The short answer: The forgotten layer of data strategy is philosophy: the concepts about how data gets used and turned into products. Some treat employees and customers as equal partners; others treat them as tools and markets to be exploited. Without understanding that, a data leader ends up with a tech stack strategy, not a real strategy.
Key takeaways
- Philosophy is one of the foundational pillars of data, and it is often skipped.
- How data is used and turned into products reflects a company's underlying philosophy.
- Some philosophies treat people as equal partners; others treat them as tools and markets.
- Without it, a data strategy becomes a tech stack strategy.
The forgotten layer
There is often a layer that gets forgotten when assessing a data strategy. That is the philosophy layer.
I am doing training and teaching people about data. Starting from scratch, I realized that if I do not teach philosophy concepts and how they relate to data, then I am not teaching one of the foundational pillars of data.
Two stances toward people
Concepts around how data gets used and turned into products are a fundamental piece of how a company uses data. There are concepts which treat people (employees and customers) as equal partners in the process. And there are other concepts which treat those same people as tools to be used and markets to be exploited.
I can tell you how a company will develop just from understanding its underlying philosophy of data.
| People as partners | People as tools or markets |
|---|---|
| Employees and customers are equal partners in the process | Employees and customers are used or exploited |
Questions to ask about your own company
These questions are suggestions drawn from the two stances above.
- Do the people whose data we use benefit from how we use it?
- Do employees help shape the data products they will use, or are the products imposed on them?
- When a metric and a person's interest conflict, which wins?
Why data leaders need it
That is why it is important, as a data leader, to understand philosophy. Without it, when you are asked to come up with a data strategy in a competitive market, you really cannot. You just end up developing a tech stack strategy, not a real strategy.
Get out there and learn economic, life, societal, and individual philosophy concepts, and it will make you a better data leader.
I have spoken on this theme: see Press and Podcasts for "Data Philosophy" and "Data x Philosophy". The Phronetic AI Framework applies the same idea to AI design: enhancing human judgment instead of replacing it.
Frequently asked questions
Why does philosophy matter for data strategy?
Concepts about how data gets used and turned into products are a fundamental piece of how a company uses data. Without understanding them, you end up developing a tech stack strategy, not a real strategy.
What philosophies should a data leader study?
Economic, life, societal, and individual philosophy concepts.
Is this theoretical?
It came from teaching people about data from scratch: leaving out philosophy meant leaving out one of the foundational pillars of data. I can tell you how a company will develop just from understanding its underlying philosophy of data.