talk-data.com

In this video, John Napoleon-Kuofie from Farfetch shares how his team uses testing to improve their sanity, output and their relationships with their stakeholders.

Canva is growing fast, and with that comes a much higher volume and variety of data. In conjunction with this, Canva's data specialty teams are growing too. In this video, Jose and Krishna will share how they addressed the challenges that come about with growth, the missteps, the course corrections, and what they have planned next.

Speakers: Krishna Naidu, Data Engineer with Canva Jose Galarza, Data Warehouse Lead with Canva

This video covers what you'll need to build a marketing attribution data model and why it's important to have one to evaluate your business. It covers how the team at Grailed has built data models in an effort to determine where their users come from, where their orders come from, and more. It shares the value these models have unlocked for our marketing team and what it might unlock for yours!

Speaker: Evy Kho, Senior Operations Analyst, Grailed

Have you ever looked at your data and not known where to start? In this video, you'll learn how Firefly Health leveraged Fivetran's dbt packages to quickly transform their raw Salesforce data to analytics-ready models. A process that typically would take weeks was cut down to just minutes with the power of dbt packages.

Speakers: Dom Colyer, Senior Sales Engineer, Fivetran Jacob Mulligan, Head of Analytics, Firefly Health

Now that your team is using dbt, what are the ways you can help your team work even more efficiently? In this video, Bastien Boutonnet and Jean-François Lairie from TripActions share how they supercharged their data team, by using tooling and processes to make the lives of data analysts and data scientists smoother, and to get everyone working like a data engineer (without them even realizing)!

Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue. But if that’s all you’re doing, your team is probably bored and your organization definitely isn’t getting as much value as it could out of its data.

Open-ended creative work is a huge part of the appeal of working in this field – identifying opportunities to improve processes, appeal to new customers, or build better products adds value for the organization, but it is also incredibly satisfying. One of the fundamental challenges of managing a data team is balancing the need for rigor and reliability with the team’s desire to spend most of their time creating new knowledge. In this video, Caitlin Moorman, Head of Analytics with Trove Recommerce, discusses how we can manage those sometimes conflicting priorities, and create tools and processes that make the balance easier.

Negotiating from a position of power can be game changing for your career. In this video, Shannon Morales will lead a conversation sharing pro-tips for reviewing and adding value to a job offer.

Topics covered will include:

Evaluating an offer Examining cultural fit Finding equitable employers Negotiating salary & benefits

The dbt project at tails.com has over 600 models and 66k lines of code. With multiple contributors to a project and varying SQL backgrounds, it's really difficult to maintain consistent readability and comprehension across a codebase like that by hand.

Python has flake8, Javascript has JSLint, but SQL...?

Listen to this talk from Alan Cruickshank to find out whether SQLFluff might help your teams be more productive with SQL.

Everyone knows that having consistent data definitions is important, but how do you do it? And once you’ve done it, how do you make sure everyone knows how to use them and where to learn more, especially in an ever-growing company?

After 12 years of confusing terminology and wonky spreadsheets, the team at Education Perfect decided to properly define their key data points, enabling them to simplify the interactions between their team.

In this video, Nadja Jury will take you through her journey of trying to balance accuracy with simplicity, where they’ve landed, and where they want to go next.

Dimensional modeling described in the Kimball Toolbook was in its 3rd edition 15 years ago yet is still the latest in data modeling advice. So much is different in cloud warehouses that many of those best practices are now bad practices. In this video Dave Fowler, the founder of Chartio and author of Cloud Data Management goes over what no longer applies, and what does.

Data teams can significantly improve their stature and abilities in an organization, when they work with a product mindset. Product teams typically have UX experts, Designers, Product Managers, Engineering Managers and more, involved in the process of generating new features that will delight their customers.

In this video, we'll argue that Data teams should take on a very similar mindset when leading and growing their data org. We'll make the case that this mindset can scale from a single person team to a large organization. We'll share what this looks like on the ground and in the day to day.

Viewers will be able to walk away feeling empowered about the vital role the data team should — and can — play in every organization. They will explore a new mental framework for how to think about all of the data related activities in their organization.

A shared understanding of how data is defined and managed provides confidence in how it's used. Metadata makes data valuable. But it also holds enormous potential for creating value in and of itself, through workflow tuning, optimization, and a variety of other performance-based use cases. In this video, industry experts shed some light on the different approaches to metadata management, whether there's any truth to vendor claims, and who at any organization should really care.

What does it take to implement a data modeling project when working at a company that's in hypergrowth? In this video, Vincey Au will take us through a recent project to incorporate mobile app data into Canva's sessionization model, with the added complexity of having to make this work on petabytes of data. She'll share some of the lessons learned along the way, and what they'd do differently if they were doing it again.

Learn how a rapidly growing software development firm transformed their legacy data analytics approach by embracing analytics engineering with dbt and Looker. In this video, Johnathan Brooks of 4 Mile Analytics outlines the complementary benefits of these tools and discusses design patterns and analytics engineering principles that enable strong data governance, increased agility and scalability, while decreasing maintenance overhead.

Join us for a fireside chat with members of the TripActions data team to get an inside look at how their team gets work done. We'll learn how their data team is structured, some projects they've recently worked on, and what's coming up for the team!

Speakers: Rob Winters, Director of Data with TripActions Bart Sandbergen, Data Analyst with TripActions Virginia López-Gil Pérez, Data Engineer with TripActions Teodora Vrabcheva, Senior Data Scientist with TripActions Simon Ouderkirk (Moderator), Senior Product Manager with Fishtown Analytics

As data nerds, we want to help our organizations use data to make decisions. But Step 1 is getting users the data they need, and that often means building a seemingly never-ending list of dashboards and reports. In this video, Andrea Kopitz of Nerdwallet will discuss how to move beyond data delivery and start influencing concrete business decisions.

Jonathan Mak of Nearmap says, "We use dbt not just for data transformation but also data movement in/out of Snowflake. This makes dbt more akin to a generic scheduling and orchestration tool to us and it lives at the centre of our data pipeline. I'd like to discuss in this video why we do it this way, the pros and the cons and may also touch on our migration to Snowflake a while ago which allowed us to use dbt this way.

In this video, President and Founder of Mode, Benn Stancil discusses new ways to align the optimal application boundaries in the modern data stack, providing a set of guidelines for determining how and where to draw the lines between your many tools. He also motivates an example of these boundaries by demonstrating how metadata surfaced in an analytics tool like Mode can increase overall data confidence.