Russ: Hi I’m Russ Capper and this is The EnergyMakers Show, coming to you right now from CERAWeek where I’m very pleased to have as my guest, Babur Ozden, the Founder and CEO of MAANA. Babur, welcome to The EnergyMakers Show.
Babur: Hi Russ. Good seeing you.
Russ: You bet. Tell us about MAANA.
Babur: MAANA is a new software platform that’s used for operational industrial analytics, and it is used by some of the world’s largest oil and gas companies. It is a byproduct of a new competitional invention we at MAANA came up with and patented, built a patent portfolio around it. This enables our customers to empower their subject matter experts on operational decision workflows to make decisions with all the available data and not just some of the data.
Russ: Ok. You say new, but I know it’s kind of been a rapid upstart here because you’ve already got paying customers, you’ve already got significant investors from the industry, and it sort of feels like you’re off to the races, Do I have that right?
Babur: That’s correct, that’s correct. We still want to think that we are a young company. We’re four and a half years old, and we started selling our product in the second half of 2015, and our last year was our first year of revenue, so our business grew seven times from 2015 to 2017. This year we would be definitely at the races. And this year we started taking major competition down, so we are becoming very visible in oil and gas, and logistics, and industrial manufacturing space.
Russ: Ok, well I know enough about it to know that it’s difficult decision making processes that you go through, using multiple sources of data, but share your description of that, exactly how it works and maybe use an example.
Babur: Yeah, I’ll give you an example. So, you are, let’s say you are a manufacturing company and you have a parts business, where most manufacturing companies make their living on selling parts on main assets that last 20-30 years. So, in a normal year, you may be wasting 100-200 million dollars by sending parts that are not needed in the field. So, the process happens like this: a field technician gets an electronic ticket from some customer service center, and looking at the ticket, whether it’s a first-time installation, regular maintenance, or a repair of an equipment in the field. And the filed technician uses some system, some judgement, and picks parts and ships it. So, how can you influence the decision point at that moment so the field technician does not select parts that are not needed, or selects the parts that are only needed, so that’s how you influence that person. Now, in order to make that person the right decision, you need to be able to churn data in your maintenance records, in your inspection records, in the history of similar incidence records, in your invoicing records, etc. None of that data is available at the decision point. So, if there was a way, now with MAANA, there is, if you could think of, hypothetically, all of the available data in a corporation became to the aid of the individual at that moment, can you change that? So, this is educational.
Russ: So, you make a better decision (Babur: And faster.), and you erase all the bad decisions. Even with a skilled person selecting that new this other data was there, they would have to be tapping into (Babur: That is correct.) all these other databases, which would take quite some time. So, the vision for this product, I mean, how did that even evolve?
Babur: A few of us who would become founders of the company, and start thinking along the same lines five, six years back. So, the problem we wanted to address is a known problem of the industry and I’d like to quantify it for you. So, just at the largest six hundred industrial companies in the world; oil and gas, manufacturing, supply, logistics and shipping; two and a half million people, two and a half million employees every day take eleven million decisions on about seventy thousand workflows. If you could improve that decision making by twenty to thirty percent, using data that they have, that’s a collective and adds 1.3 trillion dollars to their bottom line, collectively. So, this is a known problem companies have been tackling it one way or another, but there has been significant technological barriers in the way, so we innovated a computational graph to eliminate most of these barriers. Therefore, picking data from different data sources without copying the entire data source to somewhere is one of our single biggest competitive advantages. So, how can you use the data while the data sits where it is without needing to copy it back and forth?
Russ: You know, we’ve spent some time in the cognitive computing category, mostly with IBM Watson, and it sort of seemed like this is an application that IBM Watson might be able to handle in a comparable way.
Babur: That’s correct. So, I have to qualify Watson. Under the Watson brand, IBM is selling a number of tools, but this will be perfect for the deep Watson, not the simpler analytical tools, and IBM is capable of doing that. The difference is the approach. We come from a modern school of learning about the data from the data itself. The Watson comes from a traditional artificial intelligence school that you have to program the computer what to look for. It takes training Watson to do exactly what I said on this repair or customer field service sending parts to an asset site. It may take nine months to a year to train a Watson system to do it. In our case, it’s a couple of weeks (Russ: Impressive.). It’s a fundamentally different approach, so I’m not saying we’re better than IBM. If you put an inordinate amount of people and training to Watson, it’s a spectacular system, but if you don’t have that time, or the resources, or the finances, you need something that’s comparable but is very quick and does the job.
Russ: So back to this example of trying to find the right part for a situation. The person that makes the decision is sitting there at their computer, and I mean, what do they do? Do they just enter the part that they think is right and the system does all of its analysis? Or do they have to describe the situation that the part is going to be used in?
Babur: So, this is when they use MAANA, right? (Russ: Right.). Ok, so now the process looks like, when an electronic ticket is received from a call center or a customer center, with that, MAANA has already taken that tickets information, and knows the entire history of similar tickets and their efficacies; what people send, what they return. So, MAANA comes back and tells the person, these are the parts we suggest you pick. So, it’s already returning with recommendations, so the entire MAANA use case is across the board, you recommend, so you don’t wait some months, you recommend and then the individual starts drilling those recommendations and you give them what if scenarios. What if I use this part, what would it be?
Russ: Ok, so here we are at CERAWeek, and it’s primarily focused on energy; oil and gas. Do you see oil and gas, perhaps being the biggest industry for MAANA?
Babur: It is so far. It is our biggest customer base and it occupies half of my prospective customer pipeline, and the reason that we establish our maturity in this space, we became a referenceable vendor. Our customers say good things about us, so word of mouth, naturally, is doing good things for us.
Russ: Well that’s great, and I think you actually have attracted investment from the oil and gas space, right?
Babur: That’s correct. So, in the years we built the product and the company, our first two investors were General Electric and Intel. That was our seed round of financing, and then we did an A round of financing a year and a half after that. Along with GE and Intel, Chevron and ConocoPhillips became investors in the company. And then a year ago, this time, we did a B round of financing along with our existing investors, Saudi Aramco and Shell became investors.
Russ: Sounds like the dream team.
Babur: So, Russ its goes to the, we have a groundbreaking innovation, and it’s a pure invention. Its patented and has a patent portfolio. So, our customers who are also investors have a spectacular scientific background as large corporations. So, they’re capable to do a due diligence of what is being pitched to them.
Russ: And obviously that was a successful two years?
Babur: But you also need to see, look, they could just consume us as customers and we could be vendors. So, they see a significant opportunity to use MAANA platform to digitally transform all decision making. So, they want to have a say in it and see that it is adapted in their organizations.
Russ: Well, it sounds like a great story and I really appreciate you sharing it with us today.
Babur: It’s always great seeing you, Russ.
Russ: You bet. And that wraps up my discussion with Babur Ozden, the Founder and CEO of MAANA. And this is The EnergyMakers Show.
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