Join the Analytics Architecture & Technology (AAT) Community  for an exciting panel discussion on "Enabling the Citizen Data Scientist with Next-Generation Data Science Technologies". This session on May 3, 2021 is part two from a panel discussion that took place back in September 2020 on “The Next Generation of Analytics Tools”. During September 2020, we covered many topics at a high-level and the members of the community requested that we take a deeper dive on some of the topics discussed in the future. First up on that deeper dive, our utility analytics experts will share their perspectives and insight on "Enabling the Citizen Data Scientist with Next-Generation Data Science Technologies".

To level set, what does ‘citizen data scientist’ mean? We have learned that “citizen data scientist” is a term initiated by Gartner. Gartner defines it as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” In a nutshell, they are non-technical employees who can use data science tools and technologies to solve business problems. Citizen data scientists can provide business and industry area of expertise that many data science experts lack. Their business experience and awareness of business priorities enable them to effectively integrate data science and machine learning output into business processes.

With that said, our panel of experts will answer questions like:

• Can we successfully convert non-technical employees into data scientists by leveraging and combining their area of expertise with data science technology to solve business problems? How?
• How do you ensure Artificial Intelligence and Machine Learning becomes more accessible to the average data analyst/citizen data scientist?
• In your opinion, what are the factors that are beginning to democratize data science through data science technology?
• How do I get started with democratizing data science through data science technology, especially using what I’ve learned in the past combined with what we are doing today to properly unleash our future vision of enabling the citizen data scientist?
• When acquiring and implementing your analytics and data science technology stack, what are your considerations to ensure success for your average data analysts/citizen data scientists?
• There are many tools’ promises to empower citizen data scientists. What are THE must have innovative tools for the next generation of citizen data scientists?
• How do I balance the different technologies used by Citizen Data Scientists?
• I’ve got the capability, i.e. the data science technology, the right staff, trained at the right level, so how do I unleash it and use it at scale to add value to the business?
• How do I market the concept of democratization of data science through data science technology to my organization? What are my change management considerations? And how best do I communicate it to the business once I get buy-in?
• Most of us know that data wrangling is a big part of any analyst’s job. How do we best educate our “citizen data scientists” in data wrangling (data management, data tools, simple queries, data quality, etc.)?
• How do we best connect our “citizen data scientists” with the right tools for their level of interest, ability, expertise, and business need? For instance, do they just need to learn how to use a pre-trained image recognition model, or do they need to learn how to build their own Convolutional Neural Network in TensorFlow? How do we best match tool to talent?  
• I was wildly successful at selling the promise of Data Science inside my company and the demand quickly outstripped our ability to supply. How do I best manage the influx of demand?
• The data scientist skillset includes the understanding of databases and data warehouses. Data scientists are responsible for setting up appropriate data marts that provide users with less complex queries that do not bring down the system, as well as maintaining administration over permissions and security is a building block to establishing self-service analytics. With that said, do we allow "citizen data scientists" to set-up models that pull data from source systems on-demand, which could bring a halt to systems, especially for smaller utilities with more limitations?