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TREATING DATA AS A PRODUCTData downtime is a hot topic in data at the moment, and for obvious reasons.The cost of data downtime – a term coined by Monte Carlo to refer to periodswhere data is partial, erroneous, missing or otherwise inaccurate – can besignificant for companies who rely on behavioral data for decision making.If making key strategic decisions based on inaccurate data or wastingvaluable time finding and diagnosing issues with data sounds commonplace,then your company suffers from data downtime.But how exactly does data downtime occur? And what can we do to eliminate it?41
TREATING DATA AS A PRODUCTA real-life example of data downtime at AcmeEvery Monday morning at 9am, a weekly strategy meeting takes place at Acmewith attendees dialling in from around the world. Ralph, the SVP ofCommerce runs through the numbers for the past week, and key decisions aremade for the week and month ahead. The report includes data from multiplesources; from online and offline sales, to payments, promotions and so on.The report lands in Ralph’s inbox ahead of the meeting every Monday, givinghim time to look through the data and prepare. However, this week there is aproblem. Ralph believes the numbers look off; he was expecting much betterperformance last week and sends an urgent email out to the entire Data teamquestioning the accuracy of the data and requesting that it is resolved assoon as possible.42
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TREATING DATA AS A PRODUCT
A real-life example of data downtime at Acme
Every Monday morning at 9am, a weekly strategy meeting takes place at Acme
with attendees dialling in from around the world. Ralph, the SVP of
Commerce runs through the numbers for the past week, and key decisions are
made for the week and month ahead. The report includes data from multiple
sources; from online and offline sales, to payments, promotions and so on.
The report lands in Ralph’s inbox ahead of the meeting every Monday, giving
him time to look through the data and prepare. However, this week there is a
problem. Ralph believes the numbers look off; he was expecting much better
performance last week and sends an urgent email out to the entire Data team
questioning the accuracy of the data and requesting that it is resolved as
soon as possible.
42
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