Good morning, it’s まさきん.
Starting today, I’m adding a new category: Marketing. I want to gradually write about the books I’ve read and the things I’ve thought about while working as a digital marketer.
First up is a book review: Competing on Analytics, by Thomas H. Davenport and Jane G. Harris.
It started with a question: “Does analysis actually make money?”
People sometimes ask me this at work. Does analysis itself generate revenue?
Honestly, analysis alone doesn’t directly create sales. But I’ve long felt that companies that skip analysis will struggle to survive going forward.
What the book covers: why analytical power becomes a competitive edge
The book covers companies across many industries, not just internet companies like Google and Amazon, but finance, manufacturing, retail, and services too.
These are companies using data and analysis as the backbone of their management to pull ahead of competitors. The book lists several conditions for analytical power to become a competitive advantage.
- Hard to imitate
- Distinctive
- Adaptable
- Substantially better than competitors
- Continuously evolving
That all makes sense, but I also felt that not many companies actually pull this off as a matter of course.
What makes the book interesting is that it points out there’s no single path companies take to become analytics-driven. Some, like Google and Amazon, were data-centric from the start. Others, like Walmart or financial institutions, strengthened their data practices out of necessity. Some sports teams and manufacturers built up their analytical edge in stages. The starting points vary, but the book’s view is that they all end up somewhere similar.
Companies are shifting their decision-making from gut feeling to data. The same idea might apply to household fixed costs. I’m thinking of running the numbers on my own phone bill for once.
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Two paths: the “fast path” and the “slow path”
How does a company transform into one that wields analytics as a weapon? The book introduces two routes.
One is the “fast path,” where executives trust data over gut instinct from the outset and spare no investment to make it happen. Change spreads across the whole company relatively quickly, like boarding an express train.
In reality, though, not many companies are in that position. Most follow the other route: the “slow path.” It starts from a small unit, like a marketing department, builds results through steady accumulation, and gradually spreads understanding to other departments based on those results. It takes time and the risk of failure isn’t small, but the upside is a smaller initial investment. Whichever path a company takes, once analytical influence spreads company-wide, the book says what comes next is the same: gather data, build up talent, and keep producing output continuously. I think that steady persistence is exactly what analytics-driven companies have in common.
Three types of people behind analytical power
Another point that stuck with me is how the book divides the people needed to make use of analytics into three layers.
The first is the management team. They should understand the importance of data and, ideally, have some working knowledge of individual analytical methods. The second is professionals: people with specialized training and advanced data skills. Outsourcing is an option these days, but the book warns that if you outsource the core of your analytical capability entirely, your competitiveness as a company ends up borrowed rather than owned.
The third group, actually the largest and considered the most important, is the “amateur analysts.” The idea is that even without advanced analytical skills, it’s essential for people on the ground to understand data definitions and handling correctly and consistently.
This really resonates with my own work experience. I’ve seen the exact same data interpreted completely differently by different teams more than once. Everyone’s looking at the same numbers, yet one team reads it as a good sign while another reads it as a warning sign. The cause is usually that data definitions and assumptions weren’t properly shared on the ground. I feel like getting amateur analysts on the same page might have a bigger impact than building a sophisticated analytics platform, even if it’s less glamorous. I also wrote about this “shared-language skill” in the translator-like skill data analysis work requires.
Reading it again now, the core still holds up
This book came out around 2010, so it’s over 15 years old now. But I feel the core of its thinking still holds up well today.
Lately, AI’s role has increasingly been framed around “value creation.” Generative AI has automated a lot of the actual work of processing data, compiling it, and writing reports. Because of that, what’s growing relatively more important is the ability to identify the right KPIs and find the winning angle in the data. The idea isn’t just “solving problems” but the hypothesis-building work of deciding what to treat as the problem in the first place, and that’s said to be the part of the job that stays human. Marketing mix modeling might be another example of an old technique getting a fresh look today.
Even as analytical tools evolve, people are still the ones making the final decisions. How you interpret the numbers and turn that into the next move is, I think, the core of the job, then and now. Whether it’s the fast path or the slow path, in the end both are about shaping how people engage with numbers, so they’re really the same road.
Wrap-up
I almost skipped this book for being old, but it still had plenty to teach me on this read. The idea of making analytical power a competitive edge might apply not just to running a company, but to how you approach reviewing your household finances too.
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