The true cost of dirty data: 4 ways to tackle common data prep issues

Presented by

Sam Priddy - Tableau; Jason Harmer - Telus; Gordon Strodel - Slalom

About this talk

You may (or may not) be surprised to hear that data scientists spend a staggering 80% of their time on data prep activities. Dirty data – that is, data that’s poorly structured, full of inaccuracies, or just plain incomplete – is a source of constant headaches, but it can also have a severe impact on your bottom line. This webinar examines the cost of dirty data and counts down four ways to tackle common data prep issues so that you can devote more time to more valuable data activities. Inside, learn: • Why dirty data happens • The impact and annual costs of poor data quality • Four common data prep issues (and how to solve them) • And more

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