During this special episode, Felipe gives a presentation on what data science can do for business. The CEOs in the audience were from all sorts of industries with companies of different sizes in various stages. Business leaders can use AI to offer new solutions to their customers. Felipe explains machine learning – humans give the input and the output. The machine will do the calculations in-between the input and the output. We can automate a lot of processes using machine learning – it can help us make better decisions, understand human bias, and help us create better businesses. Very few algorithms are 99% accurate. However, they will allow your business to automate decision making and give more insight to make better decisions. Next, Felipe talks about building a data science team. Once you pay people enough, they care about three things: autonomy, mastery, and purpose. Companies are focusing on creating data science products. Most of the value is designed this way; it is very enticing for a data scientist to work in production. Every process that you automate is creating a lot of data that you should be capturing. The ways you can get data is by buying it, scraping it from the web, or collecting it within your organization. Getting information from a different division of your business will improve what you can do internally. Later, Felipe speaks about design-thinking. Find problems that your customer cares so deeply about that they have hacked together a solution. When you find something they care about that has business value, it’s a great place to start. Stay tuned as Felipe takes questions from the audience. 

Enjoy the show! 

We speak about: 

[01:15] About Felipe 

[03:30] How does machine learning work? 

[07:30] The algorithm will provide insight 

[09:30] Building a data science team 

[13:30] How to create data 

[15:20] The difference between lake, warehouse, and swamp 

[18:15] About design-thinking 

[20:55] The key points 

[22:00] Audience questions 

Resources: 

Idio: https://idio.ai 

Quotes: 

“When working with data scientists, get them away from thinking everything needs to be perfect.” 

“Machine learning can be used for automation and to make better decisions.” 

“Data scientists need to think about the outcome instead of research.” 

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