Google and the Transformation of Marketing

Tom Davenport, IIA Research Director

I spoke last fall at the Google Analytics Summit in Mountain View, and couldn’t help being impressed with the pace of change at both Google and the marketing profession in general. As an aside, it struck me that Google today is much like AT&T in its prime

2014 IIA Symposium: We’re Really Hummin’ Now!

Tom Davenport, IIA Research Director

Analytics have gotten big and strategic in many organizations, to the point where analytical capabilities have the attention of senior management. Here are a few semi-random examples from the many analytical leaders and practitioners who attended IIA’s 2014 Winter Analytics Symposium earlier this month.

For Better and Worse, Big Data Grows Up

Tom Davenport, IIA Research Director

Big data is maturing and developing more value to large companies. So what’s the downside? The issue is that with this grown-up resource comes grown-up responsibilities. The phrase “governance for big data” is coming up more and more in large organizations. Others include “stewardship” and “control.”

Industrial-Strength Analytics With Machine Learning

Tom Davenport, IIA Research Director

A short while ago I wrote a post suggesting that human analysts would not be disappearing anytime soon. As important as hiring good analytical people, however, is taking advantage of all of the current possibilities for improving their productivity in analytical work. Machine learning tools and platforms are the most promising approach to creating analytical models at the pace required by big data.

Forecasting the Future of Analytics: 2014 Edition

Tom Davenport, IIA Research Director

Each year, IIA gathers its faculty and leadership to look at the evidence and personal experience and provide a perspective on what the future holds in the world of analytics. Last year, we predicted that data visualization tools would become more prevalent, suggested that personalized customer analytics would transcend product driven analytics, and posed that the lines between data scientists and other analytics professionals would blur.

Centenarians Get Frisky with Big Data

Tom Davenport, IIA Research Director

Silicon Valley companies may have pioneered the use of big data, but it’s these nimble giants that are taking things to the next level. They’re showing that big data can be a driver not just of online business, but of every kind of business.

Organizing Analytics and Big Data

Tom Davenport, IIA Research Director

Many companies are attracted to small “centers of excellence” (CoEs) that put a small number of people in a central coordination role, but leave the great majority of quants to fend for themselves in highly decentralized environments. This is appealing if you want to apply a gloss of coordination to a largely uncoordinated activity, but I don’t think it suggests a strong commitment to a well-organized analytical capability.

Will Human Analysts Ever Go Away?

Tom Davenport, IIA Research Director

Smart organizations build smart humans into their analytical processes from the beginning, in key functions and business units. They work closely with senior executives to explain analytical results, pose new questions of the data, and be present at meetings where analytics and data are discussed.

C-Level Help for Big Data and Analytics

Tom Davenport, IIA Research Director

There are many other examples of organizations that have successfully folded analytics into other C-level jobs. At Procter & Gamble Co. , CIO (and head of Global Business Services) Filippo Passerini has done a great job of bringing analytics to the executive suite.

Why Large Firms Don’t Need ‘Big Data Departments’

Tom Davenport, IIA Research Director

What do big companies do with Big Data? Jill Dyché from SAS Institute Inc. and I have just finished a study of over 20 companies on this topic (you can download the full report here). Much of what has been said about Big Data until now has come from online firms like Google Inc., eBay Inc., LinkedIn Inc., and Facebook Inc., and startups in data-intensive industries. These companies were built around Big Data from the beginning. No integration with existing architectures or processes was necessary. Big Data could stand alone, Big Data analytics could be the only focus of analytics, and Big Data technology architectures could be the only architecture.

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