Preparing Big Data for Automation and Monetization

With data in-hand, machine learning aids in everything from cleaning datasets to managing multiple data sources to synthesizing data. With the help of machine learning, data can now be monetized. This track identifies key business strategies for data monetization and steps to be taken to maximize the impact of AI technology interaction with Big Data.

Friday, October 25

Waterfront 2

7:45 am
Registration Opens

8:00 Continental Breakfast (Harborview Foyer)

8:15 am – 12:30 pm Keynote Session (Harborview)

12:30 Networking, Coffee & Dessert in the Expo – Last Chance for Viewing (Commonwealth Hall)

1:45 Opening Remarks: Preparing Big Data for Automation and Monetization

Hurwitz_JudithJudith Hurwitz, President, Hurwitz & Associates


There is tremendous excitement and anticipation about the business value of AI and machine learning. While much of the focus has been on algorithms and models, the most important issue that makes AI valuable to business is how well the data is managed for applicability for AI. 

Are you focused on the data that will drive your AI strategy? If data is an afterthought, you will cause havoc in your organization. You need to plan: Are you focused on the business problem you are intending to solve, and the data needed to ensure that your models are accurate? What is the source of the data and is it accurate? Is there sufficient data to ensure that the model produces desired results? Has the model been trained with representative data? These are some of the questions that you will need answers to in order to be able to monetize AI and support your business goals. 

2:10 Panel: Talk Title to be Announced

Hurwitz_JudithModerator: Judith Hurwitz, President, Hurwitz & Associates

Harding_ChrisPanelist: Chris Harding, Commissioner Massachusetts Department of Revenue, Commonwealth of Massachusetts

Justin_WilfredWilfred Justin, Head, AWS AI/ML Evangelism and Partnerships, Amazon (AWS)  

Davis_SteveSteve Davis, Sr. Vice President, Data Strategy and Cross-Market Offerings, Optum 


2:40 PANEL: Chief Data Officers Speak Out on Monetizing Big Data with AI

In the age of data analytics, leaders are regularly asked to justify the business impact of investing in advanced analytics and AI projects. Our panel will discuss their experiences in leading an organization through data, analytics, and AI disruption and innovation.  The panel will provide attendees with deep insights and best practices that will help them learn from experienced executive level practitioners.  Learn from these leaders how they are navigating the transformative times and are using AI to build not only core competencies but also create sustainable growth. 

Attendees will learn: 

  • What it takes to build and lead an enterprise data analytics practice with a focus on AI,
  • What are today’s best practices, including benchmarks, tools and technologies that enterprises should focus on 
    Specific tactical steps tomorrow’s leader could use to build a healthy AI analytics practice,
  • How to recruit and develop AI core competency in your organization.


Kumar_VishalModerator: Vishal Kumar, CEO and President, AnalyticsWeek

Santikary_PrakriteswarPanelists: Prakriteswar Santikary, PhD, Vice President and Global Chief Data Officer, ERT 

Trivedi_PawanPawan Trivedi, Principal Consultant, Services Transformation Group, Atos Syntel

Kierner_SlawekSlawek Kierner, Senior Vice President | Chief Data and Analytics Officer, Humana 

Harper_KyleKyle Harper, AI Strategist, Dell Technologies

3:10 Networking Break (Plaza & Harbor Level Atriums)

3:25 Digital Transformation through Data-Driven Revenue Strategies

Schneider_LynneLynne Schneider, Research Director, Data as a Service, IDC 

3:55 PANEL: The Data Preparation Cycle from Exploration to Acquisition to Feature Engineering

Big Data is proving its value in many application areas. But what can you do when you don’t have a large data set? Or when you have a large amount of data, but not much labeled data in the domain of the prediction you need to model? This panel considers the potential of data exploration, feature selection/creation, and un-supervised machine learning to deliver value when relevant labeled data is scarce.

Morris_HenryModerator: Henry Morris, PhD, Principal, Henry Morris Analytics

Kumar_SureshPanelists: Suresh Kumar, Head of AI Solutions, PARC

Kirby_MaxMax Kirby, Director of Cloud Platform Solutions, Publicis Sapient/Google 

Cordero_PaulPaul Cordero, Director of Product Sales, Cyber and Intelligence Solutions (CAI), Brighterion 

Jim Balchunas, Senior Consultant, Credit Suisse 

4:45 Close of AI World 2019