Summary:<\/em><\/strong>\u00a0 The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.<\/em><\/p>\n \u00a0 The role of Analytics Translator was recently identified by McKinsey<\/u><\/em><\/a> as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.<\/p>\n As our profession of data science has evolved, any number of authors including myself has offered different taxonomies to describe the differences among the different \u2018tribes\u2019 of data scientists.\u00a0 We may disagree on the categories but we agree that we\u2019re not all alike.<\/p>\n Ten years ago, around the time that Hadoop and Big Data went open source there was still a perception that data scientists should be capable of performing every task in the analytics lifecycle.\u00a0<\/p>\n The obvious skills were model creation and deployment, and data blending and munging.\u00a0 Other important skills in this bucket would have included setting up data infrastructure (data lakes, streaming architectures, Big Data NoSQL DBs, etc.).\u00a0 And finally the skills that were just assumed to come with seniority, storytelling (explaining it to executive sponsors), and great project management skills.<\/p>\n Frankly, when I entered the profession, this was true and for the most part, in those early projects, I did indeed do it all.<\/p>\n \u00a0<\/p>\n Data Science \u2013 A Profession of Specialties<\/strong><\/span><\/p>\n It\u2019s fair to say that today nobody expects this.\u00a0 Ours is rapidly becoming a field of specialists, defined by data types (NLP, image, streaming, classic static data), role (data engineer, junior data scientist, senior data scientist), or by use cases (predictive maintenance, inventory forecasting, personalized marketing, fraud detection, chatbot UIs, etc.).\u00a0 These aren\u2019t rigid boundaries and a good data scientist may bridge several of these, but not all.<\/p>\n With about 65% of folks with the title \u2018data scientist\u2019 having entered the profession in the last three years, clearly these new data experts also have learned to slot themselves into roles for which they\u2019re trained and presumably enjoy.<\/p>\n We\u2019ve always had a problem with the consistency of job titles.\u00a0 Everyone still wants to be a \u2018data scientist\u2019.\u00a0 But it\u2019s fair to say that our understanding of different roles has matured just as these roles have proliferated.<\/p>\n The most recent major change in the way that DS roles are defined comes from the growing importance and penetration of analytics in business.\u00a0 Analytics has become a team sport requiring not only data scientists, but also data engineers, IT, LOB managers, and classic analysts (aka citizen data scientists).<\/p>\n If you\u2019ve watched the way analytic platform developers have addressed the market over the last many years, clearly their move was to make more room at the technical data table (data scientists, data engineers, analysts).\u00a0 Now that advanced analytics has become decidedly mainstream among the leaders in all industry segments, the table is getting even larger.<\/p>\n McKinsey with its appropriate focus on business effectiveness and outcomes takes this a step further.\u00a0 By looking at the now large number of businesses with advanced analytics programs, they say that just having a large and inclusive table is not enough.\u00a0 Here\u2019s their most current Venn diagram of analytics roles:<\/p>\n \u00a0 Not unexpectedly they focus on an overall strategy and executive sponsorship.\u00a0 They have also focused on the role of Analytics Translator<\/strong> as a key requirement for success.<\/p>\n \u00a0<\/p>\n What Does an Analytics Translator Do?<\/strong><\/span><\/p>\n In short, the job of Analytics Translator is to:<\/p>\n In other words, translate business problems into data science projects and lead in quantifying the various types of risk and rewards that allow these projects to be prioritized.<\/p>\n This implies that they have certain skills:<\/p>\n \u00a0<\/p>\n Where Do You Find Them?<\/strong><\/span><\/p>\n As a long-time senior data scientist with deep business and domain understanding I would have said look to your senior data scientists or perhaps to outside consultants like myself.\u00a0<\/p>\n Of the skills requirements for the job I still rate the detailed understanding of the underlying data science as the most important.\u00a0 Even if I don\u2019t personally write the code or set up the architecture I\u2019ve led enough projects to know the ins-and-outs in some detail.<\/p>\n Where I differ from McKinsey\u2019s conclusion is their assertion that these Analytics Translators<\/u><\/em><\/a> may come from outside of data science, perhaps from the ranks of existing managers with a focus on business insider knowledge and a track record of successful projects.\u00a0 Their premise is to find a good general LOB manager and train them for the analytics.<\/p>\n McKinsey\u2019s logic is based on their perception, probably correct, of a severe shortage of experienced data scientists.\u00a0 But it\u2019s also based on the premise that it takes longer for an outsider to learn the business than it does for an insider to learn data science.\u00a0<\/p>\n Hmmm.\u00a0 I\u2019d take exception to that second assertion even if you\u2019re not trying to create a hands-on data scientist.<\/p>\n To support their argument they cite \u2018a global steel company\u2019 (unnamed) training 300 translators in a one year program.\u00a0 McKinsey says they\u2019ve also created their own academy and have trained 1,000 translators in the past few years.<\/p>\n I\u2019m generally reticent to create new naming conventions for roles that have been intuitively obvious if not specifically named.\u00a0 In this case though I support companies in evaluating their analytics teams to ensure the Analytics Translator role is filled.<\/p>\n Personally, I\u2019d favor hiring senior data science talent from outside favoring those who have broad project experience over many project types, including those outside of the target industry.\u00a0 Data literacy, or now \u2018analytics literacy\u2019 is great.\u00a0 Formal training of LOB managers in advanced analytics is valuable.\u00a0 I\u2019d still prefer my Analytics Translators to be deeply knowledgeable of the challenges and opportunities unique to each valuable use case, and that means coming from some years doing data science.<\/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 Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist since 2001.\u00a0 He can be reached at:<\/p>\n Bill@Data-Magnum.com<\/a> or<\/span> Bill@DataScienceCentral.com<\/a><\/p>\n \u00a0<\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"![](\"http:\/\/api.ning.com\/files\/Y5oclxjGfk79NjR6Sq1eV4i4s0nDgeaA3rF-LfiliryKp-kTctZenwzHMCAtAdwrJFJBwUql2Z6RS3rEoCFtIOt2fT9ZheNN\/goodtranslator.png\"){kind=spacer}
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