A day dedicated to
large-scale data systems
and the future of
data leadership
June 7, 2023 | Live Virtual Event
Enterprise Data Summit 2023 | Live Virtual Agenda
8:35 AM PDT
Morning Keynote: Data management and analytics market trends: Rosy or cautiously optimistic?
George Mathew
Managing Director
Rohit Choudhary
Founder & CEO
As a venture capitalist at the forefront of technology investments, George has been closely observing the dynamics of this market and identifying key trends that are shaping its future.
In this keynote address, George will share insights into the latest data management and analytics market trends that every business leader and technologist should be aware of.
From the rise of AI and ML in data analytics to the increasing adoption of cloud-based data management solutions, George will delve into the transformative forces that are driving the evolution of the market.
9:05 AM PDT
Panel Discussion: How are data leaders navigating right now
Dora Boussias
Senior Director, Data Strategy & Architecture
Stryker
John Steinmetz
VP of Data & Analytics
Keyur Desai
Global Vice President, Data, Analytics & Artificial Intelligence
Eric Gonzalez
VP, Business Intelligence Architecture
Join four data leaders as they discuss the current state of data leadership with a strategic focus on managing through uncertainty, resource allocation for human capital and budgets, and advocating for the further empowerment of the data function throughout their organizations.
9:45 AM PDT
Session: Optimizing time to value with a "lego style" data team
Veronika Durgin
VP of Data
“Lego style” data teams, with their modular structure, can be capable of adapting to challenges faster, can facilitate quick feedback cycles, and better utilize individual skill sets to realize a shorter time to value.
Veronika joins us to discuss her strategy for optimizing time to value with a “lego style” team and how to unlock their true potential.
9:45 AM PDT
Session: Building data products, not features
Deepak Jose
Global Head of ODDA Analytics Solutions, Senior Director
While data features are essential for uncovering insights, data products provide tangible value to stakeholders in the form of actionable reports or usable tools.
Deepak's session explores how creating and implementing data products effectively drives business value and improves stakeholder satisfaction. He'll share his perspective on how data products are both more easily monetized and more accessible to non-technical requestors, democratizing data and making it easier to use and understand.
10:20 AM PDT
Session: Addressing productivity black holes for data analytics and AI teams
Sandeep Uttamchandani, Ph.D.
VP, Analytics, AI & Data
Data can only be the new oil if it can fuel better and faster data-driven insights and models that drive business value and transformation. There are several bottlenecks today that slow down Data Analytics and AI teams.
In this talk, Sandeep, a seasoned leader with more than two decades of experience leading data, analytics, and AI teams, will discuss his perspective on the productivity black holes that limit Data Analytics and AI teams, and how to address them by thinking holistically across people, process, technology, data, and mindset levers.
10:20 AM PDT
Dale McDiarmid
Product Engineer
Session: Do I need an ML-specific database in my modern data stack?
Are you exploring leveraging advancements in AI like ChatGPT and other LLMs? Are you trying to work out how to fit them into your existing architecture, or wondering where to start?
In this session, we explore vectors, how they relate to LLMs like ChatGPT, and how they're used to power AI functionality in everyday applications. We cover the rise of vector databases, when they're needed, and when they aren't.
You'll leave the session with a buyer's guide to the decision process, and hopefully answer the question: "Do I need a vector database?"
All without asking ChatGPT.
10:55 AM PDT
Fireside Chat: Looking ahead: What will be expected from data leaders by 2025
Vishnu Ram Venkataraman
Vice President, Data Science & Engineering
Akash Garg
Advisor
This fireside chat explores the changing role of data leadership over the next two years and what additional expectations will be placed on data leaders as they seek to empower their teams and maximize the impact of data throughout the organization.
Hear insights from Akash and Vishnu on essential skills and qualities such as the ability to effectively communicate with stakeholders, build and manage diverse teams, and drive a culture of data-driven decision-making, as well as the importance of creating a strong data infrastructure and governance framework to ensure that data teams are able to meet the company's data goals.
11:30 AM PDT
Session: How T-Mobile implemented data observability with their platform modernization
Vikas Ranjan
Senior Leader, Data Intelligence & Innovation
Vikas joins us to share how T-Mobile has developed a comprehensive strategy for data platform modernization and general engineering best practices from a Tier-1 telecommunication provider.
He will walk through his perspective on how to de-risk data platform migrations and accelerate migration initiatives. Attendees of this session will learn how data observability has helped throughout the modernization journey from a legacy ecosystem to an open source and cloud native platforms such as Databricks and Snowflake.
12:05 PM PDT
Session: What I'm seeing from current DataOps adopters and how to get there at your organization
William Lloyd
Managing Director
Enterprise data teams struggle with day-to-day operations, maintenance, and enhancements of data infrastructure, platforms, applications, management, and governance. Overcoming DataOps challenges for effective data-driven decision-making has been complex but beneficial.
In this how-to session, William will walk through:
- Talent needed to build a DataOps team
- Common tools and solutions needed for successful deployment
- How to put together a complete DataOps playbook
- Who and how to partner with for maximum effectiveness
12:05 PM PDT
Session: Decision making insights for moving from one data vendor to another
Jhakir A. Miah
Director of Engineering
There are many considerations when making the switch from one data platform to another. There are vendor-specific considerations like cost, vendor lock-in, vendor flexibility, and migration support as well as internal-specific considerations such as budget, performance requirements, future roadmap compatibility, and data security.
12:40 PM PDT
Session: Generating engineering code using GPT: realistic or not quite ready?
Abhishek Choudhary
Senior Staff Data Engineer
Rapidly gaining adoption, data engineering teams are starting to explore GPT as a viable option for generating engineering code. Join Abhishek as he walks through this potentially valuable GPT use case, how he's approaching this currently, and the resulting impacts it can make for data teams.
12:40 PM PDT
Session: Ensure cloud data platform migration success using data observability
Pushpa Ramachandran
General Manager & Head - Strategy, Alliances, Platforms & Solutions
Modernizing data platforms to address issues with scalability and performance, security, growing cost, reliability of data, and support for advanced analytics and AI workloads has become a multi-phase, multi-year journey.
Data observability has emerged as a more effective way to help modernize data platforms away from Hadoop/proprietary Cloudera ecosystem to an open source platform or to cloud native platforms such as Snowflake, Databricks, and Redshift.
In Pushpa's talk, you will see how data observability addresses key modernization challenges:
- De-risking data platform migrations with data reconciliation and pipeline performance
- Automating, data validation, and troubleshooting data and performance issues
- Avoiding unexpected overruns and determining optimal workload placement
1:15 PM PDT
Kevin Petrie
Vice President of Research
Session: Top ten problems to solve with data observability
The emerging discipline of data observability optimizes data quality and pipeline performance by using techniques adapted from governance and application performance tools. This session explores ten common use cases for data observability, including the business and technical problems they solve. Together they enable enterprises to prepare, operate, and adjust data delivery across complex modern environments.
1:50 PM PDT
Panel Discussion: Emerging architectures in the enterprise
Mark Freeman II
Founder
Chad Sanderson
Chief Operator
Matthew Housley
CTO
Where & when?
Enterprise Data Summit 2023 will take place virtually on June 7th, 2023.
What is the cost to attend the virtual sessions?
EDS 2023 is always free and open for all to attend.
What is EDS 2023?
Data initiatives and priorities are often affected by resources, organizational structure, and data maturity, all of which can be different inside an enterprise-sized company or for teams processing data at scale.
Enterprise Data Summit is an annual day where leaders from large companies looking to succeed with large-scale data systems can explore ways to modernize their data platforms, enable engineering best practices, allocate resources efficiently, and wrangle topics like data observability.
Who's coming to EDS 2023?
Data, analytics, AI, data science, and other team and department leadership, engineers and administrators, FinOps, and risk/compliance officers.
Join us for talks that include:
- Guiding enterprise-level data teams
- Modernizing data platforms
- Cloud financial management
- Managing large-scale data systems
- Data quality and data reliability
- Resource and talent allocation
Interested in speaking at EDS 2023 or supporting the event as a sponsor?
Please submit your talk topic here or reach out to [email protected].
Sign up below for a free virtual ticket to Enterprise Data Summit!
Thank you to our 2023 sponsors who have made this year's summit possible
