Delivering confidence in the intelligent and effective use of data

It’s said that by 2020, more than 20 billion connected devices will be in use across the world. Despite this, companies only use a fraction of their data, and a lack of standardisation means communications between systems is limited. Digital standards are vital to bridging this gap.

We are developing the techniques and platforms needed to support the digital age. By establishing the uncertainty quantification methodologies and providing capability for assessing trust in algorithms and software, we will ensure new technologies such as AI and machine learning can be used with confidence.

As instruments and systems become fully digital, digital twins, or replicas, and models are needed to drive our understanding and supplement increasingly automated control systems. Currently, data is used to provide model inputs and for validation purposes, but the digital twin approach goes beyond this paradigm by using data gathered at every stage of manufacturing and usage to update the model throughout a product's lifetime. This approach means that lifetime predictions and maintenance schedules can be individually tailored for high-value assets. The approach depends on bringing together high-fidelity modelling, data of known quality, and a rigorous approach to uncertainty evaluation. Our work is developing tools for all three of these cornerstones.

We will develop the techniques, platforms and standards needed to underpin the digitisation of measurement and digital traceability chains: ontology, metadata standards, curation, data quality indicators; distributed ledger technology and data security. In addition, we will provide the science leadership for a number of laboratory-of-the-future pilots as we take the first steps to ensure that NPL becomes an exemplar for digital and data-intensive measurement.