However, one of the challenges Lazada faced was designing a system that can capture data at the speed and volume that the company generates. "At present, we capture about 100GB of data a day; and at peak periods, we can capture up to tens of thousands of user events per second," he said.
Right people behind the right technology
Moving forward, Hardenberg said Lazada aims to deliver more actionable insights and real-time product recommendations to their customers.
The company is also evaluating new technologies that they can leverage to improve customer experience.
"We have to be very selective of the technology that we employ, and we are constantly keeping an eye on new solutions. When we see something that can potentially help us solve a problem and enhance the consumer's experience, we are not afraid to explore it," said Hardenberg.
But behind every technological success is an excellent technologist, he cautioned. "Technology acts as an enabler; and its real benefits can only be reaped if we have the right people with the accompanying skillsets."
When adopting big data, he said that there are two important factors that need to be considered: getting the right data to the right place at the right time, and using the data wisely. "While it may sound obvious, a lot of companies look at the data they have before thinking about what they can do with it. However, at Lazada, we found that it was a lot more useful to look at the problems you want to solve and then work backwards," explained Hardenberg.
Aside from data science, he said retailers and other e-commerce companies should also take note of the wealthy results data engineering - which includes collecting and processing data, as well as operationalising results - can produce.
In addition, Hardenberg said that companies should not only be analysing existing data, but also work on capturing the data they need.
Lastly, organisations should also take advantage of the offerings of big data solutions such as cheap data storage. "Take advantage of it and don't throw out raw data that might be useful or interesting. Even if its use is not apparent now, you may find a use for it later," Hardenberg advised.
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