“Citizen Data Scientist,” a term coined by Gartner, refers to an advanced data analyst or data engineer who can use machine learning technology to drive deeper analytical insights, identify trends and make predictions. Organizations of all sizes are becoming more data-driven but have struggled with the shortage of trained data scientists. To mitigate this supply problem innovative organizations are empowering their data analytics professionals and subject matter experts with the tools and support they need to become Citizen Data Scientists.

Citizen data scientists typically reside within the data analytics organization or within a business unit like sales, marketing, or finance. These individuals possess deep domain knowledge of the business and the challenges their business units face.  Armed with powerful open source and commercial products, they can perform deep dive diagnostic analysis and create machine learning models that supplement the work typically requiring a data scientist or statistician.

In this presentation we will examine the low code/no-code tools and tricks of the Citizen Data Science trade. We will discuss how Citizen Data Scientists work in tandem with professional data scientists on projects that require detailed business expertise demonstrating how this allows the traditionally educated data scientist to focus on more complex projects that impacting the enterprise.

This frustration leads to low success and high dissatisfaction rates with your customers, meaning users won’t want to use your chat solution in the future. This directly impact opinions about your company’s support as well as increases Tier-0 staffing costs and frustration.

Who wants to use keywords to trigger a set of rules, when you could converse in full sentences and have the system understand you, show empathy and crack a joke once and a while.

We’ll talk about how to build a more human-centric conversational interface to boost your satisfaction and hopefully your deflection rates, saving your Tier-0 support time and money.