Last Friday’s blog discussed how I’ve just been blown away by the most elegant OSS innovation I’ve seen in decades.
You can read more detail via the link, but the three major factors in this simple, elegant solution to data quality problems (probably OSS‘ biggest kryptonite) are:
- Being able to make connections that break standard object hierarchy rules; but
- Having the ability to mark that standard rules haven’t been followed; and
- Being able to uses the markers to prioritise the fixing of data at a more convenient time
It’s effectively point 2 that has me most excited. So novel, yet so obvious in hindsight. When doing data migrations in the past, I’ve used confidence flags to indicate what I can rely on and what needs further audit / remediation / cleansing. But the recent demo I saw of the CROSS product is the first time I’ve seen it built into the user interface of an OSS.
This one factor, if it spreads, has the ability to change OSS data quality in the same way that Likes (or equivalent) have changed social media by acting as markers of confidence / quality.
Think about this for a moment – what if everyone who interacts with an OSS GUI had the ability to rank their confidence in any element of data they’re touching, with a mechanism as simple as clicking a like/dislike button (or similar)?
Bad example here but let’s say field techs are given a design pack, and upon arriving at site, find that the design doesn’t match in-situ conditions (eg the fibre pairs they’re expecting to splice a customer lead-in cable to are already carrying live traffic, which they diagnose is due to data problems in an upstream distribution joint). Rather than jeopardising the customer activation window by having to spend hours/days fixing all the trickle-down effects of the distribution joint data, they just mark confidence levels in the vicinity and get the customer connected.
The aggregate of that confidence information is then used to show data quality heat maps and help remediation teams prioritise the areas that they need to work on next. It helps to identify data and process improvements using big circle and/or little circle remediation techniques.
Possibly the most important implication of the in-built ranking system is that everyone in the end-to-end flow, from order takers to designers through to coal-face operators, can better predict whether they need to cater for potential data problems.
Your thoughts?? In what scenarios do you think it could work best, or alternatively, not work?