Dependency Chain modelling

A rusted chain
Photo by Danielle MacInnes on Unsplash

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.

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Quick and dirty dependency map automation

horizontal neon lights
Photo by H Shaw on Unsplash

After some of my recent articles on building a dependency map, a few people got in touch asking for tips on actually creating them. Here’s a quick way to get started.

You might have noticed the following example in my previous posts.

Dependency map example

I created the graphic above with an amazing bit of kit called Neo4j. It’s actually an incredibly sophisticated graph database technology, so it almost feels a little sacrilegious to be using it for this.

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Dependency Map Analysis

pen on paper showing a graph
Photo by Isaac Smith on Unsplash

If you’re not up to speed on the concept of dependency mapping, then I’d suggest taking a look at my previous post where I talked through how to go about building a dependency map.

So what happens now? You’ve gone through the workshop and now have a bunch of data that’s telling you what? Something about your system? I’m going to run through some of the actions I take when attempting to understand a dependency map.

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