CERAWeek Conversations with Babur Ozden

Listen to Maana CEO Babur Ozden discuss how you can make significant steps in your digital transformation journey using technology\\



Paul: Welcome to CERAWeek 2019, I’m Paul Markwell, I’m here with Babur Ozden, founder and CEO of Maana. Welcome, Babur

Babur: Hey Paul, how are you? Great to see you, thank you for having me. Well, this is another year where we’re featuring technology and digital transformation and in a really big way.

You’re a man I think who knows what digital transformation is but help us with the definition here. What is it? Is it about the people and how they change the work? Is it about the technology alone? How do you define it?

It’s definitely a combination of organizational change, cultural change, and significant technological adoptions. But as a technology vendor I am very good at understanding technological implications and the cultural organizations above my pay grade.

I’m going to focus on the on the technological side. If you could strip some buzzwords that come with digitization like artificial intelligence and machine learning and AI, IOT digital twins, etc. at the end of the day, a digital transformation is a journey companies are making significant investments to utilize their data in an unprecedented fashion.

To change the way they make day-to-day operational decisions, and this almost always boils into a few years of journey that involves doing dozens to hundreds of cases depending on the size of the company.

So from everybody’s point of view the best way to grasp a digital transformation journey is a multi-year

effort to do several hundred new use cases. Each use case will be an application.

So the speed at which these use cases could be developed, tested, deployed, and operated plays a significant success in someone’s digital transformation journey.

When I talk to people, I say ignore for a second all the technological words and stuff. When you embark a digital transformation journey, you’re embarking to do several dozen to several hundred use cases, so that’s the sort of infrastructure that you would need organizationally, culturally, and technologically to be embarked and do those successfully.

That’s all geared towards as you said, decision making, that’s correct. Decision making in a traditional way is done across teams with multiple functions. You see the impact of what you’ve been doing and how it helps to break some of the barriers or silos. Very much so, and it’s not just us, it is sort of the division of digitization, so we’re among the vendors that enable this breaking down, these decision silos.

Oil and gas companies have been phenomenally successful at improving their efficiencies and productivity, using advanced analytics over the decades.

Regrettably though, because of they make these decisions and data siloed, they’re good at improving their local decisions, but at best they can locally optimize those decisions. But digitization comes with the pursuit of that, can I now improve my decision-making so I can globally optimize my local decision points right. It’s a very important distinction of separating how half digitization makes decision improvement from traditional way of improvement.

It’s global optimization of local decisions. This is the new way of doing analytics in this digital world right. So, in a way you mean to take it and be able to roll it out across a global organization? Yes. More easily because that’s one of the barriers, right? Yes, that’s correct.

It first starts with extracting useful information and creating knowledge out of data when it sits in silos– physical silos as well as silos in different departments. The very first attempt into a digital journey is to build that digital technological infrastructure, where the data from different data silos can be made available into these digital news cases.

So, breaking out of silos without breaking the silos is the very first investment, technological investment, into a digital ambition. From that moment on, if you have let’s say hypothetically, your exploration data and your trading data sitting side by side, nothing inhibits a curious mind to find out if there are any correlations in the operations of what you explore and what you trade.

It’ll be the interesting way to grow, yeah? If I think back to the last few years about digitalization and the way that it gets used, there’s always a buzzword of the year and we do hear a lot of good things about advanced analytics machine learning, you mentioned it there. Where do you think that can have the greatest impact, that’s specifically around the sort of AI? Machine learning is the foundational algorithm rhythm category for the digital world, and it is an important algorithm.

The reason that there is a need for machine learning is that you’re now merging data from so many different data sources. Your traditional analytics when you were improving your local decision-making, you would churn data from two, three, maybe four, data sources. At most now for the same decision point, you’re churning data from 20, 30, 40, 50, sometimes hundreds of different data sources.

So, the category of algorithm you use to understand what’s in the data is changing, and this new category is a machine learning category. You run your machine learning and enter that data coming from dozens of different data sources. So it is extremely important to be able to take top efficiency out of machine learning, it runs in these siloed data sources right.

So the way you’ve described it companies are making big advances on efficiency, breaking down silos, making bigger, better decisions or decisions with new types of data. If I could think of

maybe a final question, you know, we were sitting here saying five years’ time, and we were looking at what the advances had been in digitalization in the broadest sense since today.

What do you think would have had the most impact, what might have failed, or maybe not lived up to expectation, and then what do you think is the next level, where is it going? Five years from now if somebody successfully or an industry successfully transformed itself, we would definitely be seeing a new digital side of the companies, and the traditional side of the companies. The digital side of the companies is where that there is no silo walls—legally, culturally, and organizationally.

Every piece of data is available to every subject matter expert, to model, to improve decision making. That’s one thing, the silo effect in the digital world will go away so there’ll be a new de-siloed world in the digital world. I think that’s sort of that level of achievement that you will see.

Second, you would see that the that organization has successfully managed to convert its subject matter experts into what we call today’s citizen data scientists. They themselves, people themselves, people who are making day-to-day decisions have ways to access that de-siloed world and use that data in very efficient ways without needing any other skill sets and talents.

That’s what I would call. Lastly is that a number of today’s workflows will be disrupted, so the whole idea of digitization is not gradual improvement, it is fundamentally improving things at the expense of disrupting and making useless some existing practices.

Paul: Alright, some disruption. Oh yeah thank you for that insight. Thanks everyone for watching CERAWeek 2019.


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