The mechanics of collecting the data, which algorithm is the best-fit, and even deriving insights are all important. But the greatest business value from big data, analytics, and AI comes from acting upon it. Decision-making requires modeling both the
data and the people making the decision. Achieving desired outcomes and the ability to act upon the data matters most when operationalizing enterprise big data with AI.
Thursday, October 24
7:45 am Registration Opens
8:00 Continental Breakfast (Harborview Foyer)
9:00 – 12:25 pm Keynote Session (Harborview)
12:25 pm Networking, Coffee & Dessert in the Expo (Commonwealth Hall)
1:30 pm Opening Remarks
Dan Vesset, Group Vice President, Analytics and Information Management, IDC
1:35 The Modern AI Pipeline: Moving From Data to Value
Ben Solari, Vice President, Inside Sales, DataRobot
There are many barriers organizations face in operationalizing their AI models and actually monetizing all of the data assets that they have spent many years and dollars building. By breaking down the AI Lifecycle and carefully mapping out all of
the barriers that organizations face as they move from data to value, we can better prepare to get ahead of the road blocks.
2:05 Operationalizing Decision Making In the Era of Big Data and AI
Dan Vesset, Group Vice President,
Analytics and Information Management, IDC
Who will analyze the data? Who will make the decision? Who will execute an action? These are three critical questions facing every enterprise as it re-evaluates the relationship between humans and machines in the context of data driven decision making.
In this session, we'll explore challenges facing enterprises in their quest to infuse more AI into decision making processes and frameworks for addressing these challenges.
2:25 Using an Intent Graph to Understand Future Customers
Sumeet Singh, Vice President of Big Data, AI & Location Platforms, Verizon Media
The adoption of fractured consumer engagement touchpoints over the past decade – from hopeful email campaigns to meaningless social media “likes” – has left marketers with a chaotic view of consumer intent. The intent graph gathers
sentiment across numerous touchpoints to build a personalized identity of each customer. Machine learning further enables analysis to scale across hundreds of thousands and even millions of users and anticipate future needs.
2:45 PANEL: Intelligent Automation with RPA
All industries face unprecedented operating challenges as they manage mounting budget constraints while trying to become more agile and increase business objectives. Unable, in many cases, to hire more employees, enterprise leaders are forced to spend
dollars on contractor support or shift resources away from strategic work to handle routine, manual tasks. Robotic process automation (RPA) provides businesses the capability to operate more efficiently with reduced resources. Furthermore, RPA is
moving from the back office to the front and empowering knowledge workers. Hear from thought leaders and subject matter experts who will discuss leading use cases for intelligent automation and its advantages over machine learning approaches.
Kashyap Kompella, CEO and Chief Analyst, rpa2ai Research
Lawrence Lee, Vice President, Incubation and Innovation Strategy, Xerox
Business Process Integration Manager, Stant Corporation
Elif Tutuk, Associate Vice President, Qlik Research
3:20 Networking Break in the Expo (Commonwealth Hall)
4:05 Predicting and Prosecuting Crime in Rio de Janeiro: A Machine Learning Story
Carvalho Belchior, Senior Tech Lead Big Data, Public Prosecutor’s Office, City of Rio de Janeiro, Brazil
The role of public prosecutor is different in Brazil than in other countries. However, the application and workforce benefits of machine learning are the same in many countries and several industries. Important techniques along this journey include
data discovery, selecting the right data, and understanding its value. Even in a constantly changing regulatory environment, planning with learning systems remains possible. In this talk, you will learn:
- How big data and machine learning help the Prosecutor’s Office in predicting crime
- The importance of asking the right questions of the data
- What this AI experience can teach others in law enforcement, the legal sector, and other industries
4:35 Machine Learning in Practice: Anomaly Detection for Army ERP Data
In this session, we’ll review a machine learning case study for the US Army. During this project, the team set out to evaluate the potential of machine learning to improve operational data quality, and thereby increase Army readiness. A progression
of analysis and machine learning approaches were leveraged to better understand problematic datasets within the Army’s ERP environment, identify and classify anomalies, and ultimately provide a path to resolution.
Cashorali, CEO, TCB Analytics
Donnie Horner, PhD, Vice President, Leader Development & Organizational Performance, Higher Echelon, Inc.
5:05 Networking Reception in the Expo (Commonwealth Hall)
6:30 Meetup Groups (Cityview)
7:30 Close of Day 2