Key learnings from the fireside chat with Orangetheory Fitness

Last week, Switchboard founders Ju-kay Kwek and Michael Manoochehri met with Ameen Kazerouni, Chief Data and Analytics Officer at Orangetheory Fitness, over a fireside chat. Ameen took us through Orangetheory’s accelerated customer and data journey that drives marketing efficiency across 1400 studios and 24 countries globally.

Switchboard has been an integral part of transforming all that customer data into knowledge and insights that helped drive Orangetheory’s explosive growth.

Laying the foundation for a solid data transformation effort

Orangetheory’s data transformation projects, although critical, are not easy, especially when disparate systems need to be connected to derive a cohesive story and enhance the customer journey. From this fireside chat, three major themes emerge for forward-thinking enterprises embarking on a similar data transformation journey at scale.

1. Conduct solid stakeholder interviews with business teams

For every data project at hand, there’s a definite interplay between these two teams. Engineering teams can build the most beautiful and elegant data solution there is. But if the business teams cannot consume that data asset to drive business value, then there’s a lot of resources and cycles spent for nothing.

Therefore, interviewing the business stakeholders early – before any data is processed – is essential. This interview process helps to identify the gaps, define the goals and KPIs, and understand the usage and value of that dataset. Once this brief comes to life, it is a much simpler task to connect the datasets and write the business rules that make sense. It’s not just about using ETL to put the raw data where it’s needed. It is about making that data accessible and consumable downstream.

Michael Manoochehri alludes to the fact that when working on data unification projects for customers, the two teams magically come to the table to discuss the project having never met each other before. And that’s why data maturity is a process but one that works.

2. Don’t boil the ocean – employ an incremental and iterative approach to data sets

When connecting and unifying data assets at scale, it is important to establish early wins via effective feedback loops on a specific one without going all out to industrialize a million data sources. Instead, have all hands on deck to get the first one live with a high-quality bar.

Once it is live, it is important to get that data to a place where it is accessible and consumable by the line of business teams. Monitor the usage to ensure you’ve industrialized the right data asset.

One of the principles to keep in mind while doing this is to ensure that a copy of the last-mile certified data asset is available for tech-savvy users to tweak as necessary. So the various tiers of access allow unlocking the one-to-many capabilities for each asset that comes to life. And it is of course important to keep the new, certified data asset connected to the other existing, streamlined assets to avoid springing back into the data silos problem.

3. Acquire new data capabilities for pre-ETL preparedness

For enterprises to really unlock the core value of data, it’s not just about collecting the data and putting it in pipelines. It is about transforming it to drive knowledge and then insights to positively influence the customer journey via effective funnels and campaign performance. While that transformation is critical, it is tough to do considering the volume and disjointedness of this disparate data. That’s really the core data challenge.

But even before the data comes in, there is a lot of expertise needed in knowing how to integrate this data. This problem compounds with scale when we’re talking about billions of rows with several data formats, redundancies, and many different vendors. It requires domain expertise in understanding these early mappings before the data even becomes usable for the ETL process. That’s the dual data challenge.

Orangetheory is one of the forward-looking enterprises pushing the envelope to do many meaningful things with data. We at Switchboard believe that several brands and media companies will follow their footsteps in the next couple of years. And that’s why these early learnings are priceless.

Switchboard has helped and is helping many other large media companies and brands such as Fandom, Freestar, Financial Times to name a few via a sophisticated cloud-based data automation platform combined with domain knowledge in data.