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Melvin Sumapung works with corporate strategy and business development at big data and analytics firm Dattabot (previously Mediatrac). He’s standing on the product stage at Tech in Asia Jakarta 2016, and he’s talking to us about porn.

He’s talking to us about porn.

“Who knows that there are basically a lot of desperate housewives in Indonesia?” he asks. A slide in his presentation shows all four iconic Desperate Housewives actresses, along with the result of a survey of 50 to 60 million people: “We find that, counterintuitively, housewives watch mobile porn a lot more.” Welcome to the wizardry of data analysis.

With magician-like swiftness, Melvin pulls out other quirky facts during his presentation. For example, a great indicator of people who are most trustworthy with credit are people who fill out the “optional” column on their debt forms. The Chinese horoscope tends to be a good predictor of farmers’ productivity.

No one expects the porn slide.

Given enough data points, one can pull information on a number of trends and relationships. But how do you pull useful data when there’s so much out there?

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Hurdles

The product stage is so packed that I can only manage to cram myself into the back of the room, where I and other standing individuals stand nearly shoulder-to-shoulder. Melvin says that good big data insights require connectivity, intelligence, and integration. Indonesia faces challenges there because internet penetration in the country is only 34 percent and the big data industry in the country is quite young.

Indonesia kicked off this year with an engineering shortage.

“In order for you to get a lot of data to be turned into insights, you need to overcome these [technological challenges]. There are a lot of data formats, structures, sources,” Melvin tells the crowd. “Preparing them is very time-consuming and complex.” Even if one executes that process without a hitch, there’s still the problem of communicating the data in a way that people will understand.

You need people for that – engineers and data scientists. Indonesia kicked off this year with an engineering shortage. Melvin says that existing engineers choose to go into fields like finance, consulting, and big corporates. “Not a lot of people have the patience and the urge to really play with data, and the challenge is in integrating or in mindset,” he says.

As an example, he points to his own slideshow presentation – it’s broken down into a basic framework (think outline style) of themes, challenges, and solutions to help the audience grasp the subject matter. Data doesn’t work like that. People need to be trained to think that way.

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Solutions

Melvin addresses a packed room.

Here’s where Melvin sees room for immediate improvement.

Indonesia’s big data connectivity problems are best tackled in the public sector, where Melvin finds the best opportunity to make the public feel the value of big data. In turn, they’ll want to use big data more, in what he sees as a “very, very reputable feedback loop.”

It comes down to the age-old conflict of mind vs. heart and realizing you don’t have to choose.

The big data industry needs better knowledge-based and deep learning AI. Dattabot is working on this by connecting with customers’ telephone companies to pull location data, call data, and browsing history. With these, it wants to build a customer profile that can understand a person’s demographic, interests, “and better yet, your impact,” says Melvin. Matching those profiles to ads and products that will appeal to people is then trial and error, and the AI learns from both.

As far as the challenge of mindset, it comes down to the age-old conflict of mind vs. heart and realizing you don’t have to choose. “What if you don’t need to choose between your gut feeling and the way you think with data?” Melvin asks. Once you come at it with that thought, everything becomes easier.

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