Data Analytics Strategy: How Quantum Thinking Enables Change Without Disruption

Data Analytics Strategy: How Quantum Thinking Enables Change Without Disruption

Inspired by a Data Matas podcast featuring David Draper, Data Science Manager at Iris Software Group

A Risk-Aware Approach to Building a Modern Data Analytics Strategy

In many organisations, the greatest barrier to progress in data systems is not budget or lack of innovation—it is risk. Data leaders hesitate to switch platforms or tools due to fears of disruption, especially as artificial intelligence accelerates the pace of change.

In this episode of Data Matas, David Draper shares a practical approach to modernising infrastructure. By applying principles of quantum thinking—not quantum computing—his team at Iris has developed a data analytics strategy that supports innovation while maintaining operational stability.

Key Takeaways for Enhancing Your Data Analytics Strategy

1. Build Modular Systems That Support Safe Experimentation

A resilient data analytics strategy is modular. Draper’s team constructs systems in isolated components, enabling them to test and iterate without affecting business-as-usual operations.

How to implement:

• Break your stack into distinct components: ingestion, transformation, visualisation.
• Use feature toggles or isolated environments for controlled testing.
• Avoid dependencies that make deployments risky.
• Test using real workloads, not samples.

What you’ll gain:

• Greater confidence in rolling out changes.
• Fewer unplanned outages.
• A future-ready, adaptable architecture.

2. Embed Innovation Time into Daily Operations

Effective innovation isn’t about waiting for quiet periods. It’s about designing a data analytics strategy that includes dedicated time for learning and experimentation.

At Iris, knowledge-sharing is a priority. Whether through design sprints or technical forums, Draper’s team maintains a culture where curiosity leads to progress.

How to implement:

• Schedule regular “go wide” sessions for ideation.
• Allocate 10–20% of time for exploring new tools.
• Encourage peer-led talks and demonstrations.
• Pilot innovations quickly and iterate fast.

What you’ll gain:

• A more engaged team with shared ownership of innovation.
• Faster uptake of new tools and methods.
• A proactive, not reactive, delivery environment.

3. Make AI Outputs Explainable and Transparent

AI should complement human decision-making, not replace it. Draper’s approach ensures that AI-generated insights are understandable by end users—an essential element of any responsible data analytics strategy.

How to implement:

• Use models that generate human-readable summaries.
• Annotate outputs with key contributing factors or logic.
• Avoid opaque “black box” models in high-stakes settings.
• Create a culture where AI results are questioned and discussed.

What you’ll gain:

• Wider adoption of AI across the business.
• More robust decision-making, even among non-specialists.
• Less reliance on central data teams for interpretation.

Rolling Out a Smarter Data Analytics Strategy

Transforming your approach does not require a full system overhaul. Draper recommends a phased rollout that prioritises stability while encouraging innovation.

Months 1–2:

• Run a parallel infrastructure test for one critical dashboard.
• Allocate innovation time within sprints.
• Launch an internal knowledge-sharing session.

Months 3–4:

• Introduce explainable AI outputs into one reporting flow.
• Run a proof of concept (POC) with one AI tool.
• Identify areas of your ETL stack for modular redesign.

Metrics to track:

• Number of successful migrations without downtime.
• Rate of AI tool adoption.
• Time set aside vs used for experimentation.
• Participation in learning forums.

Outcomes from Real-World Application

When Iris deployed AI summarisation to improve dashboard usability, it led to deeper engagement across the business. People began to ask better questions, trust the data more, and rely less on technical intermediaries.

“It helps people who aren’t data specialists make sense of what they’re seeing.”

This reflects a core principle of Draper’s strategy: tools should empower users—not create new silos of complexity.

Final Thoughts: Applying Quantum Thinking to Your Data Analytics Strategy

David Draper’s work illustrates that innovation doesn’t always require cutting-edge technology. A forward-thinking data analytics strategy prioritises:

• Modular, testable systems
• Time for structured experimentation
• AI that supports rather than replaces human judgement

Start small—choose one dashboard, one process, or one internal discussion to trial this approach. Iterate, learn, and scale what works.

🎧 Listen to Episode 3 of Data Matas for the full conversation.

Further Resources

• 🎥 Watch the full episode

• 🌐 Explore more insights

• 👤 Connect with David Draper on LinkedIn