Spring 2022
The Data Observability Hybrid Event
Join today's data leaders as they share key strategies
to make an IMPACT with your data.


Join us IRL (in real life) as we take on three major cities for an awesome in-person experience combining panel discussions, hands-on workshops, certification programs and networking events. This is the industry event you don’t want to miss. We’re bringing together every type of data professional from CEOs and co-founders, to leaders in data engineering and data science.

San Francisco, Bay Area
May 10, 2022
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London, United Kingdom
May 11, 2022
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New York, NYC
May 12, 2022
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On-Demand Virtual Keynotes

Access a 3-part data leaders virtual keynote series, where you'll hear from Hilary Mason, Ben Rojogan, and Afua Bruce on the three factors you need to drive real and lasting impact: people, processes, and technology.

Building A Culture of Data Trust:
Winning Over Resistors & Influencing Organizations

Succeeding with data isn’t just a matter of dumping your data in a data lake and populating a few executive dashboards. It requires you to develop a data culture throughout the entire organization.

Hilary outlines the steps she has taken at organizations of all types, sizes, and maturity levels to transform cultures from data curious to data driven.

Watch this session and learn:
Why data democratization brings more benefits than challenges
How to make the scientific method a standard operating procedure across your team
Organizational challenges and tradeoffs for setting up a functional data practice
Overcoming organizational inertia, skeptical resistors and apathetic laggards
Processes and technologies for keeping everyone engaged and aligned
Building data trust and executive buy-in
Hilary Mason
Co-founder Hidden Door, co-founder hackNY, former GM Machine Learning at Cloudera.

Architecting for Data Reliability 

Just as site reliability engineers manage application downtime, data engineers need to move beyond unit and integration testing as part of their efforts to manage data downtime. The modern data stack has too many moving parts to do otherwise.

Ben will show how organizations are implementing principles of data observability, an organization’s ability to fully understand the health of the data in their system, to build more reliable data ecosystems. You will see how end-to-end monitoring, alerts and lineage can not only prevent errors from occurring, but accelerate root cause analysis and incident resolution when they do.

We’ll also cover real use cases for how organizations have built data SLAs to set expectations, measure performance, and treat their data platform like a product.

Ben Rogojan
Seattle Based Data Consultant, former Data Engineer at Meta and data influencer of the "Seattle Data Guy" channel.

Democratizing Data Quality

Your team migrated to Snowflake. Your CTO wants to adopt a data mesh (or so she thinks). And your data engineers won’t stop talking about the “metrics layer.” 

Still, dashboards are spun up in a hurry for ad-hoc requests, then go untouched for weeks; critical reports are ignored by the stakeholders that need them most. Implementing new technologies is just the first step towards accelerating innovation; at the end of the day, it boils down to adoption and trust.

In this talk, Afua Bruce, former Chief Program Officer at DataKind and Executive Director for the White House Office of Science and Technology Policy, will discuss common barriers to adoption for data analytics initiatives and share best practices for leaders seeking to overcome them.

Topics include: 
1.) Strategies for getting cross-functional organizations to move quickly
2.) Using data as “connective tissue” between teams
3.) Setting KPIs and SLAs for your data team
4.) Perform data quality checks non-data professionals can understand
And much more

Afua Bruce
Author, The Tech That Comes Next, former Chief Program Officer at DataKind, and Director Of Engineering, Public Interest Technology

O'Reilly's Data Quality Fundamentals

Do your product dashboards look funky? Are your quarterly reports way off? Are you sick and tired of running a SQL query only to discover that the dataset you’re using is broken or just plain wrong?

These errors are highly costly and affect almost every team, yet they’re typically only addressed on an ad hoc basis and in a reactive manner. If you answered yes to any of these questions, then this book is for you.

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