Data Warehouse & Integration optimisation
Proven Reference Architectures, Patterns & Blueprints
Big Data Architecture implementations
Data Lake & Logical Data Warehouse implementation
Enterprise & Integration Architecture
Our 3 tier approach helps clients implement effective Data Strategies & Architectures
Using the latest automated Data Discovery, Mining & Profiling techniques we identify the often hidden issues that matter in your data landscape.
Applying cutting edge cost & risk modelling techniques we quantify the true value of improving your organisation’s Information Efficiency.
By focusing on fundamentals we help you undertake meaningful and practical steps with a clear Return on Investment.
All our projects feature:
Smaller batches & more frequent deliveries mean clients receive solutions faster.
We minimise sunk costs & misunderstood requirements through prototyping solutions.
We use an iterative delivery method to continuously improve results.
Intuitive, meaningful, business-friendly metrics govern everything we do.
Cross functional teams
Blending business & technical expertise leads to better results.
We use the latest emerging Big Data & Data Science tools.
We create flexible solutions which evolve as you do.
Focus on eliminating waste
We minimise data waste & maximise reuse of existing components.
"We have engaged Data To Value a number of times. They have helped us set-up and build a new data management function and procure data quality tooling. Nigel and the team have demonstrated great flexibility to work around our changing schedule and consistently delivered high quality output. Their expert advice will have long lasting benefits for our organisation."James Green - Gambling Commission
"Data to Value really helped us understand and leverage the opportunities available from a mix of emerging and proven data techniques and technologies"Lars Meisinger,
"Data to Value are using new approaches to Data Quality Management that I've personally not seen elsewhere and really pushing some new ways of thinking around data quality."Dylan Jones,