One weird tip to improve the success of Data Science projects

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I was recently speaking to some data science friends on Slack, and we were discussing projects and war stories. Something that came across was that ‘data science’ projects aren’t always successful.

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Source: pixabay

Somewhere around this discussion a lightbulb went off in my head about some of the problems we have with embarking on data science projects. There’s a certain amount of Cargo cult Data Science and so collectively we as a community – of business people, technologists and executives don’t think deeply enough about the risks and opportunities of projects.

So I had my lightbulb moment and now I share it with everyone.

The one weird trick is to write down risks before embarking on a project.

Here’s some questions you should ask you start a project – preferably gather all data .

  • What happens if we don’t do this project? What is the worse case scenario?
  • What legal, ethical or reputational risks are there involved if we successfully deliver results with this project?
  • What engineering risks are there in the project? Is it possible this could turn into a 2 year engineering project as opposed to a quick win?
  • What data risks are there? What kinds of data do we have, and what are we not sure we have? What risks are there in terms of privacy and legal/ ethics?

I’ve found that gathering stakeholders around helps a lot with this, you hear different perspectives and it can help you figure out what the key risks in your project are. I’ve found for instance in the past that ‘lack of data’ killed certain projects. It’s good to clarify that before you spend 3 months on a project.

Try this out and let me know how it works for you! Share your stories with me at myfullname[at]google[dot]com.

 

 

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An interview with a data artisan

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J.D.Long is the current AVP Risk Management at RenaissanceRe and has a 15 year history of working as an analytics professional.

I sent him an interview recently to see what he would say.

Good questions Peadar. Here’s a really fast attempt at answers: 
1. What project have you worked on do you wish you could go back to, and do better? 

1) I’ve been asked this question before:http://www.cerebralmastication.com/2011/04/the-best-interview-question-ive-ever-been-asked/

Longer answer: Interestingly, what I find myself thinking about when asked this question is not analytics projects where I wish I could redo the analysis, but rather instances where I felt I did good analysis but did a bad job explaining the implications to those who needed the info. Which brings me to #2… 


2. What advice do you have to younger analytics professionals? 

2) Learn technical skills and enjoy learning new things, naturally. But, 1) always plot your data to visualize relationships and 2) remember at the end of the analysis you have to tell a story. Humans are hard wired to remember stories and not numbers. Throw away your slide deck pages with a table of p values and instead put a picture of someone’s face and tell their story. Or possible show a graph that illustrates the story. But don’t forget to tell the story. 

3. What do you wish you knew earlier about being a data artisan? 

3) Inside of a firm, cost savings of $1mm seems like it should be the same as generating income of $1mm. It’s not. As an analyst you can kick and whine and gripe about that reality, or you can live with it. One rational reason for the inequality is that income is often more reproducible than cost savings. However, the real reason is psychological. Once a cost savings happens it’s the new expectation. So there’s no ‘credit’ for future years. Income is a little different in that people who can produce $1mm in income every year are valued every year. That’s one of the reasons I listed “be a profit center” in the post John referenced. There are many more reasons, but that alone is a good one. 


4. How do you respond when you hear the phrase ‘big data’? 

4) I immediately think, “buzz word alert”. The phrase is almost meaningless. I try to listen to what comes next to see if I’m interested. 

5. What is the most exciting thing about your field? 
5) Everybody loves a good “ah-ha!” moment. Analytics is full of those. I think most of us get a little endorphin drop when we learn or discover something. I’ve always been very open about what I like about my job. I like being surrounded by interesting people, working on interesting problems, and being well compensated. What’s not to love! 

Cheers, 

P.s. the post J.D.Long mentioned is http://www.johndcook.com/blog/2011/11/21/career-advice-regarding-tools/