Data science

Data architecture

Data is the lifeblood of your organisation. Successful businesses tend to be ‘data-centric’, treating data as a strategic, permanent asset, even if the applications
using that data change. A documented, durable data architecture will enable your business to more readily take advantage of new and emerging technologies, such as artificial intelligence, the Internet of Things (IoT), and the ‘semantic web’ – an approach which will help structure your data so it can be read directly by computers, enabling increased interoperability and information sharing.

We apply our data architecture expertise to help your organisation transition to a data-centric architecture, offering a range of services including data modelling,
data migration, and data wrangling (i.e. repeatable processes for preparation, restructuring and cleansing of data). Through a long-term commitment to a ‘living’ enterprise-wide data model and unified governance of data and processes, you will help to minimise duplication and provide confidence that everyone is working from the same data. Your organisation may also benefit from a simplified systems and applications portfolio.

Many of our customers use geospatial data as part of their business operations. They recognise the value of location-based intelligence for planning and decision-making, particularly when they are involved in managing geographically dispersed assets or critical national infrastructure.

Delivering insights from data

At Frazer-Nash, we use the power of modelling, simulation and visualisation to help you achieve greater business performance. Combining advanced modelling
and data analysis techniques with deep market knowledge and operational understanding, we help you make sense of your data, enabling you to use it as a powerful tool to inform your decision-making, manage uncertainty and take advantage of predictive analytics.

We are experts in the use of agent-based models, probabilistic and statistical approaches and Artificial Intelligence and Machine Learning methods.

We follow a four-step process involving:

• Assessment: understanding the problem and the required data elements, including data enrichment and feature extraction
• Model evolution: using physical, Bayesian and Agent-based methods and application of machine or deep learning techniques
• Exploration: recalibration, validation and uncertainty reduction
• Communication: through visualisation, trend statistics and solution-focused recommendations.