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: 1A 10 Level: Beginner
Secondary topics:  Machine Learning in the enterprise

Who is this presentation for?

(1) academics looking to transition to roles applying scientific methods in a business environment, (2) business professionals looking to expand their analytical skillsets, (3) business non-analysts working with data science teams. Required expertise is minimal; it will be sufficient to have an interest in, and a general understanding of the motivation for, data-driven decision making in a business context.

Prerequisite knowledge

Required expertise is minimal; it will be sufficient to have an interest in, and a general understanding of the motivation for, data-driven decision making in a business context.

Materials or downloads needed in advance


What you'll learn

An approach to set yourself up to successfully solve business problems with advanced analytics. How to define and prioritize valuable business question. Our proven method to deliver insights using an agile approach. How do you define criteria specific to your business to identify valuable business questions How do you frame those questions and support prioritization


This tutorial is a primer on crafting well-conceived data science projects on course toward uncovering valuable business insights. Using case studies and hands-on skills development, we will teach techniques that are essential for a variety of audiences invested in effecting real business change: (1) academics looking to transition to roles applying the scientific method in a business environment, (2) business professionals looking to expand their analytical skillsets, and (3) business non-analysts working with data science teams.

We will start our discussion with case studies demonstrating advanced analytics entry points (the initial impetus for the project). Our case studies were chosen to 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. We will also show you how to avoid missteps that can lead to less than stellar results or wasted effort, with a checklist to follow to get started on the right path from the beginning!

The next portion of the session will 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! Finally, we will demonstrate a pragmatic approach to frame your data driven decision making projects in an agile project methodology. An agile approach 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 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 strategy practices.

Photo of Danielle Leighton

Danielle Leighton


Danielle helps clients approach, design, implement, and integrate new insights and advanced analytics data products that align with their business goals. 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. Currently, she focuses most of her time on data science in the energy sector.

Photo of Lindsay Brin

Lindsay Brin


Lindsay is a motivated, curious, and analytical data scientist with more than a decade of experience with research methods and the scientific process. From generating testable hypotheses, through wrangling imperfect data, to finding insights via analytical models, she excels at asking incisive questions and using data to tell compelling stories.
Lindsay is passionate about teaching the skills necessary to analyze data more efficiently and effectively. Through this work, she has developed and taught workshops and online courses at the University of New Brunswick, and is 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.

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