Data & Analytics Maturity Canvas

This Canvas is my investigation into the topic of Data & Analytics maturity assessment.

It includes:

• The editable MIRO template of maturity map with 135+ elements (bricks) by 25 key factors (in rows) and 3 maturity levels (in columns).
• 4 Patterns - my very subjective view on data and analytics maturity benchmarks - showing the route for different types of companies: what the target zone should be, what could be optional, and what is not needed.

My idea is not just to create another sophisticated tool for assessing data maturity, but rather to develop a visually appealing and user-friendly tool that enables quick self-assessment and planning exercises within a data team.

Why I've created this:

Every system is chaotic, in fact. Data & Analytics are no exception. Our goal is to structure and simplify them to enhance understanding and manageability.

The Data Maturity Model serves as an example. You know and use models from Gartner, CMMI, IBM, TDWI and more.

But maturity assessment exercises for in-house data leaders can often appear speculative. I also used to consider this as typical consulting nonsense.

🤦‍♂️ I identify at least 4 main issues:

1. Unclear: Understanding the logic behind factors can be challenging, or they may not be shared at all.
2. Incomplete: Many assessments focus solely on #datagovernance, neglecting analytics, or culture, or AI etc.
3. Either too high-level and overly simplistic, lacking significant value, or too complex for operational teams to grasp.
4. Not visually appealing or technological: While you might input data, it's challenging to visualize the overall picture, lacking an engaging component.
   

👨‍💻 So, I'm attempting to create something new (another one? Yes, I know, sorry).

The main difference lies in viewing maturity assessment as a facilitation — a visualthinking exercise for the data leadership team, not a consulting black box.

The objective is to gain a clear picture of:

• The current state (where we are);
• The target picture (where your type of company should be to be data-driven and what trends you have to ignore);
• Specific initiatives we can undertake to bridge the gap.
  

Data leaders - do not be depressed!

Vendors with their hyped features often impose excessive technologies upon us, even for processes that your company may not need or may not be at the necessary level for.

While aspiring to become a data-driven company sounds like a universal goal, its implementation varies greatly depending on the size and industry of the company.

A low level of maturity of the data infrastructure in a tech company developing digital products may be advanced for the average traditional enterprise. At the same time, reasonably complex reporting delivery practices in a traditional enterprise not be needed for a company centered around digital products.

😒 This can lead to depression and burnout for data leaders.

Therefore, I designed this tool to try to address this issue.

Feedback

I'm aware of how controversial some of these assumptions may be, I will continue to reflect on how to make it better.

So I'd truly appreciate your feedback and any concerns you may have.