Kate Lee is a Strategic Analytics Analyst at big-data company IRI which specializes in CPG (consumer packaged goods). At a recent event hosted by data-upskill school, Promotable, she discussed the differences between the roles of data analytics analyst and data scientist. From her perspective, the former is essentially focused on reporting and insights-generation compared to the latter whose foremost concentration is parsing causal relationships to inform predictability. The scale and scope of the data sets collected and examined also varies between the two disciplines. Kate’s elaboration of the distinct differences between these two fields provides a great primer for anyone, data-literate or not, who is curious about them as potential career paths.
Until Kate’s talk, I perceived data analytics and data science as synonymous. Not only was this assumption of mine corrected, she clarified their respective purposes which are jointly vital in helping people and organizations navigate this era of the brutally “new normal.” Though the objectives and focal points are different between data analytics and data science, this statement from Kate’s opener rung true:
“We tell the truth.”This is a claim, a call-to-action and an oath wrapped up in a bite-size proclamation. Whatever is revealed by data is also supported by it—whether the revelation is fancied or not. Especially now, truth and outcomes matter a lot. More than ever, how data is utilized helps make the decision-making process much less shallow.
Although practitioners of data analytics and data science may differ in their roles, they share the same mission: to inform choices.
Thanks again to Promotable who further contextualize their virtual workshops with relevant perspectives through their planning of regular talks online! Explore their channel on YouTube.
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