Traditionally, when assessing the maturity of quality management within an organization, we focus on several core areas:
- Leadership and strategy
- Culture and people (engagement)
- Processes and standardization
- Measurements and KPIs
- Customer orientation and continuous improvement
These dimensions have been embedded in classical models such as EFQM, ISO 9004, Baldrige, Crosby etc.
They helped organizations determine not only compliance with standards but also readiness to grow through quality.
However, the landscape has changed.
With the widespread adoption of digital technologies and different solutions based on AI, two new dimensions of maturity are becoming critical:
- Information and technology maturity – readiness of IT infrastructure to support automation, analytics, and AI-driven decision-making.
- Data maturity – the ability to collect, structure, store, and manage data so it becomes a real business asset rather than digital noise.
Why does this matter?
While previously a company could only achieve maturity through processes and people's engagement, now without a data strategy and a technological foundation, any quality system is limited.
For modern solutions to be effective, it's important to "feed" them with high-quality data.
Today, we can say that "data maturity" = "quality management maturity"
𝐘𝐨𝐮𝐫 𝐭𝐮𝐫𝐧: How would you rate your company’s maturity today?
- High, already moving towards a data-driven approach
- Moderate, with a solid base but more work needed in data and tech
- Low, still relying mostly on classical processes