The best way to kill and bury your crusty old PHP system – replatforming a legacy system without your users noticing.
Hearing those words makes me feel like I’m tied mutely to a railway track, unable to scream for help as a train thunders towards me. We humans are walking sacks of blood, bile and bias, and estimating how long things will take brings out the worst in us.
A product manager recently asked me if one can get better at knowing whether things are easy or hard, and how long they will take. The good news is that with practice, you can help people estimate much better with your help than they would on their own.
Understand the problem you’re trying to solve.
If you don’t understand the problem well enough, you’re certainly blind to its potential complexities. Product managers are often in a *better* position than anyone else!
Understand what’s involved in the proposed solution(s).
This can be the trickiest part for non-engineers, because the details of the solution may sometimes be pretty arcane. Here’s what you can do:
- You can go a long way by asking good questions about how things work, and what’s involved in the solution. Listen carefully to the answers. If they don’t make sense, ask for a higher-level explanation, or from a different person. Explain it back – that will make sure you’ve got it right and help you internalise it. Take good notes. Over time, you’ll start to see how the pieces interconnect, and what problems are similar to one another, and this will get easier and easier.
- Don’t ask for an estimate for the whole solution. Break the solution down into pieces, estimate the size of each piece, and add them back together. In my experience, people can’t reliably estimate how long things will take beyond a few hours – so if the estimates are much bigger than this, break the pieces down into smaller and smaller chunks.
- Be the rubber duck!
- Offer to pair-program with a developer during the unit testing. You’ll get a really deep understanding of how the system works, and where the difficulties lie. Better still, if you write your tests before writing your code, your test suite provides a kind of score card for how close you are to a solution, and you’ll reduce time spent in QA.
Be aware and on the alert for pitfalls and cognitive biases that lead to poor estimations.
Human beings tend to be lazy about thinking through all the pieces for a complete solution (just focusing on the major parts, or the interesting parts, and ignoring the detail or the final 20% to make things perfect that takes all the time). They also tend to focus on the best case (if everything goes right) and ignore all the things that might go wrong. You never know what will go wrong, but if you have a sense of some possible pitfalls, you can factor them into your estimate. Possible approaches:
- Start by asking out loud ‘what are the hidden traps, complications, edge cases, difficulties or things that could go wrong. When we did similar things in the past, how long did it end up taking? Were there surprise pitfalls that made it harder than we anticipated?’ Or run a premortem. You’ll get much better estimates after this discussion.
- Use Planning Poker as an estimation approach. Each person makes an estimate in isolation – this forces them to think things through, and avoids estimates being dominated by what was said first or most loudly. The discussion afterwards creates an informed consensus view, and provides immediate feedback for people whose estimates are wildly off.
- As a last resort: make an optimistic estimate and double it.
Learn from feedback.
- Force yourself (or the project team) to make an estimate in advance, then during the project retrospective, compare the actual time taken to the estimated time. That would be the best way for everyone to learn from feedback! ‘We thought it was going to be X, but it turned out to be 2X’.
- If things take much longer than anticipated, ask how we could have predicted this in advance. That might help you avoid similar estimation mistakes in future.
- Notice if certain kinds of tasks tend to take longer than anticipated.
- Notice if certain people tend to be inaccurate, and give them feedback on this.
Abe Gong asked for good examples of ‘data sidekicks‘.
I still haven’t got the hang of distilling complex thoughts into 140 characters, and so I was worried my reply might have been compressed into cryptic nonsense.
Here’s what I was trying to say:
Let’s say you’re trying to do a difficult classification on a dataset that has had a lot of preprocessing/transformation, like fMRI brain data. There are a million reasons why things could be going wrong.
Things could be failing for meaningful reasons, e.g.:
- the brain doesn’t work the way you think, so you’re analysing the wrong brain regions or representing things in a different way
- there’s signal there but it’s represented at a finer-grained resolution than you can measure.
But the most likely explanation is that you screwed up your preprocessing (mis-imported the data, mis-aligned the labels, mixed up the X-Y-Z dimensions etc).
If you can’t classify someone staring at a blank screen vs a screen with something on it, it’s probably something like this, since visual input is pretty much the strongest and most wide-spread signal in the brain – your whole posterior cortex lights up in response to high-salience images (like faces and places).
In the time I spent writing this, Abe had already figured out what I meant 🙂
Have you ever had trouble deciding where to store a file on your hard disk? Or worse, had trouble finding it later?
When you store a file on your hard disk, you have to decide which folder to put it in. That folder can in turn live inside other folders. This results in a hierarchy, known in computer science as a *tree*.
The main problem with trees is that sometimes you want things to live in multiple places.
Tagging provides an alternate system. Tags are a lot like folders, except that things can belong to multiple tags. However, but the tags can’t themselves belong to anything. So you have just one level of organisation with no nesting.
The main problem with single-level tagging is that it’s too simple. We want to be able to use fine-grained categories (e.g. ‘lesser spotted greeb’) that themselves belong to higher-level categories (e.g. ‘greeb’, or even ‘bird’ or ‘animal’). But we said that tags can’t themselves belong to tags.
Described like this, perhaps the solution will seem obvious to you too. We want things to belong to multiple tags, and for those tags to sometimes belong to other tags.
I built this into Emacs Freex, my note-taking system.
For instance, I have tagged this blog post with ‘data structure’ and ‘blogme’. In turn ‘data structure’ is tagged with ‘computer science’ and ‘blogme’ is tagged with ‘writing’. So I can find this blog post later in various ways, including by intersecting ‘computer science’ and ‘writing’.
This gives you the best of both worlds: things belong to multiple categories, along with a hierarchy of categories.
When it comes to tools, I am a hedgehog rather than a fox. I like to have a small number of tools, and to know them well.
I recently resolved to start writing again. But I decided that I needed to sharpen my pencils first.
I have plans on how publishing and sharing should work. Grand plans. Too grand, perhaps.
So for now, I wrote something simple for myself. Now I can type away, press buttons… publish.
If you like Emacs, Python and WordPress, this might be interesting to you too. If not, it certainly won’t be.
Most of the work is being done by this great Python/Wordpress library. Thank you.
I wrote some simple Python scripts. One grabs all my existing blog posts. One looks through their titles, and checks them against the filename to see if this is a new post.
And then there’s a very simple Emacs function that calls them to save/publish the current text file.
I could add more things: deleting posts, or a proper workflow for moving from draft to published. Maybe later.
I wrote this post, then hit M-x wordpress-publish-this-file.
I realized I never uploaded the final version of my dissertation, ‘Weakening memories by half-remembering them‘.