electronic data interchange) from, say, the ocean lines and the airfreight firms so that you knew where your package was, all the way down to today where you have everything kept, let’s say, in a bill of material within an ERP system or a supply chain system.
What people have been doing for the past few years is really bill of material versioning, where you’re running scenarios on the same product configuration, of bill of materials for multiple locations, to understand where they should make a certain good. Those considerations are allowing people flexibility. They can make the time and cost tradeoffs to look at when they can have goods in a market, whether it’s seasonality or whether it’s some disruption or whether it’s some demand pop for some reason people may not know. Allowing people to run multiple bills of material or versions of bills of material allows them the flexibility to identify what they should produce where and what it should be made of.
Michael Krigsman: It sounds like this is a data and analytics problem.
Tony Nash: It is, and the way things have been done typically is, as a manufacturer, you sign a longer-term agreement for your raw materials with a vendor. They provide that for you to a certain point. You make it in factory A somewhere and then ship it out. Of course, there is not necessarily a single factory for any large company, but it’s a well-worn path.
We’ve had an atomization of that with mini manufacturing, or regional manufacturing, flexible manufacturing, so people can have localized versions or, like I said, seasonality. These sorts of things. Manufacturing and finance teams can only make those types of decisions with data and with automation. It’s a simpler way on how to make better business decisions.
Digital tansformation and sustainability in supply chain
Michael Krigsman: You need clarity around the goals and the strategy. You need the right kinds of data. Then you need the cultural willingness to innovate and do things differently. Is that an accurate way of summarizing?
Jon Chorley: I agree. I think you need to have some idea of where you’re going. Although, that probably is going to change. But you need to have that idea. You need to have the information, as Tony has discussed, that helps you navigate that path.
Then you need to be able to course-correct because we live in the real world, and nothing quite goes the way you expect it to. You need to be able to constantly course-correct.
Like I say, if you have a great set of headlights, you can see what’s coming. You’re coming to a cliff. If you have no brakes and no steering wheel, it’s a huge problem you’d rather not know.
The ability to course correct is like having brakes and a steering wheel. You need to be able to make those adjustments as things change around you. That means flexible systems, flexible processes, a willingness to look at new ways of doing things, cultural changes. All of those things become important.
Michael Krigsman: Tony, I have to imagine you spend a lot of time thinking about the sources of data as well as the machine learning models and other types of models that you create.
Tony Nash: I get excited about things like data governance, but most people don’t. I get excited about it because I understand that it helps to have much better forecasting applications and tools to make those decisions.
Yes, we’re thinking about the granularity, the frequency, the level of detail people have. Are they using the data that they have to make decisions today because it’s not just, let’s say, a cultural change of let’s rely on automation of things like forward-looking views or forecasting or proactive planning? It could also be a cultural change: are we looking at our data to make our decisions? How much of our data are we looking at? Are we looking at maybe the error rates of the way we plan? Are we looking back on that from time to time?
Although that may seem mundane and small, it’s actually very big for things like digital transformation because you have to take inventory of what you’re doing today so you can plan where you’re going tomorrow. As Jon said, it’s never going to go exactly to plan – never. I wish it would, but it never does. You have to understand yourself well today so that you can identify what’s possible.
Michael Krigsman: Jon, we’ve been talking about the complexities of supply chain. Let’s shift gears slightly and talk about the complexities of sustainability. How does sustainability intersect supply chain?
Jon Chorley: Most people would agree that supply chains are about making and moving physical goods around the world. That is a huge part of what’s impacting the environment. It’s a huge impact on sustainability.
The way we design those supply chains, historically, has been what I would call a linear supply chain. Which is we make a product, we sell a product, we forget the product. We then make another product, sell that product, and forget that product. It’s a fire and forget mentality, if you like – to some degree.
If we want to be sustainable, we need to think about the full lifecycle of those products and how they get recycled back into the forward supply chain. As we progress into the future and start thinking about these things more — and we’re required to by the markets, by regulations (potentially), and by what constitutes good business — we will increasingly move towards adjusting our supply chains to be more circular. That is, looking at the full lifecycle of the product.
That begins with how you design it. That’s going to be a fundamental change in the way we think about all supply chains.
Advice on supply chain transformation for business leaders
Michael Krigsman: As we finish up, Tony, can you offer advice to business leaders and finance teams who are listening to this who say, “Yes, we want to change, transform our supply chain, but where do we even begin? It’s such a daunting challenge.”
Tony Nash: I would say, really start with the easy stuff. Get some successes. Do a pilot. Then you can accelerate it very quickly.
Data scales very quickly. Technology scales very quickly. But your team may be uncomfortable with digital transformation, especially around supply chains. Help them see some quick wins and then push forward as quickly as possible after that.
Michael Krigsman: Jon, you discussed earlier the cultural dimensions of supply chain transformation. It’s really important, so just share some further thoughts on that and advice that you have for folks who are listening.
Jon Chorley: I think any change is at least as much cultural as it is technological, and the people who implement those changes are key to its success. I think part of what’s needed is a willingness to understand that the way you did things in the past may not be the way you need to do things in the future.
Quite often companies, for example, feel that they have a certain special way of doing a process that’s absolutely required, and they hold onto that even though there is really no business differentiation for them to do it that way. They’ll invest a lot of time and energy to duplicate that on a new platform.
We always encourage people to step back a little bit and leave behind some of those preconceptions. Not everything is your secret sauce. Your secret sauce is a little bit on the top. It’s not stuff on the bottom.
Leave behind those preconceptions. I think that’s probably the single biggest cultural shift.
Then the other point we mentioned earlier is board support. I think that’s top-down. Having that support from the upper levels of the business is critical to any large-scale transformation.
I think the great thing, if there is a great thing from 2020, is that boards are aware now of the criticality of supply chains in their business and are probably more open to those kinds of conversations. Those difficult conversations from supply chain professionals with their board. Now is the time. The folks that make the investments now are the folks who are going to benefit from the uptick that we all hope is coming.
Michael Krigsman: Jon Chorley and Tony Nash, thank you both for sharing your expertise with us today.
Jon Chorley: All right.
Tony Nash: Thanks, Michael.
Jon Chorley: Thank you so much. Great talking with you all.
Here’s a mathematical problem: The sum of all the individual country GDPs never equals the global GDP. That means forecasting models are flawed from the start, and it’s impacting global supply chain economics in a big way. Entrepreneur Tony Nash found that unacceptable, so he built an AI platform to help businesses “understand the sum of everything” through a highly automated, globally data-intensive solution with zero human bias.
Complete Intelligence, Nash’s Houston-based startup, uses global market data and artificial intelligence to help organizations to visualize financial data, make predictions, adjust plans in the context of a global economy, all on the fly. The globally-integrated, cloud-based AI platform helps purchasing, supply chain planning, and revenue teams make smarter cost and revenue decisions. It’s a way on how to make better business decisions.
“The machines are learning, and many times that has meant deviating from traditionally held consensus beliefs and causality models,” said Nash. “Causal beliefs don’t hold up most of the time—it’s human bias that is holding them up—our AI data is reducing errors and getting closer to the truth, closer to the promise of superforecasting.”
Massive datasets across 1,400 industry sectors
More than 15 billion data points run through the Complete Intelligence platform daily, making hundreds of millions of calculations. Average business forecasting saas software models use 10-12 sector variables. Complete Intelligence, on the other hand, examines variables across 1,400 industry sectors. The robustness gives businesses insights and control they didn’t have before.
“We’ve seen a big shift in how category managers and planning managers are looking at their supply chains,” said Nash. “Companies are taking a closer look at the concentration of supply chains by every variable. Our platform helps companies easily visualize the outlook for their supply chain costs, and helps them pivot quickly.”
Superforecasting brings a modern mindset to an old industry
Australia-based OZ Minerals, a publicly-traded company, is a modern mining company focused on copper with mines in Australia and Brazil. OZ says their modern mantra is more than technology, it’s also a mindset: test, learn, innovate. They wanted to better navigate and understand the multi-faceted copper market, where the connectivity between miner, smelter, product maker, and consumer is incredibly complex and dynamic. They turned to Complete Intelligence.
“I need a firm understanding of both fiscal and monetary policies and foreign exchange rates to understand how commodity prices might react in the future because a depreciating and/or appreciating currency can impact the trade flows, and often very quickly, which might influence decisions we make,” said Luke McFadyen, Manager of Strategy and Economics at OZ Minerals.
“Our copper concentrate produced in Australia and Brazil may end up being refined locally or overseas. And then it is turned into a metal, which then may be turned into a wire or rod, and then used in an electric vehicle sold in New York, an air conditioner sold in Johannesburg, or used in the motor of a wind turbine in Denmark,” he explains. “The copper market is an incredibly complex system.”
With Complete Intelligence, McFadyen has a new opportunity to test for a bigger-picture understanding and responsiveness. Previously, he updated his models every few months. Now he could do it every 47 minutes if he needed to.
McFadyen points to the impact of COVID-19 as a “Black Swan” event that no business forecasting saas software could have predicted, but is nonetheless impacting currencies, foreign exchanges, and cost curves throughout global copper market and supply chains.
“If your model isn’t dynamic and responsive in events like we are experiencing today, then it is not insightful. If it’s not insightful, it’s not influencing and informing decisions,” he said. “Complete Intelligence provides a different insight compared to how the traditional price and foreign exchange models work.”
McFadyen says early results have reflected reductions in error rates and improved responsiveness.
Cloud power and partnership
Complete Intelligence needed a strong technology partner but also one with global expertise in enterprise sales and marketing that could help boost their business. They found it with Oracle for Startups.
“We have lots of concurrent and parallel processes with very large data volumes,” said Nash. “We are checking historical data against thousands of variables, anomaly detections, massive calculations processing, and storage. And it’s all optimized with Oracle Cloud.”
Nash, who migrated off Google Cloud, says Oracle Cloud gives him the confidence that his solution can handle these workloads and data sets without downtime or performance lapses. The partnership also gives him a credible technology that is native to many clients.
“As we have potential clients that come to us that are using Oracle, having our software on Oracle Cloud infrastructure will make it easier for us to deploy and scale. A seamless client experience is a critical success factor for us.”
Nash says the Oracle startup program‘s free cloud credits and 70% discount has allowed them to save costs while increasing value to customers. He also takes advantage of the program’s resources including introductions to customers and marketing and PR support.
“We’ve been impressed by the resources and dedication of Oracle for Startups team,” he said. “I’d recommend it, especially for AI and data startups ready for global scale.”
Beyond mining: superforecasting futures with AI
Beyond mining, Complete Intelligence is working with customers in oil and gas, chemicals, electronics, food and beverages, and industrial manufacturing. From packaging to polymers and sugar to sensors, these customers use Complete Intelligence for cost and revenue planning, purchasing and supply chain proactive planning, risk management, and auditing teams, as well as general market and economic forecasts.
The error rates for Complete Intelligence forecasts in energy and industrial metals performed 9.4% better than consensus forecasts over the same period, and Complete Intelligence continues to add methods to better account for market shocks and volatility.
OZ Minerals’ McFadyen said, “This is the next step in how economists can work in the future with change leading towards better forecasts, which will inform better decisions.”
Nash and Complete Intelligence are betting on it – and building for the future.
Complete Intelligence is in partnership with Oracle for Startups, and here’s a Youtube interview featuring our CEO and founder, Tony Nash, where he explained what the company does and for whom. Get to know the technology behind the superforecasting for manufacturing firms and learn how CI helps them be more profitable specially in a highly volatile market like in the Covid pandemic. There’s also a section on how CI uses the Oracle Cloud Infrastructure to better serve its clients around the world.
WD: Can you tell me a little bit about what Complete Intelligence does and for who?
TN: We work with global manufacturers and we help them better understand their cost and revenue environment. We’ll work directly with their ERP data. Work with IT in the cloud and help them understand the forecast for their costs and for their revenues. So, they’re using their exact data in their exact environment to make great decisions for their clients.
WD: I’ve heard what you do referred to as super forecasting, which sounds so cool. Which industries
are best served by the super forecasting that Complete Intelligence offers?
TN: It’s mostly manufacturers. We work with chemicals firms, mining firms, electronics manufacturers, industrial manufacturers. So people who make stuff or people who work with firms who make stuff have to know how much that stuff’s gonna sell for, how much it’s gonna cost. Anybody who has risk associated with the future cost or future price, would need what we do to really help them de-risk their future decisions and their proactive planning processes.
WD: How are the forecasts that you provide impacted by volatility caused by unprecedented global events, say a pandemic?
TN: When Covid came around, when markets were hit dramatically in February, March and into April, we increased the frequency with which we update our forecast to our clients. But we also folded in a lot more volatility-specific algorithms, so that clients would understand what the path back would be like. In a normal year, let’s say the cost forecasts for a major manufacturing firm can be off by up to 30 percent. In some cases even more. So, if you’re planning those expenses and those budgets. You have a huge variance that you’ve got to pad in your budgets.
On average, we’re looking at a four to seven percent error rate. We’re helping people in a dramatic way to really de-risk their future outlook on the cost side. What we’re doing is a fully automated process. That guesswork of people sitting around the table saying, “let’s push this number up, let’s take this number down,” that’s a long budgeting process for people. And we really put that in the cloud. We have the machines learn and work through the data and calibrate and reduce that error for clients.
WD: Working with global markets and currencies, you must have massive data sets. Increasing the frequency of running those data sets probably requires quite a bit of computational power. How does Complete Intelligence manage that?
TN: Wee do that with cloud solutions. We work with OCI and the current generation of OCI to expand our computing capability. Many companies work across clouds. They work across on-perm and cloud and so we’re flexible with all of that. The frequency of those updates, the frequency with which clients want an updated view of the future for different companies changes. You have really fast moving companies who want that on a really high frequency basis. You have slower moving companies who are looking at it maybe monthly. That’s fine. We adjust to all of them.
WD: So, flexibility and multi-cloud are two really interesting considerations for dealing with enterprise customers like you do. What are some of the other unique challenges that face startups, like yours right now?
TN: With the pandemic, we’ve seen clients be very, very risk-averse. The the risk of taking on a new small company as a vendor is a problem for major companies. They’re trying to figure out how to adjust their business to an uncertain environment. For us, partnering with Oracle has helped to de-risk that decision for major companies. Oracle says Complete Intelligence has a viable solution, let’s talk about how we can help you. And the credibility that Oracle has when we go into a client is really really important for that situation.
WD: Aligning with a credible brand that’s been around for 40 years like Oracle is absolutely something that a startup can use to hack their growth. I’m curious about your use of Oracle Cloud and solutions that are open source Cloud native like Kubernetes. Can you talk a little bit about how you work with those Cloud Native Solutions?
TN: Kubernetes is a great one where our solution is containerized. We throw it onto Oracle Cloud and we can use it with clients. So, whether it’s the database we use, whether it’s the scheduling languages we use, whether it’s containerization, all of that is flexible on Oracle Cloud. And we can use the open source infrastructure that we have within our specific configuration on Oracle Cloud.
Over the last year, OCI has changed a lot in terms of enabling some of the very specific solutions that we’ve had. And very kind of high performance computing solutions that we’ve needed. Accommodation has really given us a lot of confidence with OCI.
WD: Your startup has had a pretty unique trajectory. You started the company in Asia and now you’re based in Houston, Texas. What inspired such a significant change?
TN: I guess the biggest thought behind there, is this is where the customers are. And to be honest this is where the talent is. The people who are doing the leading edge work in what we’re focused on are here. And the context around manufacturing and the need to automate some of the decisions around manufacturing really are happening in the U.S. and Europe, in a big way.
Of course that’s happening in Asia but it’s different in Asia. I spent 15 years in Asia. We conceived of and started Complete Intelligence there but we really utilized as much as we could there. And I came to a point where we just had to move the company to the U.S. to find the resources we need to build the company.
It’s been great moving to Texas, has been great. It’s a fantastic business environment. The manufacturing clients here are fantastic. Oil and gas is seeing a lot of headwinds right now which is a real opportunity for us.
WD: So the forecast is looking bright for Complete Intelligence?
TN: Oh absolutely. Again, with the right partners, we can move into the right clients and any startup trying to go it alone today is going to have a really hard time. It’s possible and it’s probable with the right amount of work put in, but building the right partnerships like our partnership with Oracle has been huge in helping us to accelerate our commercialization and our presence in the market.
WD: Absolutely and I know that if startups want to learn more about working with Oracle they can go to oracle.com/startup. If they want to learn more about the exciting work that Complete Intelligence is doing, where should they go?
TN: They can go to completeintel.com. We’ve got all of the resources there. We have a weekly newsletter. We have regular video interviews with industry experts, similar to what you’re doing. There are a lot of resources. Our twitter feed is complete_intel as well, there’s a lot there.
WD: Great, any secret market intelligence you want to share with our viewers?
TN: The changes we’ve seen over 2020 and the risk and volatility we’ve seen over 2020, unfortunately we don’t see a return to normal soon. The challenges that we’ve faced as startups and the challenges that our customers have faced in 2020 aren’t necessarily going away. This type of up and down environments and the persistence that we’ve had to have as startups, 2021 is not going to bring a normal back. We’ll see a little bit more, but as startups we’re going to have to continue to push very, very hard to get the mindshare within those endpoints.
Tony Nash joins Jamie Robertson at the BBC Business Matters podcast and they discussed mostly the US Election as the electoral college confirms Biden win. Or is it too early to tell? They also talked about the recent Google meltdown and why it shouldn’t be a surprise. Also, do Chinatowns around the world suffer because of the anti-China movement? What about the vaccine — will it help us get back to normal at last? And, what’s wrong with California and why do businesses and people move out of the state and to Texas?
Joe Biden has been formally certified as the next president of the United States, with results from electoral colleges in all but one US state giving him 302 votes. This takes him over the 270 threshold required to win the presidency. The electors in each state are appointed to reflect the popular vote, which was won by Mr Biden in November. We get reaction from Washington DC and examine the US democratic process.
Two major cyber-incidents on Monday. The first you may well have noticed, the second will have almost certainly passed you by but may be in the long term far more significant. Google applications including YouTube, Gmail and Docs suffered a massive service outage, with users unable to access many of the company’s services. The second was a sweeping hacking campaign that may have attacked the US Department of Homeland Security, the Treasury and Commerce departments and thousands of businesses. We take a look at how working from home may be leaving businesses and government more vulnerable.
And we’re in the Philippines where the country has been showing an interest in the very English game of cricket. Throughout the programme, Jamie Robertson is joined by analyst Tony Nash in the United States and social welfare expert Rachel Cartland in Hong Kong.
JR: Joe Biden has been formally certified as the next president of the United States, but results from Electoral College is in all but one US state, giving him 302 votes. That takes him over the crucial 270 threshold, which is required to win the presidency. The electors in each state are appointed to reflect the popular vote which has won by Mr. Biden in November. OK, so, Tony, it’s all over. Pretty much, you reckon?
TN: I don’t know. I sat with you guys the day after the election and I said, everyone is pretty convinced that it was the end of the line then. And I said at that point, it’d be weeks, if not months before this was settled. And we still have a lot happening here. So honestly, I have no idea what’s going to happen on January 6th, but it’ll be interesting.
Look, Donald Trump is always interesting. You can never accuse the guy of being boring. So I wouldn’t be surprised. Actually, your commentator was not right about the president of the Senate, which is Mike Pence, actually has to accept the electors.
JR: It’s not just a question of counting. It’s a question of accepting that count as a right.
TN: So the speaker of the House and the president, the Senate have to accept the electors. So if they don’t, then it goes to the House. But there’s one vote per state and there are more Republican delegate delegations to the House of Representatives than there are Democrat delegations. If you look purely a state, no. So I actually have no idea how it’s going to end. I have no idea what’s happening on January 6th. But it’s not as simple as this was done because there are, I think, six or seven states where the Republican electors actually protested and actually sent their electors as well. So you have states divided and protesting, it can be problematic, so I have no idea what’s going to happen.
JR: I certainly haven’t heard the fat lady sing in us. Tony who feels like we might be losing a battle somewhere here, or do you think it’s not as easy to say as that?
TN: First of all, there is more hacking and there’s more information lost than most people are aware of. This is terrible, but I don’t think we should be naive and believe that this is rare.
JR: Why would it become public, though? It’s quite interesting that I mean, why tell people this has happened?
TN: Because so many companies have been exposed. It’s 18,000, I think. But I think publicly traded companies never disclose that happens to them, even though they’re obligated to disclose, they don’t govern institutions. You know, it happens all the time. And it’s a daily occurrence.
My company is on Google infrastructure for part of our work. And what worries me is we’ll never know what information was exposed, especially with the Google hack. And I think there should be a requirement that companies let people know what has been exposed, but we’ll never know and we’ll never know what was lost and what was exposed. Companies have their their corporate secrets, their trade secrets on their cloud drives. And we never really know if things were exposed. And we never you know.
JR: Tony, there’s a Chinatown in Houston. And there’s research to back this up. Chinatowns have suffered worse than many other communities in the U.S. Is it connected to an anti China feeling, do you think? Or is there something else going on here that I haven’t seen?
TN: I don’t think there is. I lived in Asia for 15 years and my entire social community for much of that time was Chinese. I understand it at a different level. I do think there is a sensitivity among the Chinese community that they may be targeted for this. So I don’t necessarily believe that is the case. At least it’s not in Houston. Maybe it is in New York. But in Houston, I don’t feel that’s the case at all.
JR: I just want to continue our conversation she had earlier, which is about China or at least Chinese Institute of Chinese Chinatowns in the United States, which may have been losing more business than most other communities, possibly because they’re Chinese. And Tony gave a very nuanced view saying, but perhaps there’s a perception this is happening, but it may not be actually true in reality. Right, Tony? A vaccine, is it a shot in the arm for the economy as well? I mean, my impression I got from that interview with Constance Hunter from KPMG was that he thinks it’s going to take a bit of time, but it may be just the light at the end of a tunnel, which everybody needs.
TN: Just put the vaccine into context. So fatality rates for Covid, at least in Texas, are a third today of what they were just in September. So it’s more the government halting business that.
JR: Can I just challenge you on that? You say it’s a third time. It is a third of the total. But on the other hand, you’re having many more many more cases identified than you were in September. I mean, that may be in absolute terms. I’m not quite sure whether if you’re getting more cases actually reported, if it’s a third of the ones being being reported.
TN: More deaths in August.
TN: August 5th and 6th, we had 229 deaths in Texas now were around 139, 146, 121, 128, OK. So right now, the thing that we need to caution both on absolute and percentage numbers, we’re a third as a percent. We’re a third better. We’re a third of what they were in September even.
It’s great that we have a vaccine. I’m not trying to pull back on that.
But the problem here is that the government pulled the plug on people’s livelihoods. Hundreds of thousands of companies in America are out of business because state and local governments shut economies. That is a fact.
JR: You don’t think people would have been frightened to go out, frightened to go to restaurants, frightened to go to cinemas?
TN: I believe that people are responsible and they would have washed their hands and done all this stuff, worn masks, all that stuff.
Government officials killed local economies. What’s happening right now is federal government officials have not put the stimulus out that they should have put out in August. The package that came out in May was only supposed to last three months. They were supposed to put another package out in August, September. They didn’t. So we’re in on one hand, it’s state and local governments that killed economies and killed hundreds of thousands of companies and millions of jobs. On the other hand, it’s the federal officials who wanted to negotiate small details while people starve. It is government on both ends of this that are harming individuals, killing companies, harming families. It’s terrible.
JR: What is wrong with California? What’s so great about Texas?
TN: I have an artificial intelligence company in Texas. So we are in a technology ecosystem here in Texas. And Oracle, as you noted, just announced on Friday, they’re coming. Tesla’s moved.
I lived in California during the first Internet bubble of the late 90s, early 2000s. And what I find about California now is especially around the talent. They’re good. There’s nothing wrong with the talent there. It’s good. But it’s not the world class that it once was. It’s really expensive and it’s very arrogant. Silicon Valley is, kind of, to use an Asian analogy, it’s the Singapore of the U.S. It’s entitled, expensive, and kind of OK, good, but not great.
I’m in Texas and I moved my company from Singapore to Texas because I can find a very experienced technical person who will roll up their sleeves and actually do hard work. People are pretty affordable. The quality of skills here are great and it’s a fantastic business environment.
JR: This may sound a little bit like an Englishman talking, but it’s not to do with the weather, is it? And there is more to that question to mention immediate, bearing in mind for the rather high temperatures and wildfires.
TN: California’s got the Pacific Ocean and beautiful weather. People are coming here initially because they have to. But then they want to because they realized there are a lot of really nice people here. And I loved it. I lived in California for a long time and I voluntarily moved to Texas because of the business environment. I’ve been here four years now.
JR: And Tony, I mean, cricket may be played in far flung things, but in Texas, I don’t think so.
TN: You would be surprised, Jamie, 20 miles from my house is the largest cricket complex in America. That’s amazing. So Texas has a very large Indian community and we have the largest cricket complex in America.
In just 2 minutes, you’ll learn why superforecasting is so much better than forecasting. Hear how automated, data-intensive AI with no human bias can help make predictions and adjust strategy on the fly, and how startup Complete Intelligence is making it happen.
Is forecasting enough when you need to analyze and take action? Meet the startup that says “no.” What’s needed is superforecasting.
Hi, it’s Mike Stiles, and this is Meet the Startups for the week of August 26th, brought to you by Oracle for Startups.
How can you be happy with forecasting when there’s something out there called superforecasting?
Startup founder Tony Nash and his company, Complete Intelligence are making super forecasting possible with a highly automated, data intensive A.I. solution.
Part of what makes it so SUPER is there’s zero human bias. No spin or wishful thinking allowed.
Complete intelligence is helping organizations visualize financial data, make predictions and adjust strategy on the fly. That gets you things like smarter purchasing, better supply chain planning, smarter cost and revenue decisions.
To get where they needed to be on performance and price, the company moved from Google Cloud to Oracle Cloud. That did it. Computing is at peak performance and Complete Intelligence’s global customers are reaping the benefits. That’s super.
We asked Complete IntelligenceCEO Tony Nash what this pandemic has done to forecasting and supply chains.
”We’ve seen a big shift in how managers are looking at their supply chains. As a result of Covid-19, companies are eager to understand their cost and revenue risks, things like concentration risk and the timing of their cost, that sort of thing. We’re helping our customers with timely and accurate information to make smarter cost and better revenue planning decisions.“
What startup doesn’t like better performance and lower costs? Oracle has a startup partnership for you at Oracle.com/startup.