dbt and data observability for strategic analytics: A case study from TOCA Football from Coalesce 2023

“Since coming to TOCA soccer, we’ve made a lot of strides. A big use case of that has been Monte Carlo and our implementation there.”

Sam Cvetkovski, Director of Data & Analytics at TOCA Football, and Barr Moses, Co-Founder & CEO of Monte Carlo, discuss the importance of data reliability, the journey of data observability, and the role of data in TOCA Football's operations.

Importance of rebuilding and maintaining trust in data

Sam stresses the importance of rebuilding and maintaining trust in data as a key concern in her role as Director of Data and Analytics. She highlights that identifying and addressing data trust issues was a priority from the moment she joined the company.

"From day one, I knew that was really our priority was to regain trust," Sam states. She points out that regaining trust wasn't just about technology, but also about the processes in place. The team worked to align with stakeholders on outcomes and implemented rigorous QA processes before creating new models or data.

The success of these trust-building efforts was measured in time saved and increased efficiency. Sam notes, "Our time to answer questions is a lot quicker. It's not necessarily easy to measure, but it's also a little bit of a feeling where people are now saying 'Oh, this is really nice. I don't have to wait three weeks to get this answer when I don't need this information anymore.'"

Addressing data debt through intentional building

Sam discusses the concept of data debt, which she describes as stemming from a point-to-point building approach. This approach initially appeared to answer stakeholders' questions efficiently but led to a patchwork of data solutions that could not sustainably serve the company's needs.

"A lot of our data debt was just that point-to-point building, trying to answer questions," she explains. To address this issue, Sam and her team decided to redefine their building approach from the ground up, constructing their data models around the company's specific needs rather than accommodating the limitations of their various data sources.

This intentional, company-focused approach to data building has significantly reduced TOCA Football's data debt. Sam says, "We need to really define from the ground–what is revenue? What are visits? What are bookings?–align it to TOCA, and then rebuild from there."

Role of AI in enhancing training

Artificial intelligence was identified as a potential tool that could enhance the training provided by TOCA Football. Sam reveals that conversations were ongoing within the company about how AI could be used to support and make training smarter.

"One of the conversations we are having around AI is: can we support that training with AI? Can we make the training smarter than it is right now?" Sam says. She adds that the team was considering using AI to adjust training to better suit the needs of individual athletes, without the need for human intervention.

The importance of stakeholder prioritization

Sams stresses the importance of stakeholder prioritization in the decision-making process. She advises that pushing stakeholders to identify what is most important to them can help guide the direction of data projects and ensure resources are used effectively.

"It takes time to work with stakeholders and leadership to say this is why we need to do this," Sam explains. "I think putting that pressure on helps people understand that this isn't an easy, ‘Okay, I’m just gonna go do this really quick and just get you this answer,’ but having them understand the impact of changing priorities and identifying what's important to them."

Sam’s key insights into data observability

• Data reliability and trust are crucial for any company, and tools like Monte Carlo can help ensure this
• The journey of data observability requires careful planning and execution, with processes put in place to ensure data quality
• TOCA Football uses data to support its operations, marketing, strategy, and product teams
• The company is exploring the use of AI to improve their training programs
• Communication and stakeholder alignment are key when building and implementing new data models
• Data teams can help drive business decisions by asking proactive questions and offering insights