Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

From theory to data product: Applying data science methods to effect business change

1:30pm–5:00pm Tuesday, 09/11/2018
Data-driven business management, Strata Business Summit
Location: 1E 15/16 Level: Beginner
Secondary topics:  Machine Learning in the enterprise
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Who is this presentation for?

  • Academics looking to transition to roles applying scientific methods in a business environment and business professionals looking to expand their analytical skillsets or working with data science teams

Prerequisite knowledge

  • A general understanding of data-driven decision making in a business context

What you'll learn

  • Learn an approach to set yourself up to successfully solve business problems with advanced analytics and a proven method to deliver insights using an Agile approach
  • Understand how to define and prioritize valuable business questions, define criteria specific to your business to identify valuable business questions, and frame those questions and support prioritization


Janet Forbes, Danielle Leighton, and Lindsay Brin lead a primer on crafting well-conceived data science projects that uncover valuable business insights. Using case studies and hands-on skills development, Janet, Danielle, and Lindsay walk you through essential techniques for effecting real business change.

Janet, Danielle, and Lindsay begin with case studies that demonstrate how a project’s entry point impacts its scope and approach and how that can diverge from the critical business drivers that ultimately measure successful data science projects. They also show you how to avoid missteps that can lead to less than stellar results or wasted effort and share a checklist to follow to get started on the right path from the beginning.

Janet, Danielle, and Lindsay outline a framework to help you define, refine, and assess value for business questions that are candidates for data science projects. Many organizations struggle with identifying and prioritizing these questions, but this step is critical to ensure your project teams are focused on the right work. They then detail a pragmatic approach to frame your data-driven decision-making projects with an Agile project methodology, which lets the project team quickly adapt, based on findings, as the project progresses. This framework helps to manage uncertainty while ensuring the project is focused on constant progress toward a stated goal.

Photo of Janet Forbes

Janet Forbes


Janet Forbes is an experienced enterprise, business, and senior systems architect at T4G. With over 25 years of experience, Janet has a deep understanding of data and functional and technical architecture, with specific focus on business and data architecture, a proven ability to define, audit, and improve business processes based on best practices, and extensive experience leading multifunctional teams through the planning and delivery of complex solutions. As a trusted advisor, Janet works closely with clients in assessing and shaping their data strategy practices.

Photo of Danielle Leighton

Danielle Leighton


Danielle Leighton is director of data science at T4G, where she helps clients approach, design, implement, and integrate new insights and advanced analytics data products that align with their business goals. She currently focuses most of her time on data science in the energy sector. She’s passionate about keeping data in context and applying research methods, best practices, and academic algorithms to industry business needs. 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. Danielle has worked in healthcare, academia, government, retail, gaming, and energy and with quantified selfers, biohackers, hacklabs, and makerspaces. She is notoriously unreadable to GSR wearables. In her previous life, Danielle worked with the world’s most sophisticated wearable to date, the hearing aid.

Photo of Lindsay Brin

Lindsay Brin


Lindsay Brin is a data scientist at T4G. A motivated, curious, and analytical data scientist with more than a decade of experience with research methods and the scientific process, Lindsay excels at asking incisive questions and using data to tell compelling stories, from generating testable hypotheses to wrangling imperfect data to finding insights via analytical models. Lindsay is passionate about teaching the skills necessary to analyze data more efficiently and effectively and has developed and taught workshops and online courses at the University of New Brunswick. She is also a Data Carpentry instructor and Ladies Learning Code chapter co-lead. Having recently made a career pivot from biogeochemistry to data science, she is also well-positioned to provide insight into the applicability of academic research and analysis skills to business problems.