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O’Reilly Radar: What is Data Science?

Mike Loukides has an excellent piece on O’Reilly Radar entitled “What is data science?”  In the article, Loukides covers making data products, the data lifecyle, working with data at scale (Big Data), story telling and data scientists. 

Throughout the article, Loukides introduces the reader to many data science concepts, tools, experts and skills.

Calling out several items, I love the “data exhaust” term:

“These recommendations are "data products" that help to drive Amazon's more traditional retail business. They come about because Amazon understands that a book isn't just a book, a camera isn't just a camera, and a customer isn't just a customer; customers generate a trail of "data exhaust" that can be mined and put to use, and a camera is a cloud of data that can be correlated with the customers' behavior, the data they leave every time they visit the site.”

I think this “make lemonade” sentiment on data quality is crucial:

“Once you've parsed the data, you can start thinking about the quality of your data. Data is frequently missing or incongruous. If data is missing, do you simply ignore the missing points? That isn't always possible. If data is incongruous, do you decide that something is wrong with badly behaved data (after all, equipment fails), or that the incongruous data is telling its own story, which may be more interesting? It's reported that the discovery of global warming was delayed because automated data collection tools discarded readings that were too low 1. In data science, what you have is frequently all you're going to get. It's usually impossible to get "better" data, and you have no alternative but to work with the data at hand.”

The big data definition is excellent.  It’s about the problem, not the (product) solutions:

“The most meaningful definition I've heard: "big data" is when the size of the data itself becomes part of the problem. We're discussing data problems ranging from gigabytes to petabytes of data. At some point, traditional techniques for working with data run out of steam.”

And the information platforms / dataspaces concept ties to my active information tier:

“What are we trying to do with data that's different? According to Jeff Hammerbacher 2 (@hackingdata), we're trying to build information platforms or dataspaces. Information platforms are similar to traditional data warehouses, but different. They expose rich APIs, and are designed for exploring and understanding the data rather than for traditional analysis and reporting. They accept all data formats, including the most messy, and their schemas evolve as the understanding of the data changes.”

If you want to learn something today, read the article. Then bookmark it for future reference.

Lessons from the Crisis: Behavior Matters

The July/August issue of the Harvard Business Review has a feature by McKinsey & Company on 10 Trends You Have to Watch.  The premise is after a year in turmoil, business executives are starting to look towards the future.  However, the world has changed, and with it, so have some key trends.

The trend that caught my attention – Management as Science -- falls squarely in the datarati realm:

“Data, computing power, and mathematical models have been transforming many realms of management from art to science. But the crisis exposed the limitations of certain tools. In particular, the world saw the folly of the reliance by banks, insurance companies, and others on financial models that assumed economic rationality, linearity, equilibrium, and bell-curve distributions. As the recession unfolded, it became clear that the models had failed badly.

It would be wrong to conclude that managers should go back to making decisions only on the basis of gut instinct. The real lessons are that the tools need to incorporate more-realistic visions of human behavior—most likely by drawing on behavioral economics, becoming more dynamic, and integrating real-world feedback—and that business executives need to get better at using them. Companies will, rightly, continue to seek ways to exploit the increasing amounts of data and computing power. As they do so, decision makers in every industry must take responsibility for looking inside the black boxes that advanced quantitative tools often represent and understanding their functioning, assumptions, and limitations.”

In retrospect, this makes perfect sense.  Human behavior is far from universally predictable.  Recall how the U.S. Government expected citizens to re-invigorate the economy by engaging in non-essential shopping with that first stimulus check.  Instead, what did many do?  Paid bills, bought groceries or tucked it away for the tough times to come.  Survival instincts won out over an algorithm.

Once you recognize that behavior matters, a natural follow-on is, “where does behavioral data come”?  No surprise, Google has a veritable treasure trove:

“Wu calls Google "the barometer of the world." Indeed, studying the clicks is like looking through a window with a panoramic view of everything. You can see the change of seasons—clicks gravitating toward skiing and heavy clothes in winter, bikinis and sunscreen in summer—and you can track who's up and down in pop culture. Most of us remember news events from television or newspapers; Googlers recall them as spikes in their graphs. "One of the big things a few years ago was the SARS epidemic," Tang says. Wu didn't even have to read the papers to know about the financial meltdown—he saw the jump in people Googling for gold.”

As for the rest of us, we can mine internal and public datasets, setup prediction markets, employ sentiment tools and/or hire behavioral economics consultants.  First though, I’d recommend familiarizing yourself with the field of behavioral economics, and pay special attention to the datarati ties. I plan to ease myself in with Dan Ariely’s Predictably Irrational. 

If you have experience applying behavioral economics in your business, or reading/learning suggestions, please share what you can in the comments or via email.

Lessons from Baseball Science: A picture is worth 1000 data points

It’d be easy to chalk up today’s choice to my being in pre-vacation mode, but in truth, I’ve had this New York Times Baseball Science article open in a tab for nearly a month.  When I first read it, I immediately thought of connections to my recent post Lessons from Googlenomics: Data Abundance, Insight Scarcity. 

In the referenced Wired Googlenomics article, Hal Varian asks, "What's ubiquitous and cheap?" His answer "Data." He follows up with “And what is scarce? The analytic ability to utilize that data.” 

The Baseball Science article highlights an innovative way Major League Baseball is collecting even more player data – defense and base running – via a new system of high-resolution cameras and supporting software:

“A new camera and software system in its final testing phases will record the exact speed and location of the ball and every player on the field, allowing the most digitized of sports to be overrun anew by hundreds of innovative statistics that will rate players more accurately, almost certainly affect their compensation and perhaps alter how the game itself is played.

…In San Francisco, four high-resolution cameras sit on light towers 162 feet up, capturing everything that happens on the field in three dimensions and wiring it to a control room below. Software tools determine which movements are the ball, which are fielders and runners, and which are passing seagulls. More than two million meaningful location points are recorded per game.” 

However, the system output is “simple time-stamped x-y-z coordinates” which require sophisticated algorithms to turn the raw data into insights:

“Software and artificial-intelligence algorithms must still be developed to turn simple time-stamped x-y-z coordinates into batted-ball speeds, throwing distances and comparative tools to make the data come alive.”

Beyond turning the raw data into meaningful information regarding player actions and game outcomes, the teams, league, and legions of fans and broadcasters, still need to figure out how to act on, and manage, this data trove:

“Teams have begun scrambling to develop uses for the new data, which will be unveiled Saturday to a group of baseball executives, statisticians and academics, knowing it will probably become the largest single advance in baseball science since the development of the box score. Several major league executives would not publicly acknowledge their enthusiasm for the new system, to better protect their plans for leveraging it.

“It can be a big deal,” the Cleveland Indians’ general manager, Mark Shapiro, said. “We’ve gotten so much data for offense, but defensive objective analysis has been the most challenging area to get any meaningful handle on. This is information that’s not available anywhere. When you create that much data you almost have to change the structure of the front office to make sense of it.””

The above two challenges, making the data meaningful, and developing actionable business insights, are accomplished by individuals that Hal Varian refers to as the “datarati”:

“Varian believes that a new era is dawning for what you might call the datarati—and it's all about harnessing supply and demand. "What's ubiquitous and cheap?" Varian asks. "Data." And what is scarce? The analytic ability to utilize that data. As a result, he believes that the kind of technical person who once would have wound up working for a hedge fund on Wall Street will now work at a firm whose business hinges on making smart, daring choices—decisions based on surprising results gleaned from algorithmic spelunking and executed with the confidence that comes from really doing the math.”

In the baseball world, Billy Beane and Theo Epstein are considered ‘datarati’ archetypes. 

As a geek by trade and a lifelong baseball fan, I find myself intrigued by this new data collection technology and the resulting analytic and management possibilities.  Of course, it also got me thinking beyond baseball, and sports, to wonder what other fields (no pun intended) might benefit from digital camera based data collection and data point to scenario reconciliation.

From my own background, I can envision the technology being applied to analyze and improve efficiencies in retail stores, warehouses and factories.  How about you?

Some questions to consider:

Could this data collection technique benefit your organization? 

How about as a data consumer?  Can you think of an external scenario that might provide meaningful “simple time-stamped x-y-z coordinates” to your organization?

Has your organization embraced the rise of the datarati?

Lessons from Googlenomics: Data abundance, Insight Scarcity

“"What's ubiquitous and cheap?" [Google’s Hal] Varian asks. "Data." And what is scarce? The analytic ability to utilize that data.”

The June issue of Wired has an excellent article by Steven Levy, entitled Secret of Googlenomics: Data-Fueled Recipe Brews Profitability.  The article delves into the history and algorithms behind Google’s auction based ad system, highlighting the significance of engineering, mathematics, economics, and data mining in Google’s success.

On the economics front, the article explains Hal Varian’s role as Chief Economist at Google, including why Google needs a chief economist:

“The simplest reason is that the company is an economy unto itself. The ad auction, marinated in that special sauce, is a seething laboratory of fiduciary forensics, with customers ranging from giant multinationals to dorm-room entrepreneurs, all billed by the world's largest micropayment system.

Google depends on economic principles to hone what has become the search engine of choice for more than 60 percent of all Internet surfers, and the company uses auction theory to grease the skids of its own operations. All these calculations require an army of math geeks, algorithms of Ramanujanian complexity, and a sales force more comfortable with whiteboard markers than fairway irons.”

After reading the article, Varian’s economic view of data ubiquity and analytic scarcity really stuck with me.  The quote I opened the post with isn’t directed at software availability or processing power.  It refers to the scarcity of people qualified to churn abundant data into economic value.  

What follows are some excerpts “about harnessing supply and demand”.  The sub-headers and emphasis are mine.

Enter Econometricians

"The people working for me are generally econometricians—sort of a cross between statisticians and economists," says Varian, who moved to Google full-time in 2007 (he's on leave from Berkeley) and leads two teams, one of them focused on analysis.

"Google needs mathematical types that have a rich tool set for looking for signals in noise," says statistician Daryl Pregibon, who joined Google in 2003 after 23 years as a top scientist at Bell Labs and AT&T Labs. "The rough rule of thumb is one statistician for every 100 computer scientists."

Ubiquitous Data

“As the amount of data at the company's disposal grows, the opportunities to exploit it multiply, which ends up further extending the range and scope of the Google economy…

Keywords and click rates are their bread and butter. "We are trying to understand the mechanisms behind the metrics," says Qing Wu, one of Varian's minions. His specialty is forecasting, so now he predicts patterns of queries based on the season, the climate, international holidays, even the time of day. "We have temperature data, weather data, and queries data, so we can do correlation and statistical modeling," Wu says. The results all feed into Google's backend system, helping advertisers devise more-efficient campaigns.”

Continuous Analysis

“To track and test their predictions, Wu and his colleagues use dozens of onscreen dashboards that continuously stream information, a sort of Bloomberg terminal for the Googlesphere. Wu checks obsessively to see whether reality is matching the forecasts: "With a dashboard, you can monitor the queries, the amount of money you make, how many advertisers you have, how many keywords they're bidding on, what the rate of return is for each advertiser."”

Behavioral Based Insights

“Wu calls Google "the barometer of the world." Indeed, studying the clicks is like looking through a window with a panoramic view of everything. You can see the change of seasons—clicks gravitating toward skiing and heavy clothes in winter, bikinis and sunscreen in summer—and you can track who's up and down in pop culture. Most of us remember news events from television or newspapers; Googlers recall them as spikes in their graphs. "One of the big things a few years ago was the SARS epidemic," Tang says. Wu didn't even have to read the papers to know about the financial meltdown—he saw the jump in people Googling for gold. And since prediction and analysis are so crucial to AdWords, every bit of data, no matter how seemingly trivial, has potential value.”

Rise of the Datarati

“Varian believes that a new era is dawning for what you might call the datarati—and it's all about harnessing supply and demand. "What's ubiquitous and cheap?" Varian asks. "Data." And what is scarce? The analytic ability to utilize that data. As a result, he believes that the kind of technical person who once would have wound up working for a hedge fund on Wall Street will now work at a firm whose business hinges on making smart, daring choices—decisions based on surprising results gleaned from algorithmic spelunking and executed with the confidence that comes from really doing the math.”

Now, a few questions I think folks should consider:

1. Who does that math in your organization? 
2. Does your analytics / active information strategy suffer from information processing richness and insight scarcity?
3. Who are, or should be, your datarati?