As mentioned the last two times, at the start, it's more important to start measuring something than it is to measure the right things. Do NOT let analysis paralysis hold you back. Start measuring early to figure out what actually matters and that will also change over time.
Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.
Use fitness functions. Episode #95 with Dave Colls covers a lot on this.
Data mesh really is a journey and your success measurement will be too. You will need to find small and simple ways to measure. Don't get bogged down. Your measurements will be rough and kinda depressing with the amount of challenges to tackle at the start. Just understand this is about how well you are doing, not how complete you are - there is always more to do!
Reflect back on how far you've come, we often forget to do that!
When it comes to data quality measurement at the implementation level, you need to think about what are you trying to accomplish. Many people go down the wrong path of trying to measure quality in a vacuum. It's about what are the expectations and why do we care about quality - to improve our decision making around data and to improve trust so more people feel they can rely on data. It's that simple. Now, measuring how well you are achieving those gets a bit harder… :D
So, what to measure or consider how to measure regarding data quality at the implementation level: how often are people in compliance with their quality SLAs, whatever those SLAs may be? How quickly are you detecting and resolving/recovering from incidents? How many incidents are you having and what is their severity? Who is actually discovering the issues - are there automated detections and is it the producer or consumers discovering them? How do you actually think about trust and the impact of trust on the success of your implementation? How do you measure and increase trust levels? How does that impact value creation? And finally, what is the quality of your metadata?
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