Going from Big Data to AI

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.

Tuesday, December 4, 2018

7:45 am Registration Open

8:20 am - 12:00 pm AI World Plenary Session - View Details

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


Track Chair

Hurwitz_JudithJudith Hurwitz, President and CEO, Hurwitz & Associates

1:15 Winning Blueprint for the Intelligent Enterprise

Davis_BoydBoyd Davis, Managing Director, Applied Intelligence, Accenture

Several years into the AI hype cycle, most organizations are still struggling to create value from the application of AI to their enterprise. Key challenges include people and processes, technology limitations, and most of all, defining a clear and compelling business case. Through extensive research and engagements with clients around the world, Accenture has identified 3 themes that will guide organizations toward success with AI. These include:

  • Future Workforce, targeted to address talent and organizational challenges
  • Digital Decoupling, designed to create an adaptable technology foundation
  • Living Business, focused on creating value through new products and services to adapt to rapidly changing customer requirements

Boyd Davis, formerly CEO of Kogentix and now part of Accenture’s Applied Intelligence practice will introduce these themes and provide practical guidance for organizations seeking to leverage AI for business advantage.

1:45 Case Study: Big Data to AI at State Street - An Armchair Interview with State Street

Kinlaw_WilliamWilliam Kinlaw, Senior Managing Director, State Street

Data, data sets and analytic models are all human creations. Even when you look at machine generated data, humans decide what data to capture and what to ignore. To gain value from data, You must be able to have the assurance that the data source is trusted. For both your own data and third-party data you must understand the lineage and biases in the data and the analysis. You also need assurance that the data is safe.

  • How to identify hidden biases in data
  • Understanding the lineage of data
  • Learn best practices and how to interrogate data to ensure that it can be trusted
  • Create a situation where you know your data is safe and secure

2:15 PANEL: Monetizing Data via New Digital Business Models

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

Ramanathan, ShriramModerator: Shriram Ramanathan, PhD, Senior Analyst, Lux Research

Gupta_AnjuPanelists: Anju Gupta, Head of Sustainability Campaign, Syngenta

Menoud_LaurieLaurie Menoud, Investment Manager, BASF Venture Capital

Jamison_SethSeth Jamison, Principal FlashBlade Engineer, Pure Storage

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

3:45 PANEL: Bringing Big Data Benefits to Small Data Operations

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?

Morris_HenryHenry Morris, PhD, former Senior Vice President, Software and Services Research, IDC; Consultant, Analytics, Henry Morris Analytics

Kompella_KashyapKashyap Kompella, CEO, RPA2ai

Colarusso_DavidDavid Colarusso, PhD, Professor, Suffolk University

Strang_NicoleNicole Strang, Senior Manager, Data Science, Wayfair

4:25 PANEL: Enterprise Strategies for Real-Time Data Analysis

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

Hurwitz_JudithJudith Hurwitz, President, Hurwitz & Associates

Fernando_SanjeevaPanelists: Sanji Fernando, VP, OptumLabs

Sweenor_DavidDavid Sweenor, Global Analytics & Industry Marketing Leader, TIBCO Software, Inc.


5:10 - 5:30 Plenary Keynote Presentation - View Details

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