Analytics Translator? Citizen Data Scientist? What is the Difference?

Analytics Translator? Citizen Data Scientist? What is the Difference?
There is a new business role on the horizon and, at first glance, it may seem very much like a role that was introduced a few short years ago. This new enterprise role is known as an 'Analytics Translator' and, while there is some confusion regarding the distinction between this role and the newly minted Citizen Data Scientist or Citizen Analyst, there are some subtle but important differences. In a previous article (What is an Analytics Translator and Why is the Role Important to Your Organization?), we discussed the definition of an Analytics Translator. Here, we will discuss the role of Citizen Data Scientist and Analytics Translator and how they differ. To understand these roles, let's look first at the somewhat more familiar role of Citizen Data Scientist (AKA Citizen Analyst).
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What is an Analytics Translator and Why is the Role Important to Your Organization?

What is an Analytics Translator and Why is the Role Important to Your Organization?
Today, enterprises recognize the critical value of advanced analytics within the organization and they are implementing data democratization initiatives. As these initiatives evolve, new roles emerge in the organization. The newest of these analysis-related roles is the 'analytics translator'. As the enterprise considers the relevance of this new role within the business, it is important to understand the responsibilities of an Analytics Translator, and how this role might help the organization to achieve its goals.
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Do Citizen Data Scientists Mark the Death of Data Scientists?

Do Citizen Data Scientists Mark the Death of Data Scientists?
To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. With the democratization of business intelligence and the advent of self-serve business intelligence tools, organizations hope to encourage and create Citizen Data Scientists and enable the average business user to leverage sophisticated predictive algorithms and BI tools without the expertise and skill of a trained data scientist. This approach allows users who are not statisticians, data scientists, programmers or analysts to leverage self-service tools to confidently make business decisions, share data, and reports and present data in a way that allows the organization to identify trends, patterns, opportunities and challenges.
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