The fourth webinar in the “Network Analysis for Business Performance” series explores specific ways that predictive analytics can increase the impact of organizational network data on business results, including employee engagement, innovation, turnover, and the management of diversity and inclusion. Practical examples are provided to demonstrate how network data can be used to test ideas and generate actionable findings. The session provides learners with step-by-step instruction on how to conduct two of the most common types of inferential tests, as well as an overview of advanced predictive tools in Ucinet.
Featuring: Inga Carboni, William & Mary School of Business
OVERALL PROGRAM DESCRIPTION
Network analytics are fast becoming an essential competency for organizations that want to understand how their informal networks function and manage them to drive change, fuel innovation and develop talent. This series of six webinars will provide you with foundational skills in designing, conducting and managing organizational network analyses. Instructors will share models, techniques, and success factors honed from years of experience across a wide range of practical, business-driven applications.
The sessions include how to manage an ONA to maximize business impact; how to choose the best approach for data capture from both survey and social media-based options; designing effective instruments for data capture; generating visual and quantitative analytics; running predictive analytics from network data, and communicating network insights to stakeholders. Also included is step-by-instruction on how to use three of the leading network analysis software packages: UciNet, NetDraw, and NodeXL.
Who should attend:
- Professionals in People Analytics, Human Resources, or Knowledge Management interested in gaining hands-on capability to conduct all or part of ONA studies.
- Professionals with responsibility for overseeing ONA projects who want more in-depth understanding of the process.
- Managers interested in exploring network data on their own.