Six Strategies for Data-Driven Decision Making - How to create excellence through relevant, contextual insights
Webinar Speaker: David Stodder, Senior Director of Research for BI, TDWI
Date: Wednesday, July 24, 2019
Time: 9:00 a.m. PT, 12:00 p.m. ET
Despite advances in self-service BI and analytics, most users are unable to gain insights from data and apply them in the context of day-to-day decision making. According to industry research, this situation is particularly burdensome for the average of 70% or more of users in organizations who do not have full-fledged BI and analytics tools or applications at their disposal. Often working as managers and frontline personnel in operations, they have too little time, skills, and interest to learn to use standalone BI and analytics tools to get the critical, relevant information and analytics insights they need to solve real-time business problems and serve customers effectively.
How can organizations push beyond traditional limitations in making data insights available to more — if not all — of their users? What new technology options can help drive decision-making excellence?
Join this TDWI Webinar to learn how you can make it easier for your organization to deliver relevant data insights and enable data interaction for a wider range of decision makers. Speakers will discuss how organizations can address user needs for contextual data insights proactively in the flow of their work rather in the traditional passive mode where systems wait for users to search for answers with standalone tools and applications.
Topics to be covered include::
- Innovations in AI, alerting, and notification that can enable organizations to proactively deliver the right answers to the right users
- How to improve operational decision making by providing contextually relevant information within user workflows
- Ways to use mobile and emerging smart interactive devices such as voice-controlled assistants and augmented reality to support data-driven decision making
- Tips for addressing human factors such as business problem definition and how analytics are operationalized
- How to ensure user trust in data answers and adherence to data governance policies