TL;DR
In this post, I walk through how data maturity is directly related to analytics maturity. Bottom line: to even think about analytics maturity, a company has to first focus on its data.
A quick recap of Part 1
In Annika’s post Data Maturity, Part 1, she presented this matrix, where we not only think about a company’s size but how data mature they are:
To be honest, when Annika first brought this up to me, it kind of blew my mind. It’s so simple and so clear, and it codifies that company size doesn’t dictate data maturity, like, at all.
Rather, data maturity is therefore a measure of how advanced a company’s people, tools, and processes are with respect to data specifically. It’s an important aspect to consider for any company.
The analytics maturity model
I’ve been in the data and analytics world for a long time now 👵🏻🤓 I’ve worked with small companies where nearly everyone used data to make decisions, mid-market companies in the throes of a digital transformation beginning to include data in their processes, and large, complex, established companies where they’d love to be data-driven but it’s a distant dream buried under spreadsheets.
The data maturity model immediately brought to mind Gartner’s analytics maturity model, which has become the stuff of legend in the analytics world. It basically looks like this:
Put simply, it’s harder to do things that are of higher value when it comes to analytics. Shocking, I know! 😉 But seriously - most companies can do really (low-value, easy) basic analytics in the form of reporting.
But to get to the place where you can do predictive or prescriptive (high-value, hard) data science and run your business based on data? Dude, that’s tough! And it doesn’t just mean you need genius data scientists and operationalized data, it means you need to treat data as a valuable primary product within your organization.
Data maturity is the precursor to analytics maturity
The way this overlaps with the data maturity model is that in order to do more valuable (and harder) analytics, you must be a more mature data organization. With low data maturity, your organization would only be able to handle basic analytics - the “hindsight” part. As you become more mature in data, you’ll have the capability to be more mature in your analytics as well - moving into the “insight” and “foresight” realms.
First, focus on your data.
For years I have been annoying people who want to bring data science into their orgs by telling them that “90% of the work is the data.” I truly believe that. Without having reliable, clean, production-quality data, the analytics and data science is meaningless.
So what does that mean in practice? An organization should focus first on assessing and improving their data maturity so that users can rely on having clean, stable datasets. This means having a data team who focuses on producing accessible and reliable data in a format that facilitates analytics; they should actively work to provide data as a first-class product to the organization. Only then will you be able to truly embrace analytics because the data will be in a state where it can reliably support analytics.
And now we come full circle
The bottom line here is quite simple, yet it’s often not clear within the complexity of modern organizations: To be mature in analytics, a company must be mature in data 💥This is how you ultimately get value from your data through analytics - by focusing on the data first.
But wait, there’s more!
There is so much that comes out of these two models - data literacy, well-organized data teams, technology choices, processes, etc. We will get to all of that, and soon!
In the meantime, make sure you’re subscribed to The Sequel to hear more! ⭐️