When I say IT prioritisation, I mean the process by which we choose how to spend our limited resources within an IT organisation in order to deliver value. It doesn’t matter if the value for you is internal efficiency, external revenue, or anything in between. We simply can’t do everything, so we need to make choices.
These choices are often hard enough to make, requiring us to make decisions using imperfect knowledge in a complex environment, but we seem hell-bent on making it 10 times harder.
This is a question that got asked on a live stream panel I was part of recently. My initial reaction was blunt. Of course we do, and here’s all the evidence you have. But then I started thinking about it a little more deeply. I’ve come to the conclusion that no, you don’t need a daily stand-up. But it’s a rare situation where this is actually true.
A definition of done is one of the cheapest and most effective ways in which you can start tackling IT issues. It can improve quality, transparency, accountability, alignment, and even predictability. All at the cost of a conversation and some documentation.
There’s plenty of material on what a definition of done looks like and why you might want one, but not much on how to go about constructing your own. This is my attempt to fill that gap.
An article I recently wrote on Agile Test Data Management prompted some responses on a more fundamental level. What is Agile testing? There are plenty of methods, frameworks, and comprehensive guides available that will provide you with a template for Agile testing. Unfortunately, most of these are either too prescriptive or thinly-veiled waterfall anyway.
This isn’t one of those, this will be a quick dive into the principles that might help you evolve your own practice.
It’s likely a coincidence, but I’ve recently noticed a slew of issues arising in my work around test data management. As I dug into this, I found a disappointing lack of prescient information on how to manage this in an Agile environment. It’s going to be a quick one this week, but here’s my take on agile test data management.
You can’t move for people talking about the project to product journey lately. Getting organisations to start mapping out what their products are so they can build cross-functional teams around them. Projects are transitory and lose us all of the learning, we want long-lived squads! While these sentiments are all rock-solid, unfortunately, they’re missing the point. I’m going to be controversial and say that creating a product team simply isn’t good enough. What you actually need is outcome alignment.
One of the largest challenges preventing organisations from competing effectively in the market is their structure. When you are optimised for technology or role, then you are not optimised for customer value. But how do you actually get there? What does good look like for your organisation if you went back to the drawing board? I’m going to share a process I’ve developed for redesigning a system for outcome alignment.
Note that this is not going to be a full article on digital transformation or strategic redesign. I am simply going to focus on the guiding principles and a specific approach to assist in exploring an outcome aligned design.
The role of the project manager in Agile is often a complicated one. While there is little concrete direction by many of the frameworks, it is often understood that a traditional project manager may often find themselves moving into a Scrum Master or Product Owner role. The intent behind this guidance is clear, there’s no need for a Project Management Office (PMO) where we’re going. I disagree. Let me introduce the Lean PMO.
A dependency chain is where you have a number of functions that all need to do some part of a piece of work in order to fully deliver it. These functions complete their part and then pass it along to another.
In my last article I showed why your IT requests were likely taking so much longer to service than you expected. But what if you have multiple dependencies chained together? How can you get a rough idea of how long something is going to take when you have a disconnected dependency chain rather than an end to end view of the system. In this article, I’ll explain the process of getting a rough statistical idea of how long something is going to take when you have multiple dependencies all linked together. I’ll explain this through an R script, but the concept is easily transferable.
You’ve got a quick job to do so fire it off to an IT team. It should only take around half a day of effort so you expect to hear back by the end of the week. Three weeks later and you’re still waiting. So why is this happening? What is the deal with IT delays?
Apologies in advance to all scientists, engineers, and mathematicians.