let's

empower

data teams,

together

live virtual

peer-to-peer

sessions

jan 24, 2024

Data Teams Summit 2024 Speakers

John Benito

Senior Manager, Data Science & Engineering

Veronika Durgin

VP of Data

Ali Tore

SVP, Advanced Analytics

Deborah Leff

Chief Revenue Officer

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Gokul Prabagaren

Software Engineering Manager

Jeff Nelson

Developer Advocate

Fabio Italiano

SVP, Head of Data Tooling & BI

Siddharth Jain

Senior Engineering Manager

Julia Bardmesser

CEO

Data4Real

Ash Dhupar

Chief Data Officer

Abe Gong

CEO & Co-Founder

Akhila Athresh

Staff Engineer

Maxime Beauchemin

CEO & Founder

Xinran Waibel

Senior Data Engineer

Tarush Aggarwal

Founder & CEO

Samir Sharma

CEO & Founder

David Jayatillake

Co-Founder & CEO

2024 Data Teams Summit Agenda

Session: Why failed MVPs are your second most important KPI

Julia Bardmesser, CEO @ Data4Real

This keynote explores the importance of failed MVPs in gauging the data teams' innovation. By exploring three common responses to failure – retreat, evolution, and revolution – the talk emphasizes the significance of cultivating a culture that creatively utilizes failure within data teams. Julia will share a transformative approach, urging leaders to reframe failure not as setbacks, but as essential stepping stones to innovation, fostering an environment where learning from mistakes propels teams toward groundbreaking solutions.

Panel Discussion on driving data-driven success: Practical approaches to use case prioritization for optimal business impact

Ali Tore, SVP, Advanced Analytics @ Salesforce

Fabio Italiano, SVP, Head of Data Tooling & BI @ M&T Bank

Ash Dhupar, Chief Data Officer @ BAE Systems

Join Ali, Fabio, Ash, and Jose for a discussion on the essentials of justifying and designing use cases in data function leadership. We'll explore the criteria for use case justification, apply design thinking to selection, and discuss tailoring requests for organizational needs.

The conversation extends to fostering cross-collaboration between teams and emphasizes the crucial role of leadership in navigating the data landscape. Gain practical insights into creating a strategic, user-centric, and collaborative approach to drive success in your data initiatives.

Session: Data insight in the fast lane: How one online bank reduced time to insight and transformed its future

Deborah Leff, Chief Revenue Officer @ SQream

Deborah joins us for a real-world data transformation session covering:

Introduction to NCBA's Challenge: An overview of the complexities faced by NCBA in handling increasing volumes of data in the fast-evolving digital banking landscape of East Africa.
Technological Shift and Solution: Detailing NCBA's transition from traditional data systems to the advanced, GPU-accelerated analytics platform.
Business and Operational Impacts: Demonstrating the significant improvements in data processing speed and efficiency, and their positive effect on customer service and internal operations.
Future Outlook: Highlighting how the data-driven approach of NCBA is shaping their strategy for future growth and innovation in the digital banking sector.

Breakout Session: Beyond the basics: The last 10 things data teams think about

Veronika Durgin, VP of Data @ Saks Fifth Avenue

Join us for a talk with Veronika Durgin as she navigates some often overlooked data engineering topics such as business seasonality and the hidden costs to consider when deciding whether to build or buy. She will cover the elusive “definition of done”, discuss the value of Data SLAs, and open the lid of the forgotten bucket of work. This talk will provide attendees with ideas for a framework to approach 2024 productively.

Breakout Session: Building a winning data culture

Xinran Waibel, Senior Data Engineer @ Netflix

Data engineering serves as the foundation for data-driven decision-making, yet assembling an effective data engineering team poses significant challenges. A lack of a cohesive data engineering culture can lead to adverse outcomes, including inefficient data systems, unsuccessful data projects, diminished trust with partners, and discontent among data engineers, ultimately driving them to seek better opportunities elsewhere.

This discussion will delve into the pivotal roles played by data engineers, underscore the essential nature of data engineering in the triumph of data organizations, and provide insights on constructing a thriving data engineering culture that not only empowers data engineers but also strengthens collaboration with partners.

Panel discussion on the rise of the data generalist: Smaller teams, bigger impact

Tarush Aggarwal, Founder @ 5X

Akhila Athresh, Staff Engineer @ WeWork

Maxime Beauchemin, CEO & Founder @ Preset

David Jayatillake, Co-Founder & CEO @ Delphi Labs

In the evolving data landscape, efficiency now trumps team size, with the rise of data generalists who can handle diverse tasks. Traditional specialized roles face a skills shift towards adaptability and versatility. The emphasis is on increased collaboration, effective communication, and the imperative for continuous learning to thrive in the evolving data landscape.

Panel discussion: Navigating the maze of specialization in data engineering

Jeff Nelson, Developer Advocate @ Google

Siddharth Jain, Senior Engineering Manager @ Wayfair

Gokul Prabagaren, Software Engineering Manager @ Capital One

The decision to specialize technologically or thematically can be daunting. In this panel, we address the tradeoffs and considerations involved in each path to help guide earlier career data engineers as they navigate.

Technological specialization: some people may want to go deeper into data warehousing, streaming technologies, machine learning, or ML engineering. Going deep in a niche can benefit your career, but faces the risk of overspecialization and adaptability to new technology
Thematic specialization: pros and cons of developing a comprehensive understanding of a specific domain (e.g. healthcare, finance) versus staying more general and thinking of "data as data". The former gives valuable insights into unique data challenges within a specific industry but could lead to restrictions in career mobility across industries
Balancing how wide and deep a data engineer goes: consider personal interests/skills/career aspirations, evaluate the current and projected demand for specialized roles, seek mentorship and guidance from experienced data engineers or SWEs for their opinions

Breakout Session: The dynamic duo of data and machine learning engineering in cybersecurity

John Benito, Senior Manager, Data Science & Engineering @ Capital One

Cybersecurity has evolved into a relentless battle against sophisticated adversaries in the era of constant digital threats. Beyond the frontline efforts led by security professionals and their tools, the game changers of our digital defenses emerge in data engineering and artificial intelligence.

This presentation delves into the complexity of the cybersecurity landscape, highlighting the transformation of adversaries and the need for a multifaceted defense strategy. Data engineering, as the silent architect, lays the groundwork for efficient data collection, storage, and processing in the realm of cybersecurity.

Meanwhile, artificial intelligence emerges as the strategic brain, utilizing algorithms and models to turn data into actionable insights. Together, these forces create a unified front against cyber adversaries, enabling real-time threat detection, automated responses, and continuous adaptation to new challenges. As technology leaders, recognizing and celebrating the pivotal roles of data engineering and artificial intelligence is crucial for fortifying our organizations and ensuring a secure digital future.

Breakout Session: Building a shared foundation of trust through data storytelling

Abe Gong, CEO & Co-Founder @ Great Expectations

As data practitioners, we’re often frustrated with how our work is received: “Why do people ignore/disagree with/distort my analysis? Why don’t people just trust the data?” This talk digs into reasons why this happens, how you can prevent it, and how to stay zen when you can’t. It’s a practically oriented talk, grounded in years of experience doing analysis, building models, and leading data teams. As a bonus, I’ll also include results from a survey of 200 data practitioners about communication and storytelling with data.

At a purely logical level, the root cause behind people not “trusting the data” is that all analysis depends on two things: the data itself, plus assumptions about the context around the data. If you and your audience don’t share the same assumptions, they won’t share your conclusions. At an emotional and organizational level, there are all kinds of miscommunications, fears, incentives, agendas, etc. influencing why people don’t share assumptions.

This talk shows how great data storytelling can help solve both kinds of problems: building a clear, logical argument to support your analysis and ensuring that your chain of reasoning is grounded in assumptions shared by your listeners. Unlike most talks on data storytelling, I won’t just focus on tactics for delivering a single presentation—I’ll talk about how to use data storytelling to build a foundation of shared trust over time. This talk is for the 81% data practitioners who consider data storytelling an important part of their role and want to get better at that skill.

Breakout Session: Your company's success isn't measured in megabytes, it's measured in impact

Samir Sharma, CEO & Founder @ Datazuum

Samir Sharma will take us through how organisations can deliver value and impact in their data strategy. Using his tool the Data Strategy Canvas Samir will provide insights into how you can utilise the tool to build more effective data strategies that will ultimately contribute to revenue growth, customer satisfaction, and overall business resilience.

What is the cost to attend and watch the virtual sessions?

Data Team Summit is always free and open for all to attend.

When is Data Teams Summit 2024?

Data Teams Summit 2024 was held on January 24, 2024.

What is Data Teams Summit?

Data Teams Summit is an annual peer-to-peer day of empowerment for data teams that reflects our focus on the teams and individuals running, managing, and monitoring data pipelines.

Data Teams Summit is a full-day virtual conference, led by real-world data practitioners and leaders at future-forward organizations about how they're establishing predictability, increasing reliability, and creating economic efficiencies with their data.

Who comes to the Data Teams Summit?

Data professionals and experts including data engineers, administrators, architects, analysts, AI/ML professionals, and relevant data technology leadership.