Poor data quality has a cost. 
Examples of data quality challenges and their impact.
Having correct data is very important in order to make correct decisions. 
Data quality goes hand in hand with proper data modelling.
Knowledge about use cases and workload is important input to your data modelling. 
Different kinds of compression can be relevant depending on your usage scenario.
Having focus on deadlines without having (data) quality in mind will hit you hard at a later point.

I will show a simple way of how you can get attention, but also how to integrate to existing
monitoring system.

Delivering good query performances and reports in time is important to business users,
but how do you measure it from their perspective?
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