If you’re struggling to measure the impact of change initiatives you’re not alone. Every company wants to think of itself as data-driven, but walking the walk is a lot easier said than done.
We’re going to dive into some of the common roadblocks teams face and key steps you can take (at any level) to make your operations more strategic.
Data analytics has almost limitless potential when it comes to creating business value. Already, organizations recognize the competitive advantage of being data-driven and they are willing to spend big to make it happen. Last year alone, companies invested $40 bn globally on data and analytics services — a number that’s predicted to grow by 12% in 2021. This is great news, but the data arms race has many organizations taking shortcuts.
Consider this statistic from a recent HBR survey: “Of 64 C-level executives at large corporations, 72% said they had yet to forge a data culture, and about half admitted they were not competing effectively on data and analytics.”
The monetary investment in data transformation is there, but the organizations themselves aren’t ready… Something isn’t adding up.
Here’s why organizations think they’re data-driven when they aren’t…
From an operations perspective, this disconnect is unsurprising. We see it every day: companies have the investment and tools but they need to catch up on the culture side with internal processes and know-how. One of the biggest misconceptions is that a data-driven culture will naturally develop at tech companies and startups. The truth is that it takes work, and this can be hard especially for organizations in the hectic growth phase.
Sometimes, being data-driven means going against your instinct and with the evidence. This is contrary to how we often use data, i.e. hunting for data to back up decisions we’ve already made or prove performance in retrospect. In either case, the data isn’t driving (it’s more like a carpool dummy).
What does data-driven measurement actually look like?
The essence of a data-driven operations team is its rigorous approach to material operational changes. They create and follow consistent project and reporting formats, never skimping on the crucial details like the project description and KPIs. It’s easy to overlook these steps as ‘bureaucratic’ but they are actually important cultural norms that should be established in order to orient teams to a data-driven mindset.
3 common mistakes that kill data transformation…
Sometimes the actions you don’t take are just as important as the ones you do. Avoiding these three common mistakes will help you make better and more insightful measurements.
1. Measuring without a plan or not measuring at all
Even if you don’t have the technology or infrastructure to do all the measurements you want, it’s still important to measure what you can. If you have a wide array of measurements available, define which data points will be most useful to your particular project and make sure they speak to overarching business goals.
2. Moving the goalpost
Resist the temptation to edit the project goals in retrospect to make a project appear more successful. In the end, your measurements won’t help you optimize your efforts going into the future.
3. Failing to choose KPIs (prior to making changes)
Plans and measurements don’t have to be static. Changes are ok! However, they should be carefully considered. Don’t be hasty with alterations, give the project enough time to run before you scrap it, and if changes are made be sure to name the new KPIs.
The most important to-do for data-driven operations teams
Sometimes we forget to do the obvious. For measuring change, this means ‘defining success’. You want to have a clear idea of what success means qualitatively and quantitatively. Define what metrics you want to move, and how those metrics will affect your business internally and externally.
You now have the basics of setting your team up for data-driven success. This is an iterative process where investment over time is what pays off. If you are at the outset of a new project, it’s a perfect time to review your process and start implementing some of these learnings.
