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

Conference:  Transform X 2021


Authors:   Chu-Cheng Hsieh


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.


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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