In this 4-part series, we’re going to take you through a sneak-peek into the realm of achieving sustainable predictability. In the second chapter, we talked about how you can get more done with less effort and how this strategy actually enabled us to switch to a 4-day workweek in just 3 months!
Today, I’d like to show you how to use data-driven decision making to make sure your improvement efforts are paying off.
Welcome to Chapter 3 of the Sustainable Predictability series, where we will talk about what it really looks like to adopt a stable delivery system that enables you to consistently hit your targets.
How do you know whether you are making the right decisions? More importantly, how do you know whether these decisions actually improve your business outcomes? What are the criteria which you use to evaluate whether you’re heading in the right direction?
These questions can be challenging, especially if you don’t have the means at your disposal to provide reliable answers. Using a data-driven decision making approach is the cornerstone to achieving that goal.
Why Is Data-Driven Decision Making Important
Achieving sustainable predictability is ultimately all about flexing your management practices to make sure you deliver results in a consistent manner. And your performance data is the evidence that will tell you whether the changes you’ve implemented are working or not.
When we strive to establish stable delivery systems, we use that evidence to help teams shift their behavior and fix the problems that hinder their performance. Without that data, you’re just working off hunches – you may feel like you’re successful, when this may not actually be the case. Without the data, you’re just stuck, you’re just spinning.
Using a data-driven approach to drive your business decisions is crucial in any agility initiative, as it not only gives you the evidence to base your decisions off, but also enables you to continuously improve your business outcomes.
Let’s say you’re struggling to deliver on your commitments and you want to understand where the delays come from.
If you don’t make decisions based on your own past performance data, chances are, you won’t be able to understand what it is that actually slows you down.
Is your QA specialist the bottleneck in the system, not being able to handle all the work produced by the developers? Is the work constantly being ignored due to other, more important work items constantly taking the lead? Is it being blocked by internal or external dependencies?
Your flow metrics and analytics will give you these answers. Without this information, you risk ending up pushing teams to work harder to meet their deadlines, when the solution actually lies in a simple tweak to your management practices.
You should always, and I can’t emphasize that enough, always let your performance data guide you towards your next steps.
The Data-Driven Approach to Improving Business Productivity
More often than not, I see how people keep moving through different frameworks, methodologies and work management approaches while getting nowhere. They start something, it doesn’t work, they go back to the very beginning, they start something new, it doesn’t work again! Then, they find themselves spinning like that all the time.
One of the Kanban change management principles is “Start with what you do now.” And it is so powerful. Start with the challenges you have, the obstacles that hinder your performance, in your own context and improve from thereby making small evolutionary steps.
Use your performance data to set your goals and define your next improvement initiative, then observe how the trends build over time. Did your initiative meet your goal? If yes, make the changes a standard, if not, discard the changes and start looking for an alternative solution.
And your improvement efforts might not pay off every time, but you have to take that step in order to get the feedback from your system and take action accordingly. Failed initiatives can only be failures if you haven’t learned the lesson and adjusted your course accordingly.
Go into your agility improvement initiatives with that investment mentality, knowing that not only are you making efforts to realize, of course, better business outcomes, but you’re also collecting evidence. You’re trying to understand how the decisions you make affect your performance.
If you decide to reduce the WIP limits of the team, did that action actually improve your delivery times? If you introduced queue states in your system design, did you gain visibility on where the bottleneck in your process is?
If you have the data, use the data. If you do not have the data, then get the data and use the data!
Your Data-Driven Decision Making Roadmap to Success
Every small initiative you want to take to achieve more consistent business outcomes should have a measurable goal and a metric tied to it. If that metric is not hitting its target, then you need to stop, rethink, adjust and retest it.
And you just do that until it’s working. Then, you move on to the next improvement initiative. It’s a continuous process, it never ends. Track your performance trends every day, at least every week, and look at the full picture – not just the metrics but the context behind them.
Achieving sustainable predictability is not magic. People who have had success with improving the predictability of their delivery workflows don’t have something that you don’t. They just followed a roadmap and found a formula that works for them in their own context, with their teams and their clients.
Everyone is capable of doing that. You don’t want to just be saying, “We’re struggling to deliver on our commitments”, and then have no idea what you should do and not have an action plan to resolve that problem. Your metrics do tell a story, so use them to navigate your next steps.
And even if you haven’t started collecting data quite yet, at the very beginning, you’ll probably have to make educated guesses. You may not really be sure exactly whether you’re picking the right numbers when setting your goals, but as long as you start gathering the data intentionally and observing how the trends move over time, even after a few weeks, you will already have the benchmarks in place.
Observe, test, adjust and then repeat. If you can do that and commit to that process, you will be able to achieve sustainable predictability. And I truly believe that because I’ve seen it happen so many times.
It’s your turn! What’s the next small step you’ll take, as early as tomorrow morning, to improve the predictability of your delivery system?
If you are striving to enable stable delivery systems that produce consistent business outcomes using your own past performance data, I’d be thrilled to welcome you to our Sustainable Predictability program!
