You often hear the argument that data quality isn't a major concern in big data projects, since the volume of information being analysed can smooth over any problems. However, Forrester researcher Michele Goetz points out this doesn't mean IT shouldn't work hard to ensure the data is as accurate as possible.
Quality control picture from Shutterstock
In a blog post, Goetz notes that "analysis can fill gaps by emphasising pattern recognition over master data". That's especially common in marketing, where data sources are often sketchy.
Despite that, it would be a mistake to use this as an excuse to minimise data quality efforts, she suggests:
IT still needs to support and certify data quality in the access and integration of data. It isn’t a question of good enough data, it is about data quality efforts that matter to outcomes.
Good point. Hit the full post to read more, and check out our top 10 rules for working with big data for more insights.
Data quality and data science are not polar opposites [Forrester Blogs]