Ensuring confidence in data

Confidence in data supports business critical and safety critical decisions

The world is transforming digitally.  Emerging technologies, such as Internet of Things, artificial intelligence (AI), 5G communications, augmented reality, biotechnology, simulation testing, additive manufacturing and distributed ledger technologies will come to define our future. All are enabled by the effective use of data and the successful adoption of these technologies will depend upon their ability to get data management right.

Generating trusted data and providing the confidence needed to take action on the information it provides will be vital for businesses, governments and society in this increasingly digitised world.  Confidence in data can mean more efficient design, test and manufacture cycles, greater efficiencies in creation and delivery and fewer operational mistakes; as well as a clearer understanding of why things go wrong and who or what is responsible.  

It will become harder to reliably quantify the risk and reliability associated with decisions which are derived from data unless there is a way to measure the quality of data consistently. Establishing 'proof of trust' within the system and the appropriate governance model involves the application of measurement infrastructure and standards throughout the data lifecycle. 

Increasing the rate of adoption and investment

NPL is addressing the following needs:

• maintain the confidence and integrity of processes in a data-centric world based on AI
• provide confidence in the collection, communication and analysis of data, when both hardware and software need to be interoperable and validated
• provide common approaches and standards for secure, traceable and authenticated security and resilience processes and reduce inherent risk of bias in programming.

We are using our world-leading expertise in metrology to:   

• maintain the focus of data science to support advances in measurement science; at the same time, leveraging advances in data science to drive masurement science forward
• measure the accuracy and precision of data, the provenance of data, and the propagation of uncertainty through data processing and curation processes. 
• build the necessary data standards and quality infrastructure, bridge the gap between the physical and digital worlds, provide traceability back to the SI and enable trustworthy and explainable AI.