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.
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.
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.
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
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.