I currently serve as Vice President of Decision Science at CenturyLink. I've previously served as a leader in the Advanced Risk & Compliance Analytics (ARCA) practice at PwC and as Director of Data Science & Analytics Engineering at Areté Associates. I've served the public as Chair of the Thousand Oaks, CA Planning Commission. I have been married to my wife Stephanie since 1993, and we have a wonderful daughter Monroe. Learn more about me »
The following essay is adapted from the forthcoming book TUESDAY NIGHT FIGHTS, which details the political and self-governance history of Thousand Oaks.
Twenty-five years ago, in 1996, Thousand Oaks voters were included as an official part of preserving the city’s general plan, as ordinances were enacted requiring voter approval of important general plan changes before becoming effective. Sponsored by the two political protagonists of the time, Linda Parks authored the Parks Initiative, a precursor to the successful Ventura County open space protection campaigns, while Andy Fox championed the city-sponsored Measure E.
You might think journalism and data science don’t really go together, but on that, I differ. Below are some thoughts on the topic and lessons we can draw from data science on how to make journalism better and more effective in these times.
It’s amazing that we’ve now had our collective awareness heightened to the problem of fake news. I get frustrated at times with the sheer nonsense that seems to swim in the public consciousness, but in search of what I can do about it, I figured I’d share something that happened to me recently.
Data Science has become an exploding field in recent years, and depending on whether you are focusing on machine learning, artificial intelligence, or citizen data science, the discipline of data science is creating very high expectations.
There is indeed much promise for data science, where predictive models and decision engines can target skin cancer in patient imagery, presciently recommend a new product that piques your interest, or power your self-driving car to evade a potential accident.
However, promise requires much effort for it to be realized. It takes a lot of work and brand new engineering disciplines that are not yet mature or even employed on a wide scale. As there is greater recognition of the value of data science, and the generation of data is increasing at exponential rates, this engineering effort is starting and will grow beyond its adolescence soon.
This is why we are at the advent of a new engineering discipline that can truly realize the promise of data science – a discipline that I call “analytics engineering”.
Richard Feynman is one of the greatest scientific minds, and what I love about him, aside from his brilliance, is his perspective on why we perform science. I’ve been reading the compilation of short works of Feynman titled The Pleasure of Finding Things Out, and I recently came across a section that really hit home with me.
In the world of data science, much is made about the algorithms used to work with data, such as random forests or k-mean clustering. However, I believe there is a missing component – one that deals the fundamentals underlying data science, and that is the real science of data science.
I’ve been performing data science before there was a field called “data science“, so I’ve had the opportunity to work with and hire a lot of great people. But if you’re trying to hire a data scientist, how do you know what to look for, and what should you consider in the interview process?
I’ve been doing what is now called “data science” since the early 1990s and have helped to hire numerous scientists and engineers over the years. The teams I’ve had the opportunity to work with are some of the best in the world, tackling some of the most challenging problems facing our country. These folks are also some of the smartest people I’ve ever had the opportunity to work with.
That said, not everyone is a good fit, and the discipline of data science requires important key elements. Hiring someone into your team is incredibly important to your business, especially if you’re a small startup or building a critical internal data science team; mistakes can be expensive in both time and money. This can be even more intimidating if you don’t have the background or experience in hiring scientists, especially someone responsible for this new discipline of working with data.
Science and business seem like two very different disciplines, but is the best approach to learning any different in these two fields? These areas of life seem so unique, and the people in them can be quite varying (one with the nerdy pocket protector and the other dressed in the well-tailored suit). However, both science and business require learning, and the best approach to learning in each is really the same.
The best approach to learning is generally through failure. For example, Thomas Edison failed an astounding number of times before he invented a working lightbulb, and there are likely thousand of stories about how successes came as a result of many tries and many failures.
I read a couple of items in this month’s Fortune magazine that I thought it was worth passing along.
The first was a small article by Brian Dumaine about the work being done at Applied Proteomics to identify cancer before it develops. At Applied Proteomics, they use mass spectroscopy to capture and catalog 360,000 different pieces of protein found in blood plasma, and then let supercomputers crunch on the data to identify anomalies associated with cancer. The company has raised $57 million in venture capital and is backed by Microsoft co-founder Paul Allen. You can read the first bit of the article here.
The second is from the Word Check callout, showing how access to information is making the word a better place:
wasa: Pronounced [wah-SUH]
(noun) Arabic slang: A display of partiality toward a favored person or group without regard for their qualifications. A system that drives much of life in the Middle East — from getting into a good school to landing a good job.