Data Science vs. Business Intelligence:
What's the Difference?
The terms "data science" and "business intelligence" seem to be used a lot in connection with big data, but they're really very different disciplines. Experts say data science is all about predicting the future, while BI involves producing static reports.
"Traditional BI engineers are effectively reporting information as is, even if they're reporting trends and standard deviations away from the norm," says Andrew Dempsey, director of DVD BI and analytics at Netflix. "They aren't really discovering new nuggets of information. The data is what it is."
But with data science, there's an element of mystery. For example, Netflix looks at historical data "to identify why someone is more or less likely to churn because of their behavior," Dempsey explains. "There's more uncertainty there because on an aggregate level, a lot of people may have similar viewing habits, but on an individual level, everyone is different."
Another key difference between the two disciplines has to do with the data itself.
First, there's the sheer volume of data. "With so much data, you need to assimilate it to look at the exceptions, rather than the reports," says Biogen Idec CIO Greg Meyers. The pharmaceutical manufacturer, he says, continually reviews data from signals throughout the manufacturing process to detect when events are out of tolerance levels. When an anomaly is detected, a different operating procedure is triggered. "It's all about trying to make sure the process of how we manufacture is as controlled as possible," Meyers says. "We've matured our analytics process by looking at data across batches so we look at trends to reduce the variability of certain things."
Another challenge is dealing with the variability of big data.
Josh Williams, a data scientist at Kontagent, notes that "in classic BI systems, you usually have highly structured data -- things like customer profiles. You come up with an analysis by correlating that data and running regressions on it."
In today's big data environment, in contrast, "you have a mess of complex data and you have no idea how the features you may be looking at -- the input factors -- relate to the output," Williams says. The upshot is that data science is "much more exploratory. It's easier to shoot yourself in the foot. You have to be much more rigorous. It's much more difficult to do the analysis, which is why there is so much more research around machine learning," he adds.