Data science has entered business vernacular with a bang...but what exactly is it?
Identified as the "sexiest job of the 21st century" by Harvard Business Review, it's no wonder that demand for the data scientist position has skyrocketed. However, according to McKinsey research, by 2018 the United States will experience a shortage of 190,000 skilled data scientists and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. With an estimated 40,000 exabytes of data being collected by 2020 -- up from 2700 exabytes in 2012 -- the implications of this shortage become apparent.
So what exactly does the role of the data scientist entail and what value can data-driven marketing bring to organizations? CXOTalk went straight to the source and talked with two of the most extraordinary data scientists of the day: Dr. Michael Wu, the Chief Scientist at Lithium Technologies and Jeremy Stanley the Chief Data Scientist of Sailthru, Inc.
The term data science has entered business vernacular with a bang…but what exactly is it? Despite all the media buzz, one story that has gone largely untold is that statisticians are asking themselves the very same question: “The exact meaning of this term is a matter of some debate; it seems like a hybrid of a computer scientist and a statistician.” I have quoted from Statistics and Science: A Report of the London Workshop on the Future of the Statistical Sciences, a product of a meeting in London in November, 2013 that was attended by more than 100 prominent statisticians from around the world.
If such a distinguished body doesn’t have the answer, for me to declare that I do would strain credibility. In place of suggesting my own definition of data science I will offer some thoughts about it and what I feel is its place in marketing research, based on my experience as a marketing science person as well as interaction with contacts and business associates who describe themselves as data scientists.
The first dimension
As noted in Statistics and Science, “data science” is loosely used to refer to lines of work that make extensive use of computer science and statistics. Most of these occupations are not directly related to marketing, genomic research and seismology being two examples, and now play a role in many fields. Data science is often coupled with the term big data, and I should note that there doesn’t appear to be much agreement about what big data means either
Back to the present
We shouldn’t let ourselves get carried away, though. In A Practitioner’s Guide to Business Analytics, Randy Bartlett devotes considerable space to organizational cultural challenges and more than he does to technical matters. We should note that the author is not a journalist or software vendor but an analytics veteran of more than 20 years who holds degrees in both computer science and statistics. I share his view that the old ways still dominate true science in most decisions: “Corporations are not as sophisticated or as successful as we might grasp from the sound bytes appearing in conferences, books, and journals. Instead opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic. We are swimming in make-believe analytics.” That is the real world as I see it too.
Identified as the "sexiest job of the 21st century" by Harvard Business Review, it's no wonder that demand for the data scientist position has skyrocketed. However, according to McKinsey research, by 2018 the United States will experience a shortage of 190,000 skilled data scientists and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. With an estimated 40,000 exabytes of data being collected by 2020 -- up from 2700 exabytes in 2012 -- the implications of this shortage become apparent.
So what exactly does the role of the data scientist entail and what value can data-driven marketing bring to organizations? CXOTalk went straight to the source and talked with two of the most extraordinary data scientists of the day: Dr. Michael Wu, the Chief Scientist at Lithium Technologies and Jeremy Stanley the Chief Data Scientist of Sailthru, Inc.
The term data science has entered business vernacular with a bang…but what exactly is it? Despite all the media buzz, one story that has gone largely untold is that statisticians are asking themselves the very same question: “The exact meaning of this term is a matter of some debate; it seems like a hybrid of a computer scientist and a statistician.” I have quoted from Statistics and Science: A Report of the London Workshop on the Future of the Statistical Sciences, a product of a meeting in London in November, 2013 that was attended by more than 100 prominent statisticians from around the world.
If such a distinguished body doesn’t have the answer, for me to declare that I do would strain credibility. In place of suggesting my own definition of data science I will offer some thoughts about it and what I feel is its place in marketing research, based on my experience as a marketing science person as well as interaction with contacts and business associates who describe themselves as data scientists.
The first dimension
As noted in Statistics and Science, “data science” is loosely used to refer to lines of work that make extensive use of computer science and statistics. Most of these occupations are not directly related to marketing, genomic research and seismology being two examples, and now play a role in many fields. Data science is often coupled with the term big data, and I should note that there doesn’t appear to be much agreement about what big data means either
Back to the present
We shouldn’t let ourselves get carried away, though. In A Practitioner’s Guide to Business Analytics, Randy Bartlett devotes considerable space to organizational cultural challenges and more than he does to technical matters. We should note that the author is not a journalist or software vendor but an analytics veteran of more than 20 years who holds degrees in both computer science and statistics. I share his view that the old ways still dominate true science in most decisions: “Corporations are not as sophisticated or as successful as we might grasp from the sound bytes appearing in conferences, books, and journals. Instead opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic. We are swimming in make-believe analytics.” That is the real world as I see it too.
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