Data Architecture and Platform Design

Starting with your vision, and driven by the need to deliver value, we will work with your business and technology teams to produce and articulate a data platform solution that will meet your goals, be achievable, and allow you to grow your data sources and services as your company evolves.

The solution design, in general, will provide an initial “conceptual” data model (unless you already have one). Any solution, whilst based on technology, must be about your data – how to capture it, manage it, give it meaning, and derive value from it.

The technical realisation will most likely be based primarily on suitable services from your preferred cloud vendor, or unified platforms, like Snowflake or Databricks, hosted in your preferred cloud, with configuration and customisation so that they function well within your organisation and are truly “fit for purpose”.

Being a cloud and vendor-agnostic consultancy gives us the freedom to tailor platform architectures to your needs and constraints for truly bespoke solutions.

Once we have established the architecture for your platform, we will work with you (possibly through a series of workshops) to identify those components that will unlock value delivery early. This approach builds confidence and trust in the platform and the delivery process. These “blueprints” will specify how the technology components will act harmoniously, how they will be built, the quality controls to be applied, and whatever additional artefacts are needed for your internal approvals such as TDA and CAB, or GDPR.

Data architecture is a fundamental enabler for any insights, predictive analytics and ML/AI capabilities - for any meaningful insight to be derived from data, it must be organised well.

Our expertise spans data lakes, data warehouses, and lakehouses data architecture patterns – we design future-proof data models optimised for cloud, analytics and AI, and ensuring a strong foundation for efficient data management and governance. We design efficient and scalable data models, clearly setting enterprise-level standards and rules for how data is collated, modelled and consumed. Following our data modelling principles, we ensure data integrity, consistency and explainability – the model explains how they arrived to their answer!

Having trust in data spans from having good data quality and making the data journey documented, visible and available to end users. Our technical analysts can work with you to define data catalogues, data contracts and methods for collating metadata, that describe the data sturcture, lineage and use case.