The data road trip: enjoying the journey

In the dynamic world of analytics, it's easy for us to get caught up in the chase for the next big thing. However, it’s crucial to remember that data is not a destination but a journey. A journey mindset allows for greater flexibility and continuous improvement, which are essential in our ever-evolving field.

This post explores my thoughts on the subject, but the concept and analogy were seeded by my good friend Bhavik Patel in their post titled Data is a Road Trip… not a Destination.

The road trip analogy

...think of Data as a Road Trip. The brain is no longer fixed on “what’s the final destination?” or “when do we need to get there?”. It even accepts that it’s a longer journey. It’s focussed on the planning of the trip. The places we are going to stop and see, the budget we have…

Bhavik Patel - Data is a Road Trip… not a Destination

Viewing data work as a road trip rather than a fixed endpoint helps us appreciate the milestones we achieve along the way. This approach allows us to adapt and evolve, ensuring that we are always moving forward, even if the ultimate destination isn't clearly defined. It’s about the journey and the insights we gather along the way.

The pitfalls of a destination mindset

...to start with, only a handful of people know where we’re going and they haven’t really told anyone. Or they might not even know know themselves, they’ve just been told that we all have to go! They don’t really know how long it’s going to take to get there, maybe they’ve never even been before. And the time they need to arrive is fuzzy at best. This means… everyone waits until the last minute to leave. Most people don’t know why they’re going in the first place. No one plans for any disruptions to the journey and they’re view is that the length of the trip is some finite amount of time. And finally, everyone thinks that once we’re there, the journey is over.

Bhavik Patel - Data is a Road Trip… not a Destination

A rigid, destination-focused mindset can lead to significant challenges. Organisations that fixate on a specific endpoint often overlook valuable insights that emerge during the process. This can result in frustration, especially when the anticipated "end goal" takes longer to achieve than expected.

For example, you may not need a perfect data warehouse to start using the GA4 data in BigQuery. Or you may not need machine learning or AI to do attribution and media mix modelling (MMM) to start optimising your marketing spend.

Embracing the journey

It's essential to appreciate where we are on our data journey. Organisations too often get caught up in the pursuit of advanced technologies and higher levels of data and analytics maturity, forgetting to make the most of their current tools and capabilities.

I can't mention analytics maturity without plugging Mark McKenzie's book Your Data is F**Ked: For Marketers. It has been a great asset in helping me shape my thoughts on this.

I also have to refer to Avinash Kaushik's 90-10 rule, which suggests that 90% of your analytics budget should be spent on people and processes. Fully leveraging existing technology and ensuring that the team can extract maximum value from it before moving on to something new is crucial. And also a large part of why analytics/data teams are too often classed as a cost center and not a profit center!

Shifting to a journey mindset

Here are some practical tips to help you shift from a destination mindset to a journey mindset:

1. Ask the Right Questions: Before investing in new technology, ask yourself "what are we going to do differently with this new tool?". This encourages a thoughtful approach to technology investment, ensuring that any new tools genuinely add value.
2. Leverage Existing Tools: Make sure you are fully utilising the capabilities of your current technology. Often, organisations jump to new tools without fully exploring the potential of what they already have.
3. Focus on People and Processes: Invest in your team and processes. Technology is only as good as the people using it and the processes supporting it.
4. Adaptability: Stay flexible and open to change. The ability to pivot and adapt to new information is crucial in the fast-paced world of analytics. Especially considering the speed of change due to generative AI.
5. Cultural Shift: Encourage a cultural shift towards appreciating partial data. Make informed decisions based on the data at hand, rather than waiting for the "perfect" data set. This can lead to more timely, effective outcomes.

Final (for now) thoughts

By embracing the journey, we can better navigate the complexities of data work and achieve meaningful value along the way. This approach not only helps in making the most of current resources but also prepares us for future advancements.

For a deeper dive into this topic, I recommend listening/watching our episode of The Measure Pod titled #102 Data is a Road Trip, Not a Destination. Myself and Bhav discuss additional insights and real-world examples that illustrate the importance of this mindset in analytics. You can find it on Spotify and YouTube, and anywhere you get your podcasts.

Remember - in the world of data, it's not just about reaching the destination but about making the most of the journey. And sometimes, someone else's destination is not where you need to be, and that's okay.