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• 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.