Start Simple & Iterate
Up to this point, I’ve largely written for those looking to break into an analytics career. Today I’ll go beyond that and discuss the most powerful lesson I and many others learned — something I wish I fully understood starting out:
Start your analytics project as simple as possible and iterate from there.
This strategy borrows a lot from Agile software development not because I’m a student of it, but because I learned the values of Agile through trial and error. Only after I stumbled upon this strategy did I learn how closely it aligns to the Agile methodology.
The Common Mistake
I’m going to assume you’ve already solved the toughest issue in analytics: identifying an ambiguous problem. Congrats! Now you need to figure out how to make it happen. This is where things can go wrong.
Many analysts (myself included!) are then tempted to:
- Retreat to your office
- Gather & clean all the data you think everyone needs
- Build the World’s Best V1 Dashboard
- Schedule a meeting to present the dashboard
- Receive unanimous praise for how amazing it is
- Watch as everyone uses your dashboard daily
What really happens:
- Retreat to your office
- Gather & clean only some of the data people need
- Spend way too long building the Dashboard No One Really Wanted
- Stakeholders email you intermittently asking if you’re making progress
- Schedule a meeting to present the dashboard
- Entire meeting spent fielding questions like “Why don’t I see X or Y?”
- Get the cold sweats realizing you don’t have what they need
- Stakeholders frustrated that so much dev time was wasted
- You’re frustrated that they are “changing what they need”
- Retreat to your office
Why Does This Happen?
Every data analyst/scientist makes this mistake. It will continually happen throughout your career, even after you think you’ll never make that mistake again. No one is immune.
There is one core reason why this happens: You assume you understand what the stakeholder wants.
Except you likely don’t. Especially when you’re early in your career. You’ll think you’re on the same page with your stakeholder, but you aren’t. You think you know what data points the stakeholder needs, but you don’t (hint: the stakeholder likely doesn’t know either!). You think you know what kind of visuals the stakeholder will find most useful, but you don’t.
In fact, it’s so difficult to get everything right the first time, you should assume you don’t fully understand the request. That one time you actually do build “The World’s Best V1 Dashboard”, celebrate the unexpected success – it won’t happen often.
Strategy: Start Simple
There’s a solution to this problem: Start your analytics projects as simple as possible. This results in less wasted time in development and happier stakeholders at the end. The process looks like this:
- Agree with stakeholder on an MVP (Minimum Viable Product) – something small that can be done quickly
- Your stakeholder may not know exactly what they want, so you may have lots of freedom here
- Gather & clean only the data you need for the MVP
- Create MVP dashboard
- Ask your stakeholder questions here, too! You don’t need to go radio silent and many times they’ll appreciate the feedback loop
- Present MVP dashboard to stakeholder
- Gather feedback from stakeholder
- Start process over again
This process is designed to be quick, with small iterations should building on each other until everyone agrees the dashboard fits the needs of the business. The more interactions with stakeholders the better – you’ll quickly identify misalignments, missing data, new requirements, changing business needs and more.
The advantages should be clear. Stakeholders will feel ownership over a product they helped develop (leading to better adoption!). The end product will be closer to what the business needs (leading to better adoption!). And stakeholders will remember the success of the project and give you a call for the next one.
Conclusion
Don’t try to build Rome in a day on any analytics project. You’ll rarely succeed. Instead, iterate and build on a project until it becomes something useful – and likely looks nothing like what you thought it would starting out.
Analytics is a dynamic field. Don’t fight upstream with how quickly things change; set up your work process to allow for quick changes. Your company & future self will thank you.