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Hi fishes,
I am planning a switch so was going through some salary data for a software engineer. My ex-senior manager recommended me a website: Growceed.com which helped me a lot in getting clarification about a lot of things but I am in doubt whether the average salaries of software engineer mentioned on Growceed.com really that much in top MNC companies.
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I usually bucket it as “insufficient data” (or something like that) and include it in the reports 😂
Basically, it’s kind of a disclaimer that X amount of data had to be tossed and what they’re seeing is only the correctly collected data. Eventually someone higher level asks what it is and either gets it fixed or decides that the data you do have is enough to make a decision.
Even if it’s a small disclaimer, everyone should know that they’re not seeing the full picture.
But try to do it in a way that doesn’t make it look like you’re just being a dick. Make sure it’s professionally handled.
Ah, the Good in, Good out dilemma. To get the point across, you could also use a food analogy:
Eat Good healthy food, get good energy out
Eat bad unhealthy food, get lack of energy and potentially sick
Garbage in, garbage out - analytics and reporting is only ever a transformative tool, it can’t (and shouldn’t !!!) create new data.
What is the specific issue btw? Incomplete data, incorrectly formatted, etc.?
This has been my experience too lol, for me, it took showing them the results of a project on that data. Once they saw how much work, or how the trestles don’t make sense. They were finally open to understanding.
I feel this so much 😂 or when a department makes a call to consolidate accounts (marketing programs, sponsorships and trade ) on paid accounts with no consideration to how we use that data for advertising reports, YOY budget conversations. I can go on and on. 🤘🏻
I think once you tell someone about an issue he is aware, might be possible that this person do not have a full picture of the problematic data or simply cannot find solution. I would recommend to settled down and identify with the person in question all process and people associated with the wrong data. Then understand the reason with the users entering the data and put a follow up in place about a specific and identified problem. It is always a bit odd but it works well to enter the scope of colleagues sometimes to bring the full view of an issue. Best of luck !
Say exactly what you said here. No secrets to clear communication.
I think I once used the phrase:
"I'm concerned that your data doesn't reflect reality, and you're going to need to improve the consistency and quality of your data if you want to realise the benefits of our service."
In an email... with a couple of senior directors cc'ed... on a critical project... with a major medtech company 😂
That made them sit up straight. We also got them to agree to a clawback where we'd charge them extra for everything that had dodgy data, and then charge them the normal amount to rerun the model on the data once fixed.
That improved things greatly.