How to converge the differents data quality models?

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Without quality data it is impossible to make decisions. There are different models and frameworks from DAMA to ISO. How to measure data quality, how to manage quality processes automatically, how to avoid Data Lake corruptions. Data Quality can be a horizontal process within the company?

Enrichment processes are the value attached to quality but how can we avoid the garbage in garbage out phenomenon? What to do with so much poor quality data.

We usually encounter these drawbacks: erroneous data that is not corrected, but recreated, the same data is entered in different systems, records created with erroneous or missing values, mismatches between data created in different systems causing additional work to be done downstream, erroneous data created during the transaction causing a subsequent action to correct or add data, latency problems between the creation of master and transaction data and its consumption by reporting and downstream applications.

How do you convince companies that the data quality lifecycle includes data death? The GDPR instructs it… but does anyone put it into practice?

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Spain Edition 20th Oct 13:15 CEST – 6:15 AM MEX

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