The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.
1962 John W. Tukey writes in “The Future of Data Analysis”: “For a long time I thought I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and doubt… I have come to feel that my central interest is in data analysis… Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics… data analysis is intrinsically an empirical science… How vital and how important… is the rise of the stored-program electronic computer? In many instances the answer may surprise many by being ‘important but not vital,’ although in others there is no doubt but what the computer has been ‘vital.’” In 1947, Tukey coined the term “bit” which Claude Shannon used in his 1948 paper “A Mathematical Theory of Communications.” In 1977, Tukey published Exploratory Data Analysis, arguing that more emphasis needed to be placed on using data to suggest hypotheses to test and that Exploratory Data Analysis and Confirmatory Data Analysis "can—and should—proceed side by side."
1974 Peter Naur publishes Concise Survey of Computer Methods in Sweden and the United States. The book is a survey of contemporary data processing methods that are used in a wide range of applications. It is organized around the concept of data as defined in the IFIP Guide to Concepts and Terms in Data Processing: “[Data is] a representation of facts or ideas in a formalized manner capable of being communicated or manipulated by some process.“ The Preface to the book tells the reader that a course plan was presented at the IFIP Congress in 1968, titled “Datalogy, the science of data and of data processes and its place in education,“ and that in the text of the book, ”the term ‘data science’ has been used freely.” Naur offers the following definition of data science: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”
1977 The International Association for Statistical Computing (IASC) is established as a Section of the ISI. “It is the mission of the IASC to link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.”
1989 Gregory Piatetsky-Shapiro organizes and chairs the first Knowledge Discovery in Databases (KDD) workshop. In 1995, it became the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).
September 1994 BusinessWeek publishes a cover story on “Database Marketing”: “Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so… An earlier flush of enthusiasm prompted by the spread of checkout scanners in the 1980s ended in widespread disappointment: Many companies were too overwhelmed by the sheer quantity of data to do anything useful with the information… Still, many companies believe they have no choice but to brave the database-marketing frontier.”
1996 Members of the International Federation of Classification Societies (IFCS) meet in Kobe, Japan, for their biennial conference. For the first time, the term “data science” is included in the title of the conference (“Data science, classification, and related methods”). The IFCS was founded in 1985 by six country- and language-specific classification societies, one of which, The Classification Society, was founded in 1964. The classification societies have variously used the terms data analysis, data mining, and data science in their publications.
1996 Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth publish “From Data Mining to Knowledge Discovery in Databases.” They write: “Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archeology, and data pattern processing… In our view, KDD [Knowledge Discovery in Databases] refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data… the additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data-mining methods (rightly criticized as data dredging in the statistical literature) can be a dangerous activity, easily leading to the discovery of meaningless and invalid patterns.”
1997 In his inaugural lecture for the H. C. Carver Chair in Statistics at the University of Michigan, Professor C. F. Jeff Wu (currently at the Georgia Institute of Technology), calls for statistics to be renamed data science and statisticians to be renamed data scientists.
1997 The journal Data Mining and Knowledge Discovery is launched; the reversal of the order of the two terms in its title reflecting the ascendance of “data mining” as the more popular way to designate “extracting information from large databases.”
December 1999 Jacob Zahavi is quoted in “Mining Data for Nuggets of Knowledge” in Knowledge@Wharton: "Conventional statistical methods work well with small data sets. Today's databases, however, can involve millions of rows and scores of columns of data… Scalability is a huge issue in data mining. Another technical challenge is developing models that can do a better job analyzing data, detecting non-linear relationships and interaction between elements… Special data mining tools may have to be developed to address web-site decisions."
2001 William S. Cleveland publishes "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics." It is a plan “to enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called ‘data science.’" Cleveland puts the proposed new discipline in the context of computer science and the contemporary work in data mining: “…the benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of knowledge bases would produce a powerful force for innovation. This suggests that statisticians should look to computing for knowledge today just as data science looked to mathematics in the past. … departments of data science should contain faculty members who devote their careers to advances in computing with data and who form partnership with computer scientists.”
2001 Leo Breiman publishes “Statistical Modeling: The Two Cultures” (PDF): “There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.”
April 2002 Launch of Data Science Journal, publishing papers on “the management of data and databases in Science and Technology. The scope of the Journal includes descriptions of data systems, their publication on the internet, applications and legal issues.” The journal is published by the Committee on Data for Science and Technology (CODATA) of the International Council for Science (ICSU).
January 2003 Launch of Journal of Data Science: “By ‘Data Science’ we mean almost everything that has something to do with data: Collecting, analyzing, modeling...... yet the most important part is its applications--all sorts of applications. This journal is devoted to applications of statistical methods at large…. The Journal of Data Science will provide a platform for all data workers to present their views and exchange ideas.”
May 2005 Thomas H. Davenport, Don Cohen, and Al Jacobson publish “Competing on Analytics,” a Babson College Working Knowledge Research Center report, describing “the emergence of a new form of competition based on the extensive use of analytics, data, and fact-based decision making... Instead of competing on traditional factors, companies are beginning to employ statistical and quantitative analysis and predictive modeling as primary elements of competition. ” The research is later published by Davenport in the Harvard Business Review (January 2006) and is expanded (with Jeanne G. Harris) into the book Competing on Analytics: The New Science of Winning (March 2007).
September 2005 The National Science Board publishes “Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century.” One of the recommendations of the report reads: “The NSF, working in partnership with collection managers and the community at large, should act to develop and mature the career path for data scientists and to ensure that the research enterprise includes a sufficient number of high-quality data scientists.” The report defines data scientists as “the information and computer scientists, database and software engineers and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.”
2007 The Research Center for Dataology and Data Science is established at Fudan University, Shanghai, China. In 2009, two of the center’s researchers, Yangyong Zhu and Yun Xiong, publish “Introduction to Dataology and Data Science,” in which they state “Different from natural science and social science, Dataology and Data Science takes data in cyberspace as its research object. It is a new science.” The center holds annual symposiums on Dataology and Data Science.
July 2008 The JISC publishes the final report of a study it commissioned to “examine and make recommendations on the role and career development of data scientists and the associated supply of specialist data curation skills to the research community. “ The study’s final report, “The Skills, Role & Career Structure of Data Scientists & Curators: Assessment of Current Practice & Future Needs,” defines data scientists as “people who work where the research is carried out--or, in the case of data centre personnel, in close collaboration with the creators of the data--and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in data base technology.”
January 2009 Harnessing the Power of Digital Data for Science and Society is published. This report of the Interagency Working Group on Digital Data to the Committee on Science of the National Science and Technology Council states that “The nation needs to identify and promote the emergence of new disciplines and specialists expert in addressing the complex and dynamic challenges of digital preservation, sustained access, reuse and repurposing of data. Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science. These individuals are key to the current and future success of the scientific enterprise. However, these individuals often receive little recognition for their contributions and have limited career paths.”
January 2009 Hal Varian, Google’s Chief Economist, tells the McKinsey Quarterly: “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades… Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it… I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.”
March 2009 Kirk D. Borne and other astrophysicists submit to the Astro2010 Decadal Survey a paper titled “The Revolution in Astronomy Education: Data Science for the Masses “(PDF): “Training the next generation in the fine art of deriving intelligent understanding from data is needed for the success of sciences, communities, projects, agencies, businesses, and economies. This is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce). Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce, integrating foundational skills for lifelong learning in a world increasingly dominated by data.”
May 2009 Mike Driscoll writes in “The Three Sexy Skills of Data Geeks”: “…with the Age of Data upon us, those who can model, munge, and visually communicate data—call us statisticians or data geeks—are a hot commodity.” [Driscoll will follow up with The Seven Secrets of Successful Data Scientists in August 2010]
June 2009 Nathan Yau writes in “Rise of the Data Scientist”: “As we've all read by now, Google's chief economist Hal Varian commented in January that the next sexy job in the next 10 years would be statisticians. Obviously, I whole-heartedly agree. Heck, I'd go a step further and say they're sexy now— mentally and physically. However, if you went on to read the rest of Varian's interview, you'd know that by statisticians, he actually meant it as a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts… [Ben] Fry… argues for an entirely new field that combines the skills and talents from often disjoint areas of expertise… [computer science; mathematics, statistics, and data mining; graphic design; infovis and human-computer interaction]. And after two years of highlighting visualization on FlowingData, it seems collaborations between the fields are growing more common, but more importantly, computational information design edges closer to reality. We're seeing data scientists—people who can do it all— emerge from the rest of the pack.”
June 2009 Troy Sadkowsky creates the data scientists group on LinkedIn as a companion to his website, datasceintists.com (which later became datascientists.net).
February 2010 Kenneth Cukier writes in The Economist Special Report ”Data, Data Everywhere“: ”… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data.”
June 2010 Mike Loukides writes in “What is Data Science?”: “Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here's a lot of data, what can you make from it?’"
September 2010 Hilary Mason and Chris Wiggins write in “A Taxonomy of Data Science”: “…we thought it would be useful to propose one possible taxonomy… of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret…. Data science is clearly a blend of the hackers’ arts… statistics and machine learning… and the expertise in mathematics and the domain of the data for the analysis to be interpretable… It requires creative decisions and open-mindedness in a scientific context.”
September 2010 Drew Conway writes in “The Data Science Venn Diagram”: “…one needs to learn a lot as they aspire to become a fully competent data scientist. Unfortunately, simply enumerating texts and tutorials does not untangle the knots. Therefore, in an effort to simplify the discussion, and add my own thoughts to what is already a crowded market of ideas, I present the Data Science Venn Diagram… hacking skills, math and stats knowledge, and substantive expertise.”
May 2011 Pete Warden writes in “Why the term ‘data science’ is flawed but useful”: “There is no widely accepted boundary for what's inside and outside of data science's scope. Is it just a faddish rebranding of statistics? I don't think so, but I also don't have a full definition. I believe that the recent abundance of data has sparked something new in the world, and when I look around I see people with shared characteristics who don't fit into traditional categories. These people tend to work beyond the narrow specialties that dominate the corporate and institutional world, handling everything from finding the data, processing it at scale, visualizing it and writing it up as a story. They also seem to start by looking at what the data can tell them, and then picking interesting threads to follow, rather than the traditional scientist's approach of choosing the problem first and then finding data to shed light on it.”
May 2011 David Smith writes in "’Data Science’: What's in a name?”: “The terms ‘Data Science’ and ‘Data Scientist’ have only been in common usage for a little over a year, but they've really taken off since then: many companies are now hiring for ‘data scientists’, and entire conferences are run under the name of ‘data science’. But despite the widespread adoption, some have resisted the change from the more traditional terms like ‘statistician’ or ‘quant’ or ‘data analyst’…. I think ‘Data Science’ better describes what we actually do: a combination of computer hacking, data analysis, and problem solving.”
June 2011 Matthew J. Graham talks at the Astrostatistics and Data Mining in Large Astronomical Databases workshop about “The Art of Data Science” (PDF). He says: “To flourish in the new data-intensive environment of 21st century science, we need to evolve new skills… We need to understand what rules [data] obeys, how it is symbolized and communicated and what its relationship to physical space and time is.”
September 2011 Harlan Harris writes in “Data Science, Moore’s Law, and Moneyball” : “’Data Science’ is defined as what ‘Data Scientists’ do. What Data Scientists do has been very well covered, and it runs the gamut from data collection and munging, through application of statistics and machine learning and related techniques, to interpretation, communication, and visualization of the results. Who Data Scientists are may be the more fundamental question… I tend to like the idea that Data Science is defined by its practitioners, that it’s a career path rather than a category of activities. In my conversations with people, it seems that people who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense.”
September 2011 D.J. Patil writes in “Building Data Science Teams”: “Starting in 2008, Jeff Hammerbacher (@hackingdata) and I sat down to share our experiences building the data and analytics groups at Facebook and LinkedIn. In many ways, that meeting was the start of data science as a distinct professional specialization.... we realized that as our organizations grew, we both had to figure out what to call the people on our teams. ‘Business analyst’ seemed too limiting. ‘Data analyst’ was a contender, but we felt that title might limit what people could do. After all, many of the people on our teams had deep engineering expertise. ‘Research scientist’ was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM. However, we felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams. It might take years for lab research to affect key products, if it ever did. Instead, the focus of our teams was to work on data applications that would have an immediate and massive impact on the business. The term that seemed to fit best was data scientist: those who use both data and science to create something new. “
September 2012 Tom Davenport and D.J. Patil publish “Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review.
An earlier version of this timeline was published in WhatsTheBigData.com
See also A Very Short History of Big Data and A Very Short History of Information Technology
This article was originally published on Forbes.com and was republished on the Attunity blog with permission from the author.
About the Author
Gil Press is the Managing Partner at gPress, a marketing, publishing, research and education consultancy. Prior to gPress, he held senior marketing and research management positions at NORC, DEC and EMC. Most recently, he was Senior Director, Thought Leadership Marketing at EMC, where he launched the Big Data conversation with the “How Much Information?” study (2000 with UC Berkeley) and the Digital Universe study (2007 with IDC). Gil is a regular contributor to Forbes and he blogs on his own sites: What’s the Big Data? And The Story of Information. He can be found on Twitter at: @GilPress.