This is the Three Data Point Thursday, making your business smarter with data & AI. Let’s dive in! Actionable insights Understand the Essence of a Good Data Strategy. Recognize that a strong data strategy is more than just a well-documented plan. It should address specific challenges, leverage unique strengths, and clearly articulate how data will drive competitive advantage or operational excellence.
Thank you for sharing your thinking process! It was a pleasure to read your breakdown, supported by familiar terms. I absolutely agree with your opinion of “Good Strategy Bad Strategy”. After reading the book, your world will never be the same, considering how often we can stumble upon "strategies" in the wild. Actually, the word "fluff" is strongly associated with the book, so its presence in the title prompted me to read the article in the first place.
> GX: valuation of close to $300m
This is astonishing! I remember cringing after trying GX as an open-source tool for writing data tests for my use case in 2020 :). Apparently, they have evolved quite a lot since then.
> We went to work and built out a decent data infrastructure. We hit our objective, and the data was used for lots of different use cases, but all of them focused on internal reporting. ... The goal of bringing value to the network participants through this data was good, but it was misaligned with the objective of building out a great data collection engine.
This part is a bit confusing. The problem I see here is that you first built an infrastructure hoping that it would create value through data and AI, without having specific high-value use cases in mind. So, technology was driving the strategy. If there were thought-out high-value use cases, then building an infrastructure would have been necessary to support them. So, it wouldn't be about misalignment, but about failing to kick off the implementation of the use cases based on the infrastructure.
> I used a technique called impact mapping to draw up a big tree of the company, the departments, and where we could create the most value. I quickly identified one department, the sales department, as being critical.
> ...
> So, instead of keeping our broad vision, I switched.
> ...
> Focus on the sales department
I believe it's more powerful to present a data strategy not in terms of focusing on a particular department, but on relevant business challenges, where we can operate with critically important metrics requiring improvement. This way, it can drive cross-organizational changes when necessary, as sales depend on other departments as well. Anyway, you presented a nice example of the power of guiding policies.
> ... I wrote down & discussed with the team was to create a strategy. Well, we set out to create a vision, we decided what our job inside the company was to “improve decision-making inside the company.”
My understanding that you didn't even have a bad strategy at the beginning. You just tried to do something good based on the general purpose of the data team (which you call vision). However outlining the purpose / JTBD / principles of a data organization is an important component.
Thank you for sharing your thinking process! It was a pleasure to read your breakdown, supported by familiar terms. I absolutely agree with your opinion of “Good Strategy Bad Strategy”. After reading the book, your world will never be the same, considering how often we can stumble upon "strategies" in the wild. Actually, the word "fluff" is strongly associated with the book, so its presence in the title prompted me to read the article in the first place.
> GX: valuation of close to $300m
This is astonishing! I remember cringing after trying GX as an open-source tool for writing data tests for my use case in 2020 :). Apparently, they have evolved quite a lot since then.
> We went to work and built out a decent data infrastructure. We hit our objective, and the data was used for lots of different use cases, but all of them focused on internal reporting. ... The goal of bringing value to the network participants through this data was good, but it was misaligned with the objective of building out a great data collection engine.
This part is a bit confusing. The problem I see here is that you first built an infrastructure hoping that it would create value through data and AI, without having specific high-value use cases in mind. So, technology was driving the strategy. If there were thought-out high-value use cases, then building an infrastructure would have been necessary to support them. So, it wouldn't be about misalignment, but about failing to kick off the implementation of the use cases based on the infrastructure.
> I used a technique called impact mapping to draw up a big tree of the company, the departments, and where we could create the most value. I quickly identified one department, the sales department, as being critical.
> ...
> So, instead of keeping our broad vision, I switched.
> ...
> Focus on the sales department
I believe it's more powerful to present a data strategy not in terms of focusing on a particular department, but on relevant business challenges, where we can operate with critically important metrics requiring improvement. This way, it can drive cross-organizational changes when necessary, as sales depend on other departments as well. Anyway, you presented a nice example of the power of guiding policies.
> ... I wrote down & discussed with the team was to create a strategy. Well, we set out to create a vision, we decided what our job inside the company was to “improve decision-making inside the company.”
My understanding that you didn't even have a bad strategy at the beginning. You just tried to do something good based on the general purpose of the data team (which you call vision). However outlining the purpose / JTBD / principles of a data organization is an important component.