Skip to content

Small Data: A book review by Bob Morris

Small DataSmall Data: The Tiny Clues That Uncover Huge Trends
Martin Lindstrom
St. Martin’s Press (February 2016)

How to mine for and then integrate Small Data with Big Data to create Rich Data

Frankly, I am dismayed by the widespread and durable abuse of terms such as “Big Data” and “Small Data.” Heaven knows how much damage has been done by well-intentioned people who are, nonetheless, understandably confused. In his latest book, Martin Lindstrom makes a valiant effort to eliminate some of the fog complicating all this. He offers dozens of examples of “tiny clues that uncover huge trends.” He also pinpoints the specific limitations and inadequacies of Big Data.

In the Foreword, Chip Heath nails it: “In today’s business environment, Big Data inspires religious levels of devotion and Martin Lindstrom is an atheist…Big Data doesn’t spark insight. New ideas typically come from juxtaposition — combining two things that previously haven’t been combined. But Big Data typically lives in databases that are defined too narrowly to create insight…Big Data is data and data favors analysis over emotion. It’s hard to imagine data capturing many of the emotional qualities we most value: beautiful or friendly or sexy or cute. If data fostered better emotional decisions, then accountants, not poets, would be the cultural prototype for great lovers…In sum, Big Data has problems, and Martin is successful at showing how Small Data is essential to overcoming them.”

These are among the several dozen passages of greatest interest and value to me, also listed to suggest the scope of Lindstrom’s coverage:

o Culture (Pages 9-11, 14-17, and 30-32)
o Food kitchens (11-12, 16-18, 21-23, 84-85, and 100-101)
o Russia (21-35, 43-46, and 63-64)
o Shopping centers and malls (35-43 and 47-48)
o Lowes Foods (47-50, 62-73, 224-226, and 232-233)
o Desire (49-50 and 130-131)
o United States: Color (61-65, 67-69, and 71-72)
o India: Mothers-in-law and daughters-in-law (78-90 and 93-94)
o India: Color (81-86 and 88-96)
o Cereal food packaging (92-96)
o Weight and weight loss (98-100 and 107-117)
o Jenny Craig (107-117)
o Aspiration (131-137)
o Religion and brands (139-141)
o Fashion and adolescent girls (148-168)
o China: Automobiles (179-180)

One of Lindstrom’s several strengths is a conviction he describes this way: “If you want to understand how animals live, you don’t go to the zoo, you go to the jungle.” When conducting research, he applies the skills of an anthropologist in search of “clues” to “an unmet or unacknowledged desire that forms the foundation of a new brand, product innovation, or business…In search for what I call small data, almost nothing is off limits…My methods may be structured, but they’re also based on a whole lot mistakes, and trial and error, and faulty hypotheses that I have to toss out before starting over again…The smallest detail, or gesture, may become the key to unlocking a desire that men, women, and children (and in some cases, the culture itself) didn’t know they had. I look for patterns, parallels, correlations and, not least, imbalances and exaggerations.”

He explains his “7C methodology” in the final chapter. Suffice to say now that the primary purpose of his research (best viewed as a series of investigations) is to identify what may view as insignificant “clues” or “keys” that prove to be, instead, significant revelations that help to explain human behavior.

When concluding his latest book, Lindstrom observes, “My role – the role of anyone trying to make sense out of small data – is to understand not just one single personality, but all of them. Which is why in the end the secret behind any ethnographic research will never be found in any methodology, even mine. It begins with yourself. Who are you? What are you like when you’re by yourself?”

As for Martin Lindstrom, his natural state is to be in relentless examination of small data, “the greatest evidence of who we are and what we desire.”

Posted in

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Scroll To Top
%d bloggers like this: