Identify Citizen Data Scientists and Provide the Support They Need to Succeed
When your business decides to begin a Citizen Data Scientist initiative, it faces numerous challenges but the resulting benefits the enterprise will reap from this program are undeniable. With the right preparation, every business can successfully enable this type of initiative and provide augmented analytics to improve collaboration, data literacy and digital transformation in the organization.
When a business chooses to follow the guidance of Gartner and embrace the Citizen Data Scientist initiative to transform business users and provide analytical tools for all team members, it must approach the initiative with appropriate planning.
Citizen Data Scientists…the predicted future of the organization! Gartner and other technology research firms have predicted the rise of Citizen Data Scientists as a way to complement and optimize data scientists and to democratize data and improve data literacy across the enterprise.
When combined, Digital Transformation and Data Literacy provide a rich and profound value to the organization. A properly structured initiative will prepare the business for an ever-changing market and business environment and make the business and its team members agile, nimble, and capable of responding to change. The initiative, once employed, will provide many benefits to offset the efforts involved in completing the initiative.
Download the Whitepaper: Preparing Your Business for Digital Transformation (Dx) and Data Literacy
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.
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.
Whether a business addresses or ignores the Digital Transformation movement, it is here to stay and Dx will change markets and competition. If a business truly wants to embrace Dx, it must be willing to take the necessary steps and engage in a review that touches all corners of the organization. To plan and execute a comprehensive, effective Digital Transformation (Dx) project, the business must consider five points of integration including Technology including Infrastructure, Software, Hardware, Network, Mobility, Enterprise Culture including Process and Analytics, Team Members, Suppliers, Partners, Vendors and Stakeholders, Customers.
Download the Whitepaper: Digital Transformation Requires Commitment and Five Points of Integration
It is a great idea to get your business users involved in analytics and to encourage and build data literacy in your enterprise but one of the things that often stops an organization from implementing self-serve analytics is the need to gather and prepare the data for analytics. How can IT or data scientist staff turn over day-to-day analytics when data is spread across the enterprise and it is so difficult to integrate that data and prepare the data for analytics?
