Of course, the debate between science and philosophy is a very old one, as is the argument we need both.  In my previous post, I slightly paraphrased Immanuel Kant (“perception without conception is blind and conception without perception is empty”) by saying that science without philosophy is blind and philosophy without science is empty.

In his book Cosmic Apprentice: Dispatches from the Edges of Science, Dorion Sagan explained that science and philosophy hang “in a kind of odd balance, watching each other, holding hands.  Science’s eye for detail, buttressed by philosophy’s broad view, makes for a kind of alembic, an antidote to both.  Although philosophy isn’t fiction, it can be more personal, creative and open, a kind of counterbalance for science even as it argues that science, with its emphasis on a kind of impersonal materialism, provides a crucial reality check for philosophy and a tendency to over-theorize that’s inimical to the scientific spirit.  Ideally, in the search for truth, science and philosophy, the impersonal and autobiographical, can keep each other honest in a kind of open circuit.”

“Science’s spirit is philosophical,” Sagan concluded.  “It is the spirit of questioning, of curiosity, of critical inquiry combined with fact-checking.  It is the spirit of being able to admit you’re wrong, of appealing to data, not authority.”

“Science,” as his father Carl Sagan said, “is a way of thinking much more than it is a body of knowledge.”  By extension, we could say that data science is about a way of thinking much more than it is about big data or about being data-driven.

I have previously blogged that science has always been about bigger questions, not bigger data.  As Claude Lévi-Strauss said, “the scientist is not a person who gives the right answers, but one who asks the right questions.”  As far as data science goes, what are the right questions?  Data scientist Melinda Thielbar proposes three key questions (Actionable? Verifiable? Repeatable?).

Here again we see the interdependence of science and philosophy.  “Philosophy,” Marilyn McCord Adams said, “is thinking really hard about the most important questions and trying to bring analytic clarity both to the questions and the answers.”

“Philosophy is critical thinking,” Don Cupitt said. “Trying to become aware of how one’s own thinking works, of all the things one takes for granted, of the way in which one’s own thinking shapes the things one’s thinking about.”  Yes, even a data scientist’s own thinking could shape the things they are thinking scientifically about.  Big data evangelist James Kobielus recently blogged about five biases that may crop up in a data scientist’s work (Cognitive, Selection, Sampling, Modeling, Funding).

“Data science has a bright future ahead,” explained Hilary Mason in a recent interview.  “There will only be more data, and more of a need for people who can find meaning and value in that data.  We’re also starting to see a greater need for data engineers, people to build infrastructure around data and algorithms, and data artists, people who can visualize the data.”

I agree with Mason, and I would add that we are also starting to see a greater need for data philosophers, people who can, borrowing the words that Anthony Kenny used to define philosophy, “think as clearly as possible about the most fundamental concepts that reach through all the disciplines.”

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“A philosopher’s job is to find out things about the world by thinking rather than observing,” the philosopher Bertrand Russell once said.  One could say a scientist’s job is to find out things about the world by observing and experimenting.  In fact, Russell observed that “the most essential characteristic of scientific technique is that it proceeds from experiment, not from tradition.”

Russell also said that “science is what we know, and philosophy is what we don’t know.”  However, Stuart Firestein, in his book Ignorance: How It Drives Science, explained “there is no surer way to screw up an experiment than to be certain of its outcome.”

Although it seems it would make more sense for science to be driven by what we know, by facts, “working scientists,” according to Firestein, “don’t get bogged down in the factual swamp because they don’t care that much for facts.  It’s not that they discount or ignore them, but rather that they don’t see them as an end in themselves.  They don’t stop at the facts; they begin there, right beyond the facts, where the facts run out.  Facts are selected for the questions they create, for the ignorance they point to.”

In this sense, philosophy and science work together to help us think about and experiment with what we do and don’t know.

Some might argue that while anyone can be a philosopher, being a scientist requires more rigorous training.  A commonly stated requirement in the era of big data is to hire data scientists, but this begs the question: Is data science only for data scientists?

“Clearly what we need,” Firestein explained, “is a crash course in citizen science—a way to humanize science so that it can be both appreciated and judged by an informed citizenry.  Aggregating facts is useless if you don’t have a context to interpret them.”

I would argue that clearly what organizations need is a crash course in data science—a way to humanize data science so that it can be both appreciated and judged by an informed business community.  Big data is useless if you don’t have a business context to interpret it.  Firestein also made great points about science not being exclusionary (i.e., not just for scientists).  Just as you can enjoy watching sports without being a professional athlete and you can appreciate music without being a professional musician, you can—and should—learn the basics of data science (especially statistics) without being a professional data scientist.

In order to truly deliver business value to organizations, data science can not be exclusionary.  This doesn’t mean you shouldn’t hire data scientists.  In many cases, you will need the expertise of professional data scientists.  However, you will not be able to direct them or interpret their findings without understanding the basics, what could be called the philosophy of data science.

Some might argue that philosophy only reigns in the absence of data, while science reigns in the analysis of data.  Although in the era of big data there seems to be fewer areas truly absent of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise.  So, an endless oscillation persists between science and philosophy, which is why science without philosophy is blind, and philosophy without science is empty.  Data needs both science and philosophy.

Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

• Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.

• Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.

• Decision Management Systems — Guest James Taylor discusses data-driven decision making and analytical concepts from his book Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

• So Long 2011, and Thanks for All the . . . — The OCDQ Radio 2011 Year in Review, featuring Jarrett Goldfedder, who discusses Big Data, Nicola Askham, who discusses Data Governance, and Daragh O Brien, who discusses Data Privacy.

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