Signs of a data project gone wrong
Trade Secret: Too many BI and analytics projects fail.
I saw this data on smart meter rollouts and while it's interesting in itself, I think it also illustrates the warning signs of a data project gone horribly wrong.
I've seen plenty of data projects go south and the story of Britain's smart meter rollout has three signals I've learned to look out for.
What's your experience?
The danger signs:
1. Insight nobody can action
My smart meter shows the energy I've used. It doesn't tell me which appliances to switch off to meaningfully impact my bill. So I ended up unplugging it.
If a project offers numbers without actionable insight, it'll suffer the worst fate of all: Being ignored.
2. Painfully slow delivery
At current rollout rates, smart meters won't be universal until 2038. The problem they solve will likely look very different by then.
Data projects must answer questions quickly, or the question will change before you're done.
3. Scope is too broad
Smart meters promise a lot: Instant insight on energy efficiency, demand balancing, realtime billing, saving Earth.
Thus far they only helped billing.
Analytics projects with similarly wide ranging aims tend to just drown us in data we're too overwhelmed to use. Concentrate on one solution that will definitely add value, rather than ten which might.
What signals do you look for to keep a project on track?