How big data can lead to bad decisions
With big data, decision-makers can be more confident, but does that mean they'll be more accurate or reliable? It's doubtful, warns Bob Lewis, in his always-illuminating style. There are three challenges to preventing big data from introducing "grave dangers to a company's decision-making health," he writes in a column at InfoWorld.
The first challenge is quality assurance. It's not hard these days to come up with statistics that look impressive but are erroneous, even when users understand the tools they're using. Big data technologies require less planning and analysis than "old-school IT reports," and it's all-to-easy to misunderstand the data.
"In the pre-big data era, IT delivered carefully constructed data views to users and user analysts, reducing the risks of misunderstanding the data being analyzed," Lewis writes. "With big data, this responsibility is shifting at the same time data structuring is becoming more ad hoc and therefore easier to get wrong."
The second danger big data presents is the growing potential for false correlations. Statisticians understand the many nuances of correlations and random chance, but laymen with sophisticated BI tools at their disposal may not.
Finally, the data that is used in big data analytics may not have been gathered for that purpose and may lead to specious conclusions. Again, statisticians recognize that legitimate analysis requires a carefully designed data collection process.
- see Bob Lewis' post at InfoWorld