2020 Day 2 Agenda
Creating an Architecture for Analytics and AI
- Discussing the ecosystem of data warehouses/lakes/the cloud, what is needed to build an effective analytics environment.
- How do you build the right architecture to handle the new analytics landscape whilst remaining agile and adaptive?
- Do you have access and control of the data you need?
Vice President of Data, Applications & Analytics
10:30AM - Day 2
Presentation: Deere’s Submission Focus: Building resilience on the farm with AI
- Lane Arthur of John Deere will discuss how AI helps build resilience on the farm. Specifically, he will discuss the following topics:
- The challenges that agriculture is facing today and how technology can help.
- How AI & AI-enabled equipment is taking the burden off farmers.
- How AI and big data is utilized at all stages of the farming cycle.
- How other industries can follow the agriculture industry in utilizing AI and data in their day-to-day operations.
. Lane Arthur, Vice President of Data, Applications & Analytics , John Deere
Senior Director – Technology
11:00AM - Day 2
Presentation: Big Data Architecture that Supports Seamless Microservices Integration
- Use cases summary where OLTP and Analytics need to work on same data
- Cloud Independent Big Data Architecture that support OLTP and Analytics Processing on same data
- AWS based Serverless Big Data Architecture that supports OLTP and Analytics Processing on same data
. Vipindas Koova, Senior Director – Technology, Nitor Infotech
President & CEO
11:20AM - Day 2
11:20AM - Day 1
Presentation: How to make sense of no sense data?
- In today’s world anyone can create any data from anywhere on any device. While this is going to grow even further with more and more business strategy shifts and technology sophistication, we are left with no choice other than to deal with lots and lots of data.
- Traditionally we have been forced to use big data platforms, analytics but does that really help? Are the leadership able to trust their own data within their organization? Are the data engineers able to steer and proactively manage their data platforms? Are the data scientist who are supposed to build models and refining algorithms able to focus just on those objectives?
- Or are we just mindlessly building data lakes and clusters with the hope one day we will make some sense of the data we have. This topic is for those who are brave enough to roll up their sleeves and willing to admit enough is enough, and try new approaches to solve the age old question of how to make sense of our own data?
. Raj Joseph, President & CEO, DQLabs