There’s a growing interest in Predictive Analytics in the Enterprise space so I reached out to an innovator in this field.
In this episode of the “Experts and Influencers”, I speak to Gauthier Vasseur, Vice President of Trufa (a cloud application company) and Teacher at Stanford University about Predictive Analytics.
Specifically, what is predictive analytics and how does it play a role in improving a company’s bottom line.
Gauthier not only explains the concept but shares a lot of information on where you can learn more on this emerging topic.
I certainly learned a lot in the short time that we spoke and I hope you enjoy this conversation. Here’s my discussion with Gauthier.
What Drives Your Performance: https://trufa.wistia.com/medias/orb08dqg3b
Stats and Entropy: http://www.trufa.net/expertise/the-science-inside/
Guenther’s Blog: http://www.trufa.net/author/timklotg/
Useful website: http://fivethirtyeight.com/
Connect with Gauthier
|Hau:||Gauthier, welcome to the podcast. Can you please tell us about yourself?|
|Gauthier:||Yes, absolutely. Good morning and afternoon everyone. For the topic of today, if I were to describe myself, I would probably give you three words, data sharing, surfing and astronomy.|
|Hau:||Surfing and astronomy.|
|Gauthier:||Yes, which I put together concatenated. Data first, I think that’s probably what brought me here today is the passion I have for data. It’s really not a passion that was born with this whole craze about big data, it was before. I think days from my younger stay with my dad who was this statistician and engineer and who rapidly showed to me how data was helping him in his daily job. How simulation and statistics made from data would dramatically enhance the way he was making decisions.
Ever since, I’ve always looked for data in my jobs, whether it was finance, treasury or audit. I rapidly found out that when you master data, when you know how to process it properly and efficiently, you gain a lot of time. You actually get on top of things faster. You get more comfortable, which gives you the ability to do more and progress in your life and in your career.
|Data is definitely a part that defines my professional career. The second word was sharing and that’s something also that came pretty quickly, as I started to recruit people back in my finance days and more recently in my software days. I realized that very few students interns or young professional knew about data.
The last thing I wanted is to see them wasting their timing on useless tasks, manual tasks. I really thought that this has to be changed. I started to teach, I started to write articles, I started to share about all the things I had learned on data to make sure my employees, my interns, my students would be able to take it from there and then spread the gospel in turn.
|You might wonder at this stage why surfing and astronomy come into play. I think they are a big drive for me because I love my job. I’ve always liked my job. I’ve been pretty lucky about that but still I want to find time to do my other passions. I get my butt kicked in the waves and that takes four hours, five hours out of my weekend. If I want to go out there in the mountains doing some astro-photography, that’s going to take a whole night. Processing the pictures is going to take probably another six to eight hours.|
|Where do I find the time? The only way I can find that time is to be faster, more efficient, more in control of what I’m doing during the day and mastering data, learning from my students and teaching back et cetera, really enable me to save that time, to be able wrap up my days faster, hence these two passions that are really for me drivers to keep on learning about data and in return sharing more about data.|
|Hau:||Got you. You actually hit on a lot of good points. I still have to go back to that surfing and the astronomy because I have a model of the silver surfer on my desk right now. If you read comic books.|
|Gauthier:||I love comic books.|
|Hau:||[inaudible 00:04:19] throughout the cosmos. What you brought up is really interesting, you are not only teaching and working in the data space but you are also applying that to your personal life when you mentioned that to make room or make space for your other activities and interests, you have to find ways to work faster or get the work done in a more timely fashion, right?|
|Gauthier:||Absolutely. I would add one element to that is, it was not on purpose but maybe that’s the way I’m wired. Whether it’s data, surfing, astronomy, astro-photography.
All these disciplines are journey, you never see the end of them, there is always something you can learn, you can do better. There is always a new technique that you are looking forward to mastering. I think that’s all the people I’ve seen around me being successful at data or astro-photography, all know they are on the journey and they never stopped learning.
That’s why I was very happy to participate to this webcast because I think it’s part of this continuous learning we all have to do on this stuff to doing more or getting better with data.
|Hau:||Thank you for making your time. Speaking of learning. I know a lot of folks in SAP SPACE, they saw a customer looking backwards in terms of what data and what things happened. It takes them a while to process the information, package it so they can make sense out of it.
When you talk about learning. There is a new, I would say, how do I put it? There is a new interest in predicting future actions based on past events. That’s where you talk a lot about predictive analytics.
For the folks who’ve been in this space similar to me fifteen years or more, where you are still used to the old ways of doing things, the old way of packaging data. How does predictive analytics fit into this journey of moving forward to the future with new tools and memory computing, how do we adapt to this new way of consuming and packaging data?
|Gauthier:||Actually, talking about the journey, that’s another page you described right there that is being open. Does this mean that predictive analytics is brand new? Absolutely not, it’s been around for centuries, except that tools and processing means were much weaker. Right now they’ve become accessible to business teams.
Finally, we can achieve what science used to do for research. We can achieve that for domains such as accounts receivable, for domains such as stock analysis. If we go back a little bit, predictive analytics always sounds like a big buzz word. Overall it doesn’t have to be that complex.
|I think you hit it pretty well is, it’s basically taking past and present element pieces of insights and being able to derive prediction and you can leave it at that.
The thing is, these predictions, the way they bring value is, as you consistently create this prediction, you’ll find out that they will beat your hunches. They will be better than the crystal ball. You’ll find out that even if they don’t have to be perfectly right, they will always be slightly better than anything you have.
Predictive analytics for me is both math and statistics and it’s also an approach to solving problems that if applied consistently will end up being positive overall.
|Something that, I usually explain it this way to my student using actually an example in a great book, The Signal and the Noise by Nick Silver. Which really shows that predictive analytics are a game changer. They can be a matter of life and death actually in the example I’m going to mention.
At the same time they are not magical, it’s not looking in the future, it’s taking the best we have, and the best math and statistics to predict something.
The example that Nick Silver brings up is predicting where a hurricane is going to hit. We know there are a lot of hurricanes in the Mexican gulf hitting Texas, Florida. Twenty years ago, the prediction level was pretty much a 350 miles radius, which means, whenever a hurricane was coming up the coast, the local authorities had a 350, miles radius margin of area to evacuate.
|At the end of the day if you take your favorite map and you look at how much that radius is. It will pretty much cover the entire coast from Texas all the way to Florida.
Which means any time a hurricane would hit, you’d say, “Evacuate all these coastlines,” which are millions and millions of people. With a better predictive tool you can say, you can predict accuracy at a hundred mile radius, which still sucks a little a hundred miles, come on.
Guess what? A hundred miles radius narrows down the number of people, the zone to evacuate to a big metropolitan area. We are not talking about entire state coastlines. We are talking about large cities. That completely changes the game in how to evacuate people and work two focused resources in order to prevent big disasters.
|That’s the lesson we learn from weather forecasting and all the sciences. Predictive analysis is giving organization the ability not to have a crystal ball but to make decisions always be better than pure random or pure get feeling and that’s what pays off in the long run.|
|Hau:||It sounds like you hit on two really important points with your last answer. For folks like myself who are not as familiar with predictive. The buzz term predictive analytics. The connotation at least in me is a black box that triggers scenes from minority report, where there are trying to see into the future.
The way you described it as math and statistics is less scary, is more concrete, is more grounded in science. The takeaway that I’ve had from you is, even though it’s not a perfect guess or estimation of the future, you are really connecting the dots to making more accurate, what’s a better word for prediction?
|Hau:||More robust plan for action in case events like the hurricane happens, right?|
|Hau:||Taking that into the business realm, what are the common business challenges or business questions that a tool like a predictive tool can help answer? Because a lot of the clients I work with, they have tons and tons of data. We are talking about twenty five, fifty million data record points.
How does a predictive tool help them to see one month, three months into the future for mundane tasks such as demand planning or forecasting?
|Gauthier:||Before hitting that very point, one thing that needs to happen from zero to getting this prediction. There are a few things that need to take place and they should not be underestimated.
To build these predictive models, companies are going to need tremendous amounts of data and not the aggregating kind. They need it at the lowest granular level, with every single little detail. That’s what statistical model we’ll use to derive this prediction.
You mentioned earlier the notion of black box. I think for all of us, the different steps are quite simple but there is indeed within this other creation of this prediction. There is a bit of heavy duty science that is brought in. That’s why we all dream of doing predictive analytics.
Unless we are PhD mathematicians, we might find ourselves a bit short. That’s why pieces of software, that’s why software is there is willing to help us. That’s why applications are designed.
|If you look at, you have an SAP, ERP, how far are you from getting predictive analytics? You are going to have to go through a series of simple steps that you may or may not be able to do.
The first one is gather the data. You need to secure that data because you need a sample to generate your predictions from.
Once you have your data you need to be able to articulate this information in meaningful business format because stat is stat but data is data. If you don’t know what the data means, statistics won’t know much more than you do.
You need to get the business being the business orientation to that data and then apply the statistic correctly on the data.
|It means you need to set up your statistics so that they comprehend what it’s all about. This is where the black box really comes in. There is a very specific.
We talked about the data but business data has some very strange specificities and you need to use robust statistics and this is a great pickup line if you want to shine at business dinners. The concept of maximum entropy. These are concepts you need to apply to your predictive models so that outliers in your business data don’t influence too much so that the results of the prediction can really be reliable.
That is the layer of black-boxing you absolutely need to have in statistics. At the end of the day, we all agree, this is terrific to predict demand, to predict machine breaks down, inventory level, but you still need the people to be able to understand that data and interact with it. There is a need for strong UI, strong graphical capabilities to make all that work.
|Hau:||I know, one of the strengths with SAP, at least on the, business warehouse side of things is that it comes I would say prepackaged with generic solutions that they can get or at least use or get up and running fairly quickly.
Does a predictive tool like Trufa bridge that gap for business users who don’t have a PhD in statistics to at least apply some rudimentary models against that data?
|Gauthier:||Yes, and I think more than bridges the gap. It addresses the problem and creates the solution. Basically the like that SAP ERP companies have is that their ERP has captured every single bit of data at the transactional level, which means you have the most complete transactional readers that you could have.
Once you have that, then we can go back to simple concepts. All it takes is pick that data up, bring it to a place where it’s going to be organized, where every data is going to be interpreted the way it should be. Every piece of information is going to be put part of a process of the transactional process.
Then you let the calculation power, the math power, the statistical power get started and you display all this in a UI, a user experience that guides any one of us, business people through different steps where you are pointed to opportunities, you are guided through simulation and then you can really predict what your actions will create in terms of economic impact.
|Actually and that goes into your earlier question about the biggest challenges. The biggest challenges we saw. I think predictive analysis is already a big challenge. The reason why I joined this company is we are actually pushing it one notch further to a concept that we call the efficient business frontier.
If you recall decades ago, the concept of the efficient frontier was created in the world of stock management, investment management, where for a given risk you would assess how much yield you could have. The frontier would be actually the perfect curve where you have the maximum yield for the minimum risk.
|If you’ll be under the curve it would mean, you have a risk for given risk, you don’t yield as much as you could. That line would be the efficient frontier. That line exists in business, it’s called the efficient business frontier.
What it shows you is that you can strike balance between all the drivers that define performance. To give you an example, a very classic one. Actually I’ve run into that very case when I was in treasury and I could never solve it.
You want to reduce working capital. You are going to see what impacts working capital. You are going to look at rebates, you are going to look at yelling at your customers, yelling at your vendors, you might change your operational processes. You would see result in working capital.
|Now you flip the page and you look at profitability and you realize that everything you did, just hindered, just hurt your profitability.
Rebates have taken down revenue. Pushing customers have done great in quality of service and they are not renewing their commitments. Pressing vendors has degraded the quality of the pieces you are being served.
At the end of the day you push on one side of the scale and the other side went the other way. By identifying and measuring the impact of these drivers when they move, where there is some profitability or working capital. By doing it on both sides, you can define that efficient business frontier. I think that is the ultimate challenge in predictive analytics.
|Hau:||I think in about twenty minutes you just got me really excited about what predictive is. Open the can a little bit to see what’s actually happening behind the scenes?
All the things that you pointed to, it sounds, like you said not a magic bullet but it does give a forward perspective on what you can do without spending too much time building and testing models to see if it works.
I think for those of us who now have a renewed interest in finding more about predictive analytics. Where can we find more information or innovations with that you are doing with predictive and where can we learn more about what the tool has to offer for business users who are just trying to maximize working capital, either in the treasury or at least in the space I’m interested in, logistics and supply chain?
|Gauthier:||I could give you a few pointers, on the fun side, there is a great website called fivethirtyeight.com. This website is a treasure of daily life examples of predictive analytics, whether it’s for election, whether it’s for NBA games, it’s full of easy to read, easy to grasp predictive analytics. fivethirtyeight.com is a great resource.
The book I mentioned earlier The Signal and the Noise by Nick Silver is also a very easy read, perfect for the beach and gives you tons of great stories that will make you look good at dinners.
On a more serious standpoint, talking about what I do, definitely the trufa.net website, www.trufa.net website also contains many examples on how predictive analytics and the concept of efficient business frontier applies to organizations.
There are actually a couple of pages I love a lot. One is Guenther’s blog.
|Guenther Tolkmit is our chief delivery officer. He is the developer behind the machine. He has a great way of explaining how this changes the world. I love his tone. Very European German tone, ready to ruffle feathers and I think that’s the way things should be in that space.
If you have a little bit more courage but still very easy read, two great papers from our chief scientist, Professor Andreas Mielke about maximum entropy and the concept of robust statistics.
Just the first page will really illuminate you on how simple the base concepts of predictive analytics can be. Then on the second page how heavy duty mathematics it’s all about.
That also tells you that, these things are great but let’s not forget, this is serious math. If it’s not as part of the integrated application, get ready for a lot of fun with some PhDs.
|The last thing I would definitely offer and this is really sincere and sometimes it surprises my students. I’m always up for a coffee, I’m always up for a call and say, “How come you have time?” I want to have that time. The last resource is just through you or directly, just connect with me. Contact me and let’s have a chat. I am passionate about what I study, I’m passionate about the domain and I’m also passionate about learning how other people address these issues. For me, it’s part of this journey I described at the very beginning of this interview. It’s a never ending journey of learning, sharing and I’ll be always happy to discover new opinions and new domains on calls like this.|
|Hau:||Gauthier, I want to say thank you for freely sharing all your knowledge. I know those resources will be added to the show notes. Personally I would take up you offer on the coffee just because I grew up in the Bay Area and I visit my parents at least once a year to say hi.|
|Gauthier:||That’d be okay.|
|Hau:||I might be stopping by. With that, I want to say thank you and we’ll talk to you in a future podcast hopefully.|
|Gauthier:||That would be with pleasure. Thank you for having me.|
|Hau:||Thank you again.|