Expert Dan Lodin discusses the 5 Biggest Big Data Myths of 2013 in an Information Week article. I find these really helpful in building a business case for data quality.
Myth 1: Data quality is not important
At the top of the list is the myth that running poor-quality data through an in-memory database will produce a suitable answer.
Dan reflects that any analytics requires quality data, irrespective of the size of the data set. A good Big Data analytics tool acting on poor quality data will simply create “fast trash”, he warns ‘ – information quickly produced but with little substance.
Myth 2 – More data is better
Another myth he uncovers is that of volume – the myth that more data is better suffers from the same caveat as myth number one. Otherwise, Dan warns, you will simply increase the complexity of your environment, reducing your ability to get good answers.
Indeed, big data quality matters: It’s not just about the volume, but the veracity of the information.
But remember, it’s not about quantity. Quality trumps quantity in the quest for meaningful insights.
Dan’s recommendations, that companies approach Big Data with a data management plan and that data quality is important, are in line with recent studies that show that companies that have implemented data governance get better results from Big Data than those that approach big data analytics in a chaotic or ad-hoc way.
Data Quality Matters 
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