When a business takes on the task of transforming business users to Citizen Data Scientists, they might have very clear goals mind but they may not have a firm grasp on what the role means or how to provide the business user with an understanding of what the role will be (what it will not be) and how it will change their day-to-day work.
As a newly minted Citizen Data Scientist, you may feel overwhelmed and uncertain how to proceed. As with any other transition and new responsibility, the first order of business is to understand what is expected of you and what the boundaries and obligations of your new responsibilities will dictate. It is also important to learn how to use any new tools that may be important to your new role.
When a business user takes on the new role of Citizen Data Scientist, it is often with misgivings and concerns. If their business and management team have not adequately explained the expectations of this new role, or made the workflow and cultural changes required to ensure that this new role is successful, the business user and team member is likely to fail, and the Citizen Data Scientist initiative is likely to fail.
Gartner has rightfully predicted the rise of Citizen Data Scientists. Coming from the ranks of business users, Citizen Data Scientists continue to function in their primary roles and fulfill their responsibilities as they take on the tasks of aligning with Digital Transformation initiatives and improving data literacy. Their new role does require that they embrace some new concepts and become comfortable with analytics and with making decisions using augmented analytical tools.
When a business initiates a Citizen Data Scientist movement within its ranks, it is important to recognize the other changes that will be required in order to reinforce the value of the program by reviewing and modifying team member evaluation and merit programs to reward Citizen Data Scientists for their collaboration, use of data-driven information in decisions and recommendations and work with Data Scientists, IT and others involved in the new initiative.
What is Augmented Analytics and Can it Benefit My Business?
What is the difference between analytics and augmented analytics? The simple answer is this: augmented analytics provide sophisticated analytics tools in an easy-to-use environment that offers guidance and recommendations, and a natural language processing (NLP) interface that makes it possible for every business user to leverage the solution, no matter their technical or analytical skills or experience.
An advanced analytics solution that is designed for augmented analytics, must be easy enough for the average business user to achieve results without IT skills or the skills of a data scientist. When a business user can leverage clickless analytics search capability, the user can achieve rapid, clear results and use those results to solve problems, share information and optimize opportunities for the business.
When a business is considering an advanced analytics solution, it must sell its management team and investors on the value and benefit of that solution and the merits of implementing such a solution or upgrading an existing solution.
Smarten is pleased to announce the introduction of its eLearning Workshop, ‘Be a Citizen Data Scientist – Aspire, Inspire, Become‘. This online course is designed to provide a solid foundation for one’s business and, more importantly, for those members of the team who may be considering as early adopters or champions of the Citizen Data Scientist transformation within their organization.
What is a Citizen Data Scientist and what is the role of a Citizen Data Scientist? It isn’t as complicated as you might think. A Citizen Data Scientist is a business user working in a professional capacity within an enterprise – one who is collaborative and curious by nature and willing and ready to adopt augmented analytics tools to prepare and analyze data and use that data to gain insight into business issues and opportunities. The role of a Citizen Data Scientist is to interact with and liaise with IT and data scientists to define use cases and refine data output to be used in making fact-based decisions.
