Singularity University Exponential Data Talk

Yesterday I had the honor to present at Singularity University's Mountain View campus to speak about Data, Data Science and Big Data. I spoke to and met many of the 100 participants of the Executive Program. People come from all over the world for this program.

I had a great time sharing my Exponential Data presentation. Afterwards we had lunch and many excellent conversations!

Stephanie from http://www.thechrysalissolution.com/ was awesome as always and produced this lovely illustration from my talk.

Singularity University Executive Program - Exponential Data Lecture Illustration

Singularity University Executive Program - Exponential Data Lecture Illustration

My May 2016 lecture on big data and data science at the May 2016 Singularity University executive program.

The Exponential Data talk is my take on the nature of data, its impact on organizations and the people within them. All companies are data companies and need the processes and technologies to adapt. The Data Science Operations framework presented within the talk will help any company begin the process of deriving meaningful insights from data.

This lecture included:

  • Information on the nature of data itself and several key attributes that make data special
  • My framework that describes how to integrate and operate with data in your organization
  • Several real world examples of using data to save lives, heal the planet and generate immense economic value

With the right data, processes and technologies companies can create immense leverage for people. Data is power.

Exponential Data Implementation Framework

Exponential Data Implementation Framework

Three Things Observed in the Field That Help Make You a Data Scientist

An interesting and little understood fact about being or becoming a Data Scientist is that almost anyone can be a data scientist.

Like most professions there are different types and skill levels involved. Some are more hardcore in areas like math, software engineering or information design. Others are more day to day business. The traits that make it far more likely that someone may adopt the title or work of a data scientist regardless of their specific role are as follows. I tend to find these traits in the people I like to work with the most.

Scientific Process Approach

The scientific method reigns supreme in real data science. It's quite different to do data science that it is to do software engineering. Generally, when you build software to a specific goal it gets better and better and better until you can release. When you do data science, sometimes no matter how hard you work on your models you get the end and it's a dead end and you simply have to discard the results and go back to the beginning.

Have (and be able to keep) Beginner’s Eyes

To minimize the impact of bias as much as a human being can is extremely challenging. Some may even argue that it is impossible. Therefore, if you are approaching a data science problem it's very useful to do so from a beginner's mindset. Do not already know that you know the best algorithm. Hypothesis that you might then test. Do not already know the answer before you have even asked the question. Try to look at each opportunity to apply data science with fresh eyes.

Ability to Communicate Results Effectively

No matter how amazing the model you train. No matter how eloquent the solution you devise. No matter at all. No one cares. They care about results. They care if they can understand you. Becoming a master of the visual display of information could not be more critical to the efficacy of your results over time. This might be a pretty chart or graph. It might be a highly complex mobile or web application. Your output might be a powerpoint presentation. Whatever the delivery medium it pays huge dividends to invest in the consumability of the output.