Looking Beyond Big Data: 9 Quotes on Putting Analysis into Action

Josh Zywien

February 21, 2014

By this point, it’s probably safe to say your company’s execs are all too familiar with the siren song of big data – the abundance of information at your fingertips, the unprecedented insight into customer preference and behavior, the veritable golden age of hyper-relevant marketing and better-informed decision making to come. But as the quotes below emphasize, knowing really is only half the battle. The truly difficult and critical part is learning how to distinguish between data that is interesting vs. data that is important — and determining how to best leverage customer information into actionable insights and results.

Looking Beyond Big Data: 9 Quotes on Putting Analysis into Action

“My view of big data is as a supporting technology for data science. And data science is a supporting craft for doing better decision making.”

— Foster Provost, co-author of Data Science for Business

Source: “Is Bigger Really Better?”

“Gathering data is important, but it’s crucial to be able to differentiate between data that’s merely interesting and data that actually has leverage.”

— Bruce Ernst, VP of Product Management at Monetate

Source: “Interesting Data Can Be a Big Swing and a Miss”

“When it comes to data, I like to think of the golden question: ‘What action will I take once I have this data?’ If the answer is no action, it’s generally a bad idea to even consider looking at the data.”

— Hiten Shah, co-founder of Crazy Egg and KISSmetrics

Source: Hiten’s AMA at Inbound.org

“No company hires Reporting Squirrels. Everyone hires what they believe are Analysis Ninjas. Reporting Squirrels spend 75% or more of their time in data production activities. Analysis Ninjas spend 75% or more of their time in analysis that delivers actionable insights.”

— Avinash Kaushik, author of Web Analytics 2.0

Source: “Empowering Analysis Ninjas? 12 Steps to Identify a Data Driven Culture”

“True understanding of your buyers does not come with the raw data that you collect. You need to analyze your insights in the context of your buyer’s journey — from being unaware of your product and its potential benefits to being a devoted customer.”

— Tien Anh Nguyen, Director of Market Insights at OpenView Venture Partners

Source: “Putting Buyer Personas into Context”

“If you’re busy explaining the data, you won’t be busy acting on it.”

— Alistair Croll and Ben Yoskovitz, authors of Lean Analytics

Source: Lean Analytics for Intrapraneurs on SlideShare

“Data are just summaries of thousands of stories — tell a few of those stories to help make the data meaningful.”

Chip and Dan Heath, authors of Switch

“You don’t need to predict accurately to get great value. Organizations, by making predictions per person that are a good bit better than guessing, actually play the numbers game better.”

— Eric Siegel, author of Predictive Analytics

“Instead of asking, ‘How can we get far more value from far more data?’ successful big data overseers seek to answer, ‘What value matters most, and what marriage of data and algorithms gets us there?'”