Building strong data foundations and governance

Building these foundations to fully exploit data relies on several factors. These include strong alignment of your data strategy to business outcomes, and the right platform and infrastructure to enable high-quality, well-governed data to be unified and used. Finally, you need the technology and capability to rapidly prototype and productionise ML and AI solutions. 

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What do we help you achieve?

Codified data governance and lineage

With tools such as Azure Purview and Databricks Unity Catalog, it's possible to embed data catalogs and lineage directly into code, rather than relying on data owners to update Excel spreadsheets. This drives usability and adoption of the catalog, federating access to data further.

Platform capabilities in ML and AI

Your business can quickly prototype and develop ML and AI solutions. We enable this using tools, such as Azure ML Studio, Vertex AI, or SageMaker, as well as generative AI platforms. This helps you reduce the cost and engineering effort required to productionise these solutions. 

High-quality, reliable data

We work with business stakeholders and analysts to ensure all shadow data quality rules that people apply, ad-hoc, are captured. These are embedded into your data pipelines and data platform. Everyone works from the same set of data, giving you confidence and consistency in your decision-making. 

What we do

We serve the enterprise data value chain.
01
Review and redesign platform architecture

Platform-level requirements are changing rapidly. With new services and concepts such as Generative AI, or even older ones with limited adoption such as MLOps, it is critical to continually evolve your platforms and infrastructure. Enabling you to compete with other organisations and stay competitive. 

02
Modernise data pipelines, embedding data quality

Our engineers refactor and modernise your ELT/ETL pipelines, bringing in data quality into code as appropriate, while also optimising cost-performance. Real-time data pipelines are a critical part of enabling most effective use of ML and AI solutions.

03
Re-architect your semantic layer

Legacy data platforms include legacy data modelling decisions. Data model decisions may have been made ten years ago. We made good decisions at the time, but as our use of it has evolved, some of those decisions are no longer correct. This is a good time to re-look at some of those decisions and work out if they're still correct.

04
Create patterns for ML and AI deployment

Build consistency across your data foundations with reference architecture, guardrails, and patterns. We build assets for the deployment of ML and AI solutions, relying on robust foundations to deliver value rapidly and consistently.

05
Review data governance and management processes

It’s easy to over-focus on technology in assessing data foundations. This is why we look at your governance. We ensure you have the right methods in place to guarantee high-quality data, consistently, with regulatory compliance.

06
Modernise data governance in platform

Many organisations look to embed, at least in critical data pipelines, full data lineage and automated data cataloging. Our engineers help modernise existing data pipelines to ensure lineage, etc, is captured in services like Azure Purview or Databricks Unity Catalog.

How we approach foundations and governance

We offer a Data Foundations Readiness, which helps identify gaps in your data quality and highlights the technical debt that will hinder the delivery of AI and ML solutions. 

 

Our technology ecosystem

See our work in action

Modern technology stack helps Zoopla launch new products faster.
See all case studies

See our work in action

ML and AI-driven technologies enable Elsevier to cut document processing times in half.
See all case studies
1 /
Real Estate
Data & AI Transformation
Modern technology stack helps Zoopla launch new products faster.
Publishing
Data & AI Transformation
ML and AI-driven technologies enable Elsevier to cut document processing times in half.

FAQs

Our Data Practice Lead answers your questions.
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01
Why is governance and foundations so closely aligned?

It used to be that data governance was managed by different teams (e.g. legal/risk) from data as a technology centre. With tools such as Purview, Unity Catalog, Fabric, etc, it's  important to bring these two things into one consideration. Too many data platforms, for example, incorporate security after the fact and must work out how to retrofit RBAC/GDPR compliance, etc. 

Furthermore, with GenerativeAI, it’s easier and more democratised for business teams to stand up and prototype a GenAI use case. There have been some quite high-profile breaches of, for example, data sovereignty, and IP leakages, with Generative AI solutions. Because of this, technologists and governance teams need to work more closely than ever before.

02
How can we ensure the foundations are right?

There's no guarantee that the foundations are perfect. However, based on our experience of GenAI and ML deployments in various organisations and by focusing on the business opportunities and specific things needed for production, we can ensure that your next wave of products are deliverable rapidly. 

03
Why does the data strategy rely heavily on the business strategy?

It’s important to understand what the business aims to achieve, and the data (and technology) strategies should reflect this. 

As an example, take a company that does a lot of mergers and acquisitions. It's critical to understand whether they need to rapidly integrate them and leverage their data or they need to quickly add value and divest the acquisition for profit. These differing strategies all have implications on what data work we may or may not want to undertake in the organisation.

04
Why do my foundations matter?

Organisations are prototyping and trying to take AI or ML solutions into production. Many are finding that if they are not fully prepared, it becomes very laborious to deliver the solutions. This causes disappointment for business stakeholders. They may have seen working prototypes and don’t understand why technology functions are quoting such a long time to turn a working prototype into a usable, production-grade product. 

Sorting out the foundations helps to ensure that, as much as possible, these barriers to entry are removed and we ease the path into production.

Furthermore, building on top of shifting sand, where there is a lack of data reliability or clarity, means your products might end up being exposed with incorrect predictions, and failing to deliver business value.  

Data Foundations Readiness

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