Turning Data into Outcomes

Christoffer Kanstrup

Chief Data Officer

In this video, Christoffer draws on Danske Bank’s experience building its “Data Supermarket” to share practical lessons on how organisations can move beyond data overload to actionable insights. He explains how treating data as a product enables faster insights, stronger decision-making, and reliable AI adoption.

Data Science

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Turning Data into Outcomes

10 mins 20 secs

Key learning objectives:

Understand how modern organisations can overcome barriers to data-driven decision-making
Identify the concept of a “Data Supermarket” and its benefits
Describe five core drivers of effective data strategies
Outline how treating data as a product creates a foundation for AI adoption and competitive advantage

Overview:

Organisations today are flooded with data, but often struggle to turn it into trusted, actionable insight. Many are shifting towards treating data as a product, creating platforms where high-quality, compliant, and reusable datasets are readily available. This approach, sometimes described as a “Data Supermarket”, enables faster insights, stronger decision-making, and a foundation for robust AI models. By embedding privacy by design, fostering trusted data products, and enabling self-service access, companies can transform how teams work with data and build a competitive edge in the age of AI.

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Summary

What challenge do organisations face with data today?

The problem is rarely a lack of data, but rather the difficulty of finding, trusting, and using it effectively. Data often sits in silos, is poorly documented, or isn’t reliable enough to act on. To compete, organisations need more than dashboards; they need systems that make data accessible, reusable, and high-quality by design. This shift unlocks better decisions, faster insights, and a foundation for AI and analytics.

What is a “Data Supermarket” and why does it matter?

A Data Supermarket is a way of managing and distributing trusted, certified data products. Just like shopping in a well-organised store, users can quickly find the data they need, packaged with documentation and compliance checks. This saves time, reduces duplication, and ensures consistent standards. Whether it’s customer insights, risk metrics, or operational data, making trusted datasets available as products empowers teams to act quickly and confidently.

What are the value drivers of a strong data strategy?

Five drivers underpin effective modern data strategies:

Easier access through standardised, self-service tools
Fostering AI-driven value creation with reliable inputs
Accelerating time to insight by reducing preparation work
Increasing trust through certification and transparent metadata
Protecting personal data with privacy by design

Together, these principles allow organisations to scale insights, support innovation, and maintain compliance while ensuring customer trust.

How does this enable AI and real-world applications?

Trusted data products create the foundation for AI to deliver reliable outcomes. For example, in credit risk, healthcare, or supply chain optimisation, teams using certified datasets can build AI models that predict trends and manage risks more effectively. Because the data is clean, standardised, and compliant, these models perform better and can be scaled across use cases. Treating data as a product enables organisations in any sector to translate raw information into intelligence and competitive advantage.

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Christoffer Kanstrup

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