The evolution of the modern data platform
Key takeaways
Our Six Steps
- Data as a product: build and manage data product outputs, which are a collection of data assets.
- Data marketplace: add a layer to trade data products. Adding this will clearly show integrity across data products, adding confidence to domains.
- Domain-level architecture: focus more on building data solutions for each domain which will add value per domain. Different domains are more or less and as complicated as one another.
- Front-to-back design: leading on from domain-level architecture, put the end consumer first. How are they going to use the data?
- Metadata management: management of metadata produced through all these products is critical to track not only redundant data, but to govern the landscape.
- Enhanced stream-driven: remove constant dependency on end-of-day processing when streams are available. Enhance the platform by supplying up to-date data so that information value may be gained sooner.
FAQ: Modern Data Platforms
What are the key components of a modern data platform blueprint?
A modern data platform consists of three core layers: systems of record, systems of storage and processing, and systems of engagement. Data flows through raw/landing, curated, and distributed layers, each designed to support domain‑specific requirements.
Key capabilities include data marketplaces, data fabrics, automatic cataloguing, and MLOps/DataOps pipelines to automate lineage tracking, validation, and model retraining.
Explore the full blueprint and design considerations in the Thought Leadership report.
How has the data platform evolved from data warehouses to lakehouses?
Data platforms have evolved from rigid relational data warehouses to more flexible big data architectures, and subsequently to lakehouses, which blend the optimisation of structured systems with the agility of unstructured data handling. Data warehouses addressed duplication and integrity challenges but lacked scalability. Big data systems allowed rapid ingestion without upfront modelling, yet sacrificed performance optimisation. Lakehouses were developed to bridge these gaps through metadata caching and architectural enhancements.
For a full evolution timeline and insights into future platform directions, download the Thought Leadership report.
How can organisations transition toward a modern data platform?
Transitioning requires an MVP‑driven approach: assess domain needs, transform using modern data capabilities, manage governance, and monetise insights. This ATMM model prevents teams from trying to “boil the ocean” and instead helps them deliver incremental value.
The modern approach integrates existing technologies - data lakes, warehouses, and lakehouses - into domain‑focused solutions guided by business value rather than strict technical uniformity.
Download the full report for step‑by‑step transition guidance.
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