Going from Big Data to AI

December 4, 2018

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.

Track Chairperson
Judith Hurwitz, President and CEO, Hurwitz & Associates

12:00 pm Keynote Exhibit Hall Presentation (Sponsorship Opportunity Available)

11:00 am - 2:00 pm Concession Stand Open for Lunch in the Exhibit Hall

1:15 Presentation to be Announced

1:45 Case Study: Big Data to AI at State Street
An Armchair Interview with State Street 
Randal C. Swanberg, Senior Vice President, State Street Technology Research Group


2:25 PANEL DISCUSSION: Monetizing Data via new Digital Business Models
Moderator:Ramanathan, ShriramShriram Ramanathan, PhD, Senior Analyst, Lux Research




Panelists: Anju Gupta, Head of Sustainability Campaign, Syngenta 

Digital transformation is all about data. While many companies have embarked on their digital transformation journey, few realize that true digital transformation means changing the way a company does business. Importantly, companies must learn to use data to create new revenue streams via new digital business models.

  • Examine the applicability of recent, non-IT industry business models for data monetization
  • What process or framework do you use to create new data monetization business models?
  • Share the broad trends in data-based business models
  • Overcoming challenges in new digital business model creation, including cultural, data siloing, and technology

3:00 Refreshment Break in the Exhibit Hall with Poster Viewing

3:45 PANEL DISCUSSION: Bringing Big Data Benefits to Small Data Operations
Moderator: Judith Hurwitz, President and CEO, Hurwitz & Associates
Panelists: Kashyap Kompella, CEO, RPA2ai and others to be announced

The benefits of Big Data are not exclusive to Fortune 100 organizations. Without massive resources added for data sciences, companies may believe data analytics is out of reach for all but the largest entities. Hear how businesses of all sizes can effectively gather, analyze, and make enterprise data actionable.

  • How much data is enough? Do you have too much data or not enough?
  • How do you to get the data that you need?
  • Is your data it trusted? Cleaned? Unbiased? Governed?

4:15 PANEL DISCUSSION: Enterprise Strategies for Real-Time Data Analysis
Moderator: Judith Hurwitz, President, Hurwitz & Associates
Panelists: Sanjeeva L. Fernando, Vice President and Head of the OptumLabs Center for Applied Data Science (CADS), OptumLabs

Big data applications typically batch data for processing and analysis, but this can be a challenge for some industry applications. An increasing number of enterprise applications rely on fast and timely data analysis to make business-critical decisions. Security and machine monitoring are only a couple examples where real-time data analytics are essential to meet the needs of enterprise applications.

  • Learn to identify when your business needs real-time data.
  • Hear how industries including emergency response, energy, finance, and transportation require real-time analysis for Big Data applications
  • Identify how challenges in data storage, processing, and analysis can be overcome using AI technologies

5:15 - 6:30 Networking Reception in the Exhibit Hall with Poster Viewing