Rethinking The Framework for Data With Chu-Cheng Hsieh of Etsy

Conference: Transform X 2021

2021-10-07

Authors: Chu-Cheng Hsieh

Summary

The presentation discusses the importance of data preparation and framework in building a successful data-driven company and machine learning models.

Data preparation is crucial in building a data-driven company and machine learning models.
The data framework consists of six layers: data sources, data storage, data processing, data module reusability, matrix, and machine learning life cycles.
The last layer of the data framework is insights, which aim to educate leaders to form opinions and influence business strategies.
Observability, governance, and automation are the future opportunities in the data-driven industry.
Proper preparation prevents poor performance in building machine learning models.
All machine learning models are hypotheses and should be verified with a B test.
It is important to ensure that the solution can be understood by humans and not just a black box.

The speaker shared an example of how machine learning was used to optimize search results for an e-commerce website. Initially, the most important KPI was to optimize for click-through rate, which led to showing all cheap items on the first page. However, this resulted in showing low-quality items and was not a good idea for a marketplace. This highlights the need for insights from the data to educate leaders and influence business strategies.

Abstract

Chu-Cheng Hsieh, Chief Data Officer at Etsy, discusses how enterprises should build a data management framework that helps them translates data into revenue-generating products. He explores best practices for each stage of the entire data lifecycle, from data capture to business insights, to enable positive AI-driven outcomes. Join this session to learn how you should be thinking about getting value from data, in your own organization.

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