A data-driven organization acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape.
There are many ways to assess whether an organization is data driven. Some like to talk about how much data they generate. Others like to talk about the sophistication of data they use, or the process of internalizing data. I prefer to start by highlighting organizations that use data effectively.
I’ve found that the strongest data-driven organizations all live by the motto “if you can’t measure it, you can’t fix it” (a motto I learned from one of the best operations people I’ve worked with). This mindset gives you a fantastic ability to deliver value to your company by:
- Instrumenting and collecting as much data as you can. Whether you’re doing business intelligence or building products, if you don’t collect the data, you can’t use it.
- Measuring in a proactive and timely way. Are your products, and strategies succeeding? If you don’t measure the results, how do you know?
- Getting many people to look at data. Any problems that may be present will become obvious more quickly — “with enough eyes all bugs are shallow.”
- Fostering increased curiosity about why the data has changed or is not changing. In a data-driven organization, everyone is thinking about the data.
It’s easy to pretend that you’re data driven. But if you get into the mindset to collect and measure everything you can, and think about what the data you’ve collected means, you’ll be ahead of most of the organizations that claim to be data driven. And while I have a lot to say about professional data scientists later in this post, keep in mind that data isn’t just for the professionals. Everyone should be looking at the data.
[data science teams]
The silos that have traditionally separated data people from engineering, from design, and from marketing, don’t work when you’re building data products. I would contend that it is questionable whether those silos work for any kind of product development. But with data, it never works to have a waterfall process in which one group defines the product, another builds visual mock-ups, a data scientist preps the data, and finally a set of engineers builds it to some specification document. We’re not building Microsoft Office, or some other product where there’s 20-plus years of shared wisdom about how interfaces should work. Every data project is a new experiment, and design is a critical part of that experiment. It’s similar for operations: data products present entirely different stresses on a network and storage infrastructure than traditional sites. They capture much more data: petabytes and even exabytes. They deliver results that mash up data from many sources, some internal, some not. You’re unlikely to create a data product that is reliable and that performs reasonably well if the product team doesn’t incorporate operations from the start. This isn’t a simple matter of pushing the prototype from your laptop to a server farm …