The One Book On Data Strategy That's Half Good
“Hey, I’m thinking of writing sth. on data strategy.” As soon as I drop that sentence with people in the space, I get an answer like this: “Drop me a link!!! I’m struggling with people not having an idea about data straight right now. It’s NEEDED!”
And I get it; the interest is exploding.
So today, I want to introduce you to one book on data strategy I sorta like. The book is “Data Strategy - How to profit from a wortld of big data, analytics and artificial intelligence” (there’s little to nothing specific about AI in it)
So, let me introduce you to the book in increasing depth.
The X version
Data, AI and the IoT are growing at an exponential rate, and as such, are changing our business world at a rate we cannot comprehend. There is gold in data, but you have to work hard to mine it.
The one-paragraph version
To mine the new possibilities in data, you have to understand two fundamentals:
Data possibilities arise mainly in six big business areas, each requiring a different set of skills to solve.
All data activities have to be objective, measurable and tied to your company strategy, otherwise they will fail to unleash the potential.
The summary
The unprecedented growth of data, AI and the IoT is changing every industry, and thus every company. Every company is a data business, whether the business realizes it or not. Every business has vast technologies and vast lakes of data at their fingertips. Competitors will use those, so either become a data business, or go out of business.
There are five areas of business that you could potentially focus your data efforts on - focus, like in choose one:
Improving decision-making with data
Use data to understand your customers
Use data to create more intelligent products & services
improve your business processes
sell your data (or a derivative of it)
To do so, you’ll need to brainstorm, think freely. Because data isn’t like software, it’s different, you might have some, it works great when you combine it in unusual way with other assets you might not have. If you don’t think outside the box, you won’t get there.
But then, once you have ideas, you’ll need to bring them down to your main business strategy and tie them in well, discard everything that doesn’t fit, and create a uniform strategy that aligns.
Who/ How should read this book?
Read this book to broaden your horizons.
Who should read this book: product managers, leaders, and managers.
There’s a clear recommendation on how to read this book: skim through it! Try to get 1-2 points from every page, but there’s no need to read all the words.
But, I would like to provide you with some guidance on the points I think are important or disagree with.
What this book is not
Important points for data strategy
When it comes to improving your decision-making with data, please keep it simple! Have simple dashboards and a list of curated dashboards owned by responsible people who know everything about them.
Start all data-supported decision-making dashboards by discovering the key business questions your business wants to answer. Never start with the technology.
Don’t forget that while you have five different paths to success, your task is to find the most promising one and focus on it!
“Optimizing operational processes is about implementing data-based decision-making in every operation of your business.”
Selling the data you have or a derivative of it is more common than you think! X owns part of a start-up that still sells API-based data analysis of tweets.
User-generated data is always more valuable than you think. For someone.
Companies also sell data to their own customers. X is selling me analytics data on my tweets.
Brainstorming, or thinking outside the box, is necessary to create great data products (but not sufficient).
You must link your data initiatives to your strategic goals.
Data initiatives are different than software projects. You need to take care of data governance and technology needs.
There’s no point in having data alone. It’s just a raw mess. Instead, you need to process it and turn it into information, insights, and actionable context.
Data is often most valuable when you combine it with existing data you might source for free or buy. #alternativedata
Points I disagree with
Right on the cover, the author suggests that the data you have is the asset you should focus on. However, from my perspective, I don’t see why the data you already have as a company is the most suitable one for solving any problem. Instead, I think you should always consider the two classes: (1) the data you can gather from now on and (2) the data others have gathered as potential solutions or parts of solutions. In that sense, “data is an asset” might be a partially true statement, but it won’t point you in the right direction.
This book defaults to dashboards and BI tools for “making your decision-making smarter.” While I have a whole rant on why BI tools aren’t great at what they do, I think it suffices to say that you shouldn’t default to anything. If you want to make your decision-making process better, you need to record it to see which parts you can automate and where you can do something with data, be that inside a CRM system, with an auto-pilot, or, in the worst case, with a dashboard.
Beware that chapters 6-7 are not actionable at all but might still broaden your horizon.
This book tries to suggest that by brainstorming ideas, you’ll end up with a unified data strategy. I’ve yet to see this implemented successfully. Instead, I advocate for either a top-down approach, starting with the business strategy, or a combined approach. But in my experience, going bottom up never has worked.
Chapter 14 is about building up data competencies inside your company. That’s important, but I think the gap between demand and supply is ever widening and as such, deserves more than a mere chapter. The best way to build up the competency is by having business demand through a good data strategy.
Great read! I especially like the bullet, "There’s no point in having data alone. It’s just a raw mess. Instead, you need to process it and turn it into information, insights, and actionable context."