Building a Business Case for Data Science and Analytics

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Watch the video of this episode: https://www.youtube.com/watch?v=UglmEt_CRQE

Memorable Quotes from the episode:

[00:31:19] "Design thinking is a great ideation framework for understanding based on the business outcome, how we can tackle that. It's five simple steps. The first one is to empathize with the stakeholder, and that's a word that I think we need to be saying a lot more in this practice is empathy."

Highlights of the show:

[00:01:22] Guest Introduction

[00:03:04] Talk to us a little bit about how you got interested in data science and what was your path into the field like?

[00:05:08] How much more hype has data science, A.I. and all that become since you first broke into the field?

[00:06:26] What do you see happening in 2022 in data science and analytics? What's the big thing that you're excited or hopeful about?

[00:13:34] What are some guiding principles that we should keep in mind to ensure that we're successfully building and leading those?

[00:15:07] What's the etiquette behind the kicking of the doors?

[00:16:48] We will get into 'design thinking' part of the book, but I want to double down on the 'process' aspect of the book. What is 'process' anyways and what is it all about?

[00:18:16] What are some some ways that we can ensure that our processes remain parsimonious? And if you got any examples that you want to share with us.

[00:19:50] Talk to us about comprehensive group of processes that that are required for for project success.

[00:23:48] Walk us through prioritization projects.

[00:25:25] Identifying things that are important, we talk about this with respect to a project scoping and planning that there's some questions that we should ask ourselves and ask our stakeholders. Two crucial ones. Can you share those questions with us? And what is it that we hope to get from from asking those questions?

[00:27:47] When it comes to dealing with stakeholders or let's say we've identified that this is a problem that we should be working on, but how do we make it? How do we frame it from the business problem to an analytics problem? What are some questions we should use to tease out what we need to, to properly frame it?

[00:31:06] There's something that you talk about called 'design thinking'. What is design thinking? What's it all about? And what does this have to do with 'process'? What does this have to do with data science?

[00:32:42] It seems like designing requires a skills that are underdeveloped in a lot of data science and analytic professionals. How do we cultivate those skills and make that process enjoyable for everyone who's involved?

[00:34:46] When it comes to executing a project, does Agile have a place in the data science world?

[00:35:32] Do you have a structured approach for generating demand within an organization, especially for new teams where all business functions are our customers?

[00:37:00] What is a SKU morph and how can we use this to our advantage in data science?

[00:39:20] Are there, if you know of any studies about how agile methods can be applied to teams in data analytics or finance.

[00:42:53] How can we start viewing ourselves as craftspeople? What do you mean by a 'bi craftsperson'? How can we start being ourselves as that?

[00:45:34] It's been extremely hard to hire and keep great data scientists. Do you have any tips that have worked for you? You've touched on a few of those, but have you got any additional tips for that?

[00:47:20] Apart from the technical skills, what is it that you look for in data science candidates?

[00:48:39] How can an individual contributor embody the characteristics of a good leader without necessarily having that title?

[00:50:11] It's 100 years in the future. What do you want to be remembered for?

Random Round:

[00:50:45] Let's just think about some interesting use cases for data science and machine learning in the aviation industries. What are a couple of ways that machine learning is being used there?

[00:52:37] If you were to write a fiction novel, what would it be about and what would you title it?

[00:53:00] What are you currently reading?

[00:53:14] What are you currently most excited about or currently exploring?

[00:53:51] What's something you learned in the last week?

[00:54:02] What have you created that you're most proud of?

[00:54:15] Have you ever saved someone's life?

[00:54:21] What's the best compliment you've ever received?

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