Indonesia is one of the hottest places for tech startups in Southeast Asia. With a new venture popping up everyday, the demand for data scientists and programmers is increasing. Algoritma Academy, a new start-up dedicated to creating a more data-driven, data-literate society, is attempting to fill that demand. Offering courses in data visualization and machine learning, the startup promises a mix of classroom technique and real-world application.
On launch day, Algoritma Academy brought in several startup founders and hosted a fireside chat. The chat was centered on data use cases and the importance of big data within business contexts.
Here are some key highlights from the talk:
- Data has a wide range of use cases. Galvin Mame of iflix noted that some of the best business decisions of his company were drawn from data insights. For instance, using data on television show preferences, they found out that Mr. Robot was one of the most pirated TV shows of the year. They subsequently bought the show, which became a runaway hit and one of the biggest shows on their platform. Irzan of Kata.ai noted that data is the “fuel to our engine,” and uses data to understand how people text and what slang is trending. “In English, there’s only one way to say ‘I,’ but in Indonesian there are probably 70. Saya, aku, gue, gua, you name it.” Building a chatbot is challenging in itself, and a Bahasa chatbot even more so. Knowing that good data is what makes good AI, Irzan cut no corners and made sure to collect as much data in as many use cases as possible.
- Tiket.com’s data success story: Natali Ardianto of booking website Tiket.com noted that data analysis has helped unlock huge sales. For instance, from looking at the data his team realized that one of the hottest problems at the time lay in filling out your name when booking tickets. Because many Indonesians have one-word names, many people could not fill out their names properly on the website and were therefore abandoning their attempts at buying tickets. By changing this form from “First Name, Last Name” to “Full name” and then doing manual work on the backend to submit names to airlines, revenue increased by IDR 10 billion. While manual work increased (Tiket.com customer support increased from 19 employees to 70), the move was worth it.
- Telecommunications companies are data powerhouses. Hiring a data guy? Consider someone with experience in Telcom. These companies have a crazy amount of data on their customers, from what apps they like to use, where they like to use them, and when. Whether or not you think someone is watching, chances are your Telcom company is. As a startup, you can model your data collection use cases on how Telcom companies use your data. For instance, tracking customers’ locations to predict where they will travel and then sending strategic push notifications to remind them to book a rental car can increase sales.
- Data science is teamwork. A single person can’t do it alone. Why? You need domain experts to contextualize data. You need data engineers to build your product. Most of the time, people can’t do both. Without a domain expert, you won’t be able to build something specific and accurate to account for exceptions and special cases. Without an engineer, you won’t be able to build your vision. Successful data science is all about collaboration and building off knowledge.
