When you take on the mantle of Citizen Data Scientist, there is a lot to process! Much of the reason business users feel overwhelmed at the idea of the Citizen Data Scientist role is the anticipation of having to learn new tools and techniques to analyze data.<\/p>\n \u00a0<\/p>\n \u201cI am not a data scientist\u201d, \u201cI don\u2019t understand analytics\u201d, \u201cthis new role is going to take up all my time\u201d, \u201cmy manager doesn\u2019t care about this stuff\u201d \u2013 these are just a few of the arguments you will hear when you bring up the concept of Citizen Data Scientists, improved data literacy and data democratization. But, in the real world, the introduction of a Citizen Data Scientist initiative should not be intimidating or overwhelming.<\/p>\n \u00a0<\/p>\n There are many benefits to the Citizen Data Scientist evolution. Here are just a few:<\/p>\n \u00a0<\/p>\n All of these benefits depend on the organization being prepared to change its culture and to support Citizen Data Scientists as they take on their new role. One of the most important aspects of this change requires establishing a foundation of augmented analytics solution and providing easy, intuitive methods to access and perform analytics without the need for data scientist or business analyst skills.<\/p>\n \u00a0<\/p>\n With the right self-serve augmented analytics, Citizen Data Scientists can leverage auto-recommendations, suggestions and guidance to choose the right algorithm or analytical technique and visualization techniques to receive clear results suitable for presentation and reporting.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
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