How to Migrate to a Data Vault Data Platform

Data warehousing has long been the backbone of business intelligence and analytics. Traditional approaches like the Inmon and Kimball methods have served organisations well in building centralised data repositories. However, as your data landscape becomes more complex and dynamic, Data Vault could be a better option. Once you reach that fork in the road, it’s important to create a strategic plan for the migration of your data platform.

In this blog post, we explore the benefits of migrating to a Data Vault data warehouse, and how to do it.

Understanding Inmon and Kimball

The Inmon method follows a top-down approach, emphasising a normalised data model with a single integrated data warehouse. On the other hand, Kimball employs a bottom-up approach, using dimensional models and building data marts to address specific business needs.

While these methods have provided a solid foundation for data management, they face challenges when dealing with evolving business requirements, data integration complexity, and the need for agile data delivery.

Introducing Data Vault

Data Vault is a modern data warehousing approach that addresses the limitations of Inmon and Kimball methodologies. The method provides a flexible, scalable, and agile architecture that handles data integration challenges effectively.

The core principles of Data Vault a represented by Hubs, Links, and Satellites, a focus on historical tracking, and a separation of concerns for data integration and business reporting. By leveraging these principles, organisations can overcome the limitations of traditional approaches and build a robust foundation for data-driven decision making.

Why Migrate to Data Vault?

Agility – Data Vault allows for easier adaptability to changing business requirements. Its flexible architecture facilitates the addition, modification, or removal of data sources and attributes without disrupting existing structures. This agility enables organisations to quickly respond to evolving market conditions and make data-driven decisions in real-time.

Scalability – As data volumes grows, Data Vault provides scalability by distributing data across multiple Hubs, Links, and Satellites. This approach allows for parallel processing and improved performance, enabling organisations to handle large data sets efficiently.

Data Integration – Data Vault simplifies the complex process of data integration. With its standardised modelling techniques and separation of concerns, it streamlines the extraction, transformation, and loading (ETL) processes. Data can be loaded into the Data Vault incrementally, reducing the impact on existing systems and enabling faster time-to-value for new data sources.

Historical Tracking – Data Vault’s emphasis on historical tracking ensures a comprehensive view of data lineage and traceability. It enables organisations to analyse data changes over time, supporting regulatory compliance, auditing, and data governance initiatives.

Data Vault project Considerations

There are some key considerations to starting a Data Vault project. For example, the need to build a Data Vault data model, including the identification of Hubs, Links, and Satellites. In addition, the ETL processes should be redesigned to accommodate the new Data Vault architecture.

The Datavault Migration framework

Our migration framework is specifically designed to support an agile way of working and deliver the following benefits:

User-oriented – Keep data analytics services running continually with a focus on immediate business priorities. Users need to feel supported through the process.

Develop new skills – There is no need to split the team between the legacy and the new system. Maintain motivation and ensure all your team develop new skills.

Controlled risk – As with any agile project, issues will become clear much earlier in the process, reducing the overall project risk.

Continuously add new functionality – Add new functionality even while legacy functionality is migrated. This was important new business requirements are not left on hold during migration.

Measure and monitor progress – By continuously testing and reconciling, it is possible to monitor project status and provide senior management visibility of progress.

Business focus – Ensuring discussions are focused on the service provided and how to improve it simplifies the debate generally making the migration more business-focussed.

At Datavault, we see clients use one of three strategic approaches for their migration projects. They are:

• Greenfield – Build a new system from the ground-up and move over to use it when it is ready.

• Lift and Shift – Take everything in the existing platform and replicate it on a new platform with little or minimal change.

• Refactoring – Build around the existing system with incremental change and replacements so a new system emerges from the old.

Generally, when working in an agile environment, as you do with Data Vault, refactoring is the migration strategy that makes sense where there are legacy considerations.

Although refactoring mutates the legacy into a new system from within, this doesn’t mean the data analytics service remains on the same platform. It does mean, however, that the data analytics service can be on a new platform or a combination of the legacy and new platform.

How to use refactoring to migrate

Think of refactoring as if you were building an extension on your house. You’d put up scaffolding to support and protect it, and carefully plan the renovation work so that you can still live in it while it is being worked on.

Like extending your house, we build a migration “scaffolding” around your legacy environment while you are refactoring it. Meaning you can still use your current environment while it is being upgraded

Summary

Migrating to a Data Vault data warehouse represents a move towards improved agility, scalability, and improved data management. By embracing the principles of Data Vault, organisations can unlock the true potential of their data assets and drive data-driven decision-making in a dynamic business environment.

You don’t have to do it alone. At Datavault, we help organisations migrate from their existing platform quickly, efficiently, and easier than doing it on their own. You can download our free White Paper which takes a deep dive into all things data platform migration here.

If talking is more your thing, get in touch with us here.