Entry Points for Advanced Analytics

This session will examine a number of companies in Energy, Health, and Retail whose initial attempts were focused on what “advanced analytics should be” and how each found the right path to meet their needs through focusing on their business drivers instead. An organization’s business drivers will be a strong influence on how and where an organization begins its advanced analytics journey. There is no “one-size-fits-all” when approaching advanced analytics but this common belief often makes for good lessons learned!

Through the lens of these companies, we will discuss successful approaches aligned with business drivers identified as priorities for advanced analytics. Learning specific business drivers and the strategy for advanced analytics that developed from them in a case-study format provides context for the techniques and tools discussed throughout this conference!

Attendees will learn:

What are business drivers, how to identify them and why they are important
How these drivers influence the Advanced Analytics Entry Point
Signs that you may be on the wrong path – heading for lessons learned – and how to adapt
Why advanced analytics is different than traditional analytics (strategy, expectations for ROI, timeline, resources)
Signs that you are heading for success

Janet Forbes is an experienced Enterprise, Business and Senior Systems Architect with deep understanding of data, functional and technical architecture and proven ability to define, audit and improve business processes based on best practices. She has extensive experience in leading multi-functional teams through the planning and delivery of complex solutions. With over 25 years of experience in various roles and organizations, Janet has proven capability in enterprise, functional and technical architecture with specific focus on Business and Data Architecture. As a trusted advisor, Janet works closely with clients in assessing and shaping their data governance practices.

Danielle Leighton works at the centre of data governance and data science, applying research methods, best practices, and academic algorithms to industry business needs. Her professional background includes healthcare, academia, government, retail, gaming, and energy. She has a proven record working with clients whose teams and data are diverse, geographically distributed, and, well, big! Danielle excels at helping clients identify testable hypotheses about their business that have real impact while addressing data issues and data governance processes to support reliable data-driven models. With a strong background in machine learning, Danielle identifies the math, visualizations, and the business questions and processes necessary to create reliable predictive models and, ultimately, good, data driven business guidance.