Step up your data maturity.

The headline: You can't skip a step when elevating your data and analytics maturity. You must progress through each stage (and spend time in each stage). Each stage builds on the last.

Why it matters: We live a culture obsessed with speed to value, but not everything can be rushed. Some things simply take consistent effort over time. Like running. You can't decide go from couch to marathon in a week, no matter how hard you try.

The ride of shame.

I had to take the "ride of shame" twice over Independence Day weekend. It's what I call not finishing a run and being forced to call my bride or son for a ride home. I hate it.

On July 4th, I started my run at 8:31 am. It was already 75 degrees, but I filled 2L of water on my back. I had ran a half marathon by 11:00 am, but also drank all 2L of water. The 4 mile run back home was all sun and it was now 85 degrees. I couldn't do it. I called for a ride.

Two days later, it was cooler and cloudy. I left at 12:28 pm with an 11 mile route planned. I made it 10 miles and had to call for a ride. This time, due to overuse - the posterior tibialis in my left foot was inflamed and even walking was unbearable (which I did the last 3 miles).

What did I learn?

You can't skip a step. When I started running in 2020, I was out of breath after a mile. I slowly built up my stamina, then my knees were the weak link that stopped me in a 5K. Now, my lungs and knees can handle 10+ miles, but my feet can't do it twice in 3 days.

It's been a slow journey with increasingly consistent effort that was necessary during each stage. I couldn't push the limits of my feet without first pushing my knees and lungs.

Back to data.

Everybody is talking AI and analytics, but no one wants to talk data literacy, data quality, and data infrastructure. Just like no one puts a 1.0 mile sticker on their car - even if its a necessary starting point to bragging with a 26.2 mile sticker on your car.

So what do you do?

There are 100 data and analytics maturity models out there. Some good. Some less. Pick one (or message me and I'll share my favorite). Assess where you're at and where you want to be (by when). I set a goal to run a marathon this fall. I'm not there yet, but on my way.

Then...

1. You start small. There will be a lot of small, unnoticed efforts. I still remember how much I detested writing metadata when I was 24 years old. No one (okay, few people) actually like talking about data governance. But those are your mile runs. Start small, but start.
2. You stick to it for (years). Accept that it's going to take time. You can upgrade technology overnight, but you can't rush data literacy across an organization of real, living, breathing people. Stay the course and stick to preaching that data is an asset.
3. You strengthen the weak spots. The more you push forward, the more you'll find what's keeping you from the next stage. For me this weekend, it was my left foot. For you, it may be that your data quality is poor, your data pipelines are too slow, or you have data silos.
4. You measure progress. The real reason I track my running data is that I'm a nerd. The other benefit is I can celebrate where I am today to where I was yesterday. A data and analytics maturity model with specific, tangible characteristics of each stage does the same.

Call to action: Don't know where your analytics capability is today? Don't ignore it, pick a model (like this ), make a plan, and get started. You can't go from couch to marathon in a week, nor can you go from data silos to AI in a month.

This article is part of my blog, Running Thoughts on Data. My first post, The Story My Data Cannot Tell , shares the genesis of my blog. The views and postings on this site are my own and do not necessarily represent those of Plante Moran.