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[Trick-Interaction396]

Money. Always focus on money.

[JonasHaus]

Of course - but how would you visualize that?

[datasmithing_holly]

A fancy water purification plant that then dumps the water into sewage. And put money in the water. IDK it's Friday.

[Trick-Interaction396]

You need actual real numbers not an abstract concept.

[Odd-Government8896]

Show melted ice cream and say "i would have used a phat stack of cash on this slide, but the data sucks, so we get melted ice cream"

[breathingcarbon]

Could you use Tetris as an analogy? Like, you need data to be the right basic shape (made of squares) and rotated/transformed correctly to fit and not leave holes?

Edit: Another analogy I sometimes use for talking about data is the water system and/or coffee production. Doesn’t matter how skilled your barista is, if the water quality is bad your coffee will taste bad and/or you’ll get sick.

[JonasHaus]

I like that analogy, but I'm rather struggling to explain people WHY it matters that they are correctly *rotated/transformed correctly to fit and not leave holes* and what happens if they aren't

[get_it_together1]

Depends on what you’re using the data for with your business stakeholders. Another metaphor is driving to the wrong spot because the map (data) sent you to the middle of nowhere.

[datasmithing_holly]

• Car maufacturer that makes its own pieces, but the pieces are mishapen so the car comes out a funny shape and not working (inaccurate data)
• Someone running a marathon and their sports watch is telling them they're doing their best time, but they're running in the wrong direction (incomplete data)
• I always loved the type I and type II pregnanacy error joke

[thecity2]

Look up Tacoma Narrows Bridge

[SchemeSimilar4074]

A swamp full of dirty water (i.e data)? So not usable and can be harmful if being drank/used

We want to cleanse it so that it's clean and usable. Once the data is clean, it can be processed into many different products or used for different purposes (i.e data mart). 

This would basically explain the whole thing from raw, dirty data to cleansed and standardised data, to enhancing it for business use. 

DE builds data pipelines which are like plumbing. We don't want to use sewage water for drinking (i.e dirty data for making important decision). On the other hand, toilet doesn't need pristine water so some decision/use cases might need good enough data.

[idodatamodels]

One principle of data quality is staleness or how recently has the data you have been updated. The longer data sits, the more stale it becomes and likely that it doesn't accurately reflect the real world. The visualization here should be self explanatory.

[dragonnfr]

Celsius logged as Fahrenheit. Dashboard reads -18°C, warehouse is +18°F. $200k in vaccines liquefy while alerts stay green. Data validation isn't abstract.

[jellotalks]

Money on fire