Summary:<\/em><\/strong>\u00a0 A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it\u2019s all the same thing.<\/em><\/p>\n \u00a0<\/p>\n Our profession of finding the signal in the data, be that supervised or unsupervised got underway in the 90s.\u00a0 In the last 20+ years we\u2019ve been called by a variety of names.\u00a0 It\u2019s not at all clear that as those names changed that any clarity was added.\u00a0<\/p>\n In fact, for a profession as concerned with accuracy as we are, we\u2019ve done a pretty poor job at naming things.\u00a0 Take \u2018Big Data\u2019 for instance.\u00a0 Not really about \u2018big\u2019 at all.\u00a0 Just as much about velocity and variety as it is about volume.\u00a0 Or NoSQL which has pretty much lost its meaning since all those NoSQL DBs now run SQL just fine.\u00a0 Or \u2018artificial intelligence\u2019.\u00a0 That term has been high jacked by the press and developers to put the sheen of AI on pretty much everything we do.<\/p>\n So just for fun, here\u2019s a brief recap of all the names that have been used to describe what we do.<\/p>\n \u00a0<\/p>\n KDD (Knowledge Discovery in Databases):<\/strong>\u00a0<\/span> This is the oldest title I can personally remember to describe what we do.\u00a0 Coined by Gregory Piatetsky-Shapiro in 1989.\u00a0 We still had both feet firmly planted in BI and its retrospective point of view but already there were the inklings that the same data could also tell us many things about the future.<\/p>\n \u00a0<\/p>\n Data Mining:<\/strong><\/span> \u00a0DM was originally intended to describe what went on in KDD, but like \u2018AI\u2019 the term was widely adopted in the business press and became the more popular descriptor up through the mid-2000s.<\/p>\n \u00a0<\/p>\n Predictive Modeling:<\/strong><\/span>\u00a0 When I first got involved in data science in 2001, Predictive Modeling was the preferred term.\u00a0 It more accurately described what we were doing and the tools we were using to predict future behavior and future values.\u00a0<\/p>\n It caught me by surprise that Gartner and the other review agencies changed that name almost immediately to \u2018Predictive Analytics\u2019<\/strong>.\u00a0 This was at the time when data viz began to play a more important role and if you read the reports from that period it looks like Gartner and the others generalized the name to \u2018Analytics\u2019 to allow the data viz platforms a place at the table.<\/p>\n \u00a0<\/p>\n Prescriptive Analytics:<\/strong><\/span>\u00a0 In 2014 Gartner once again got involved in changing definitions by introducing Prescriptive Analytics as separate from Predictive Analytics.\u00a0 Gartner\u2019s definition says we should differentiate what \u2018could happen\u2019 (predictive) versus what \u2018should happen\u2019 (prescriptive).\u00a0 I admit that I still see this as a distinction without a difference<\/a><\/u><\/em> since \u2018prescriptive\u2019 is merely \u2018predictive\u2019 with some optimization math applied.\u00a0 That\u2019s something we had been doing all along.<\/p>\n \u00a0<\/p>\n Machine Learning:<\/strong>\u00a0<\/span> The term ML actually predates KDD but it only came into common use in the last half of the 2000s.\u00a0 As our techniques for supervised and unsupervised learning became more diverse with the adoption of SVMs, ensemble methods, and the rebirth of ANNs, there was renewed focus on the fact that many new techniques belonged in the tent provided they met the original criteria of discovering patterns in the data without being explicitly programmed to do so.<\/p>\n 2006 marks the beginning of the NoSQL age with open source Hadoop that allowed us to begin to apply ML techniques to unstructured and semi-structured text and image data.\u00a0 ML still describes the most commonly adopted business applications of data science through scoring models and forecasting and the source of most of the value currently created by data science.<\/p>\n \u00a0<\/p>\n Deep Learning:<\/strong><\/span>\u00a0 To data scientists, the term Deep Learning or its more explicit title Deep Neural Nets (DNNs)<\/strong> was an outgrowth of the introduction of NoSQL DBs and the rapidly increasing compute capacity of advanced chips and the cloud.\u00a0 To data scientists, DL\/DNN was and is the tool set that enabled what we came to see as artificial intelligence.<\/p>\n It took from the advent of open source Hadoop in 2006 until about 2016 or 2017 to reach human-capable levels of speech, text, and image recognition, the cornerstones of AI.<\/p>\n It\u2019s also worth noting that the original field of ML continued to innovate better and better algorithms through about 2016.\u00a0 Very little in the way of major break throughs has occurred in ML since that time and for the last several years both ML and AI have been in mature implementation and value harvesting phases.<\/p>\n \u00a0<\/p>\n Artificial Intelligence (AI)<\/strong>:\u00a0<\/span> By 2017 the term \u201cAI\u201d had been fully appropriated by the press, the public, and even by developers.\u00a0 It evolved into a generic phrase literally defined as:<\/p>\n Anything that makes a decision or takes an action that a human used to take, or helps a human make a decision or take an action.<\/em><\/p>\n So as you have conversations with potential users today you need to have that up-front qualifying conversation about \u2018what do you really mean when you say you want an AI solution\u2019.\u00a0 The great majority of implementations continue to be Machine Learning and recently, at least within the data science profession, there\u2019s been a return to more accurately labeling this as \u201cML\/AI\u201d<\/strong>.<\/p>\n \u00a0<\/p>\n Is This Really All the Same Thing?<\/strong><\/span><\/p>\n There is a wonderful set of just five questions that describes everything we do with our models in data science.<\/p>\n I apologize that I\u2019m unable to find the author\u2019s name to credit.\u00a0 This simple summary drives home the fact that no matter what techniques you\u2019re using, and whether you\u2019re deep in DNNs or in equally sophisticated ML techniques like XGBoost, that what we do has a very common and easy to understand purpose, regardless of what you call it.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is Contributing Editor for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0 His articles have been read more than 2 million times.<\/p>\n He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n \u00a0<\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a>A little reminiscence, or for those of you who are only recently data scientists, a little history lesson.\u00a0<\/p>\n
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