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Summary:

In his talk at Structure: Data, Quid’s Sean Gourley talked about the meaningful differences between “data science” and “data intelligence.” While one is concerned with correlations, the other is concerned about solving problems.

Structure Data 2013 Sean Gourley Quid
photo: Albert Chau

For all our talk about big data saving the world and changing lives, it so far has had more impact and gotten more press for lessor achievements, according to Sean Gourley of Quid. Gourley spoke in New York City Wednesday at the Structure:Data 2013 conference on the difference between data science and data intelligence.

Gourley illustrated this difference with children’s cereal. He pointed out that data science is used today for improving advertising, such as making better packaging and pricing strategies. Meanwhile, data intelligence could try to address the issue of child obesity.

“The naive empiricism of data science goes for the low-hanging fruit: it measures what can be easily measured and changed,” Gourley said.

Part of this is because of where the ideas of data science came from: Facebook and LinkedIn (lnkd). He noted that Facebook is the largest quantification society has ever seen, but it’s designed to put people in a pre-defined vector and test how to best advertise to them.

However, data intelligence will solve big problems, such as how the number of troops we send to Iraq might change the nature of the conflict. Or it could solve strategic problems that don’t require a prediction, but rather require insights.

He outlined the differences between data intelligence and data science for the audience. For example, data intelligence asks bigger questions and builds models to solve for them, as opposed to asking for predictions and equations. Data intelligence deals with messy, small data while data science handles big data.

Check out the rest of our Structure:Data 2013 coverage here, and a video of the session follows below:


A transcription of the video follows on the next page

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  1. buzzinbusiness Thursday, April 4, 2013

    Very good explanation of the processes. I would go one step further and use a solution methodology; Analytical Data Intelligence. In order to extract the maximum value from the data you need the power of all 3.

    The power behind our predictive analytics is based on thorough adaptability of data models and self-validating and portability characteristics along with powerful variables, offering businesses a way to manage and analyse data effectively.

    Before data can be effectively used for business analytics, customer relationship management or even relatively simple marketing campaigns, the foundation data needs to be cleansed, standardised, validated and enriched.

    Intelligence is the translation of insights from customer and business data into profitable knowledge that is actionable and that focus on enabling strategies and nurturing operation excellence – transforming Intelligence from a conventional passive process to a results orientated action solution.

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