I will start with a survey from TDWI, The Data Warehouse Institute. The survey highlights some of the most common data pain points companies are facing today.

The first pain point is cloud data warehousing. Many organisations understand the benefits of moving to the cloud, but the transition often triggers multiple projects that are difficult to deliver quickly.

The second pain point is the return on investment from data lakehouses. Many companies have invested in this area, but the expected business value is not always clearly visible.

The third pain point is that data warehouse development is too resource intensive. Without automation, data warehouse projects remain slow, expensive, and dependent on scarce specialist resources.

The fourth pain point is self-service reporting. In many companies, self-service BI has created confusion instead of clarity. Reports are created in many different tools and files, which makes it difficult to know which report, KPI, or indicator is correct.

The next findings come from Gartner, one of the largest analyst organisations in the world. Gartner asked customers what frustrates them about the future of their data warehouse.

The first answer was vendor lock-in. Many companies have too many dependencies on individual vendors, and changing providers becomes difficult. This is especially relevant in cloud environments, where services, data structures, and tooling can quickly become tightly connected.

The second frustration is accessibility and availability of data. Without a holistic data model or data catalogue, users often need IT support to find and access the right data.

The third frustration is a lack of development agility. Many companies use agile methods such as Scrum, but their data warehouse processes are not automated. To support real agility, tooling must cover the full lifecycle, from modelling and development to deployment and change management.

The final frustration is increasing technical debt. When a data warehouse has been adapted many times over several years, complexity grows. Moving to a newer technology stack does not automatically remove this technical debt.

The first solution pattern is a holistic data model.

Organisations should have a clear model that shows how entities, relationships, business concepts, and the overall company data model are connected.

This holistic model can support business users with top-down modelling and KPI definitions. It can also support the BI competence centre and the technical implementation layer.

A holistic data model helps improve development agility, reduce technical debt, and establish stronger modelling standards.

The next solution pattern is data warehouse automation.

Organisations should use tools that automate the design process, lifecycle management, and deployment.

This reduces the manual effort required for data warehouse development and helps teams respond more quickly to change.

Another important pattern is the ability to generate code from the holistic data model for different technology stacks.

A data model should not be tied to only one database or environment. For example, the same model should be usable for SQL Server on-premise, cloud databases, or Azure Synapse Analytics.

When the technology stack changes, the organisation should not have to redesign everything from scratch. This supports cloud migration, improves agility, and helps reduce technical debt.

The next solution pattern is the implementation of a business intelligence competence centre.

Smaller companies may not have dedicated teams, but they can still create virtual competence centres. The goal is to define clear governance processes, training processes, and ownership for BI and analytics.

This creates a stronger foundation for consistent reporting, better data usage, and clearer responsibilities across the organisation.

Another important requirement is sustainability. The data environment should continue to run even after a subscription or vendor contract ends.

If an organisation stops using a vendor product, the generated code and deployed solution should still work without ongoing subscription payments.

This is why organisations should avoid black-box solutions. Generated code should remain accessible, understandable, and usable, even if the organisation later decides to leave the original solution.

Collaboration is also essential. Everyone involved in data modelling should be able to work in one shared environment, including business users, BI competence centres, and developers.

This supports a clearer handover from business design to technical implementation and helps distributed teams work together more effectively.

A fast change approach is also needed. New requirements should be implemented quickly without large requirement documents and long development cycles.

Ideally, a change should be made in one layer and then generated automatically into all other affected layers.

Knowledge preservation is another important solution pattern.

Documentation should not exist only at code level. It should also be available in business-readable formats, so new employees, external consultants, and business stakeholders can understand the data model and its logic.

Copyright ownership must also be considered. Some vendors generate code but legally restrict its use after a subscription is cancelled.

This creates another dependency risk. Organisations should make sure they retain the right to use generated artefacts and continue operating their solution.

Modern tools make it easy to create reports directly outside centralised data warehouses. While this can be useful, it can also create inconsistent definitions and reporting confusion.

Changes and development should therefore be guided by one central holistic data model that supports both backend and frontend layers.

These are the solution patterns we recommend. They cannot all be implemented within one year, so organisations need a realistic roadmap.

We have templates and consulting processes that help companies move towards this kind of sustainable analytics strategy.

Thank you.