August 14-15 | 
Boston, MA

Day One
Tuesday, August 14 2018

Day Two
Wednesday, August 15 2018

Chair’s Opening Remarks

  • Anne Buff Board Director, Data Governance Professionals Organization

Impact, Measurement & ROI

Keynote – Ethics, Data Ownership & Privacy in Data Science

  • Anne Buff Board Director, Data Governance Professionals Organization


• Delivering transparency – what do users expect?
• Managing accountability within systems of multiple decision-makers
• Building fairness into the system to overcome bias, discrimination, diversity
• Addressing privacy and appropriate use of data


Business Use Case – Beyond Efficiency Savings: Utilizing Data Science to Drive New Product Development


This session spotlights Forbes’ AI-powered publishing platform, Bertie. Bertie learns the writing styles and preferences of each author, suggesting editorial
changes that improve the article’s quality, shareability, and performance. Learn how the creation of AI-powered agents can add value to business by fostering human and machine collaboration.



Panel Discussion – Measuring Value & ROI of Data Science


•When does the ROI conversation become a reality? From start-up to scale-up
• What are the range of KPIs and measures of value being used across various businesses?
• Establishing ways to measure the impact, effectiveness and ROI compared to your previous environment
• Demonstrating value in new ways that the business had not previously considered – as an outcome of data science
• Communicating and selling value internally – hard and soft approaches


Morning Refreshments & Networking



This session will share practical applications of data science and will split groups by use cases:

• Supply Chain
• Marketing/Customer
• Pricing
• Product Development
• Risk & Volatility Management



Data & Technology

Business Use Case – Beyond “Data Science”: From Product Development to Demand Management


Hear cutting edge applications of how Data Science is being utilized at Plated, a recipe meal kit service, to add new value to customers. Gain insights
into its developed and enriched new customer recommendation systems,segmentation models, utilization profiles, and palate preferences. Explore
tangible results that demonstrate how this new innovative data science initiative has enhanced customer engagement, improved retention and
lowered costs.


Panel Discussion – Evolving Data Science Infrastructure & Technology Requirements


• Scaling infrastructure as the team grows: how do you build capacity in the data science function?
• Strategies to identifying appropriate technology and measuring the efficacy of your data science tools
• Top down vs bottom up approaches to building data science infrastructure: how will this involve other functions in the business?
• Reviewing technology development from within the team vs outsourcing software and tools


Leadership Story – From Design to Implementation: Engaging Senior Executives in Data Science


Understanding how the data science leader at Adobe has overcome challenges working with senior executives who strongly believe in their own experiential learnings over other forms of data-driven decision-making approaches. Learn some techniques and strategies for engaging these key executives to successfully deliver projects after the design work is complete.



Afternoon Refreshments & Networking

Business Use Case – Uber’s Intelligent Insights Assistant


Hear an exciting case study on how to drive a team of developers and ML engineers to deliver an end to end deep learning based product. Understand how Uber’s Intelligent Insights Assistant has been developed to have highly accurate forecasts, anomaly detection, experimentation and intelligent exploratory data analysis at a click of a button. You will discover new business value being driven from these scalable platforms and tools such as faster innovation cycles and more accurate insights.


Leadership Story – Embedding a Cross-Organizational Data Science Team


Hear how a Chief Data Scientist has integrated data science into the DNA of a business by embedding data scientists in teams across the company, allowing stakeholders to work directly with data scientists. Explore the advantages and challenges of a matrix management model, and how non-technical employees can be encouraged to collaborate with a data science initiative.


Data Science 2019 Onwards: Trends, Predictions & Disruption


• How can businesses learn from each other and apply use cases going forward?
• Reviewing the trends and disruptions in data science that will occur in 2019
• Beyond deep learning: what new technologies are expected to come more widely available for data science and what do early adopters have to say about them?



Chair Closing Remarks

  • Anne Buff Board Director, Data Governance Professionals Organization

Conference Close