Faculty, a VC-backed artificial intelligence startup, has won a tender to work with the NHS to make better predictions about its future requirements for patients, based on data drawn from how it handled the COVID-19 pandemic. In December 2019, Faculty raised a $10.5 million Series A funding round from U.K.-based VCs Local Globe, GMG Ventures, and Jaan Tallinn, one of Skype's founding engineers, giving it a valuation of around $100 million. Faculty will work with NHS England and NHS Improvement to build upon the Early Warning System (EWS) it developed for the service during the pandemic. Based on Bayesian hierarchical modeling, Faculty says the EWS uses aggregate data (for example, COVID-19 positive case numbers, 111 calls and mobility data) to warn hospitals about potential spikes in cases so they can divert staff, beds and equipment needed. This learning will now be applied across the whole of the service, for issues other than the pure pandemic response, such as improving service delivery and patient care and predicting A&E demand and winter pressures.

Technological advancements have made a profound impact in all aspect of our lives. And as a result, many businesses are pushing forward with their digital agendas. Digitisation has made its way into the legal system, too. The UK government published its policy paper in 2017, setting out how to develop a world-leading digital economy that works for everyone. For both the government and law firms, this change is mostly driven by client pressure, according to The Law Society.

"Big data" grows bigger every year, but today's enterprise leaders don't only need to manage larger volumes of data, but they critically need to generate insight from their existing data. Businesses need to stop merely collecting data points, and start connecting them. In other words, the relationships between data points matter almost more than the individual points themselves. In order to leverage those data relationships, your organization needs a database technology that stores relationship information as a first-class entity. That technology is a graph database. While traditional relational databases have served the industry well in the past in enabling service and process models that tread upon these complexities, in most deployments they still demand significant overhead and expert levels of administration to adapt to change. Relational databases require cumbersome indexing when faced with the non-hierarchic relationships that are becoming yet more persistent in complex IT ecosystems, with partners and/or suppliers and service providers, as well as more dynamic infrastructures associated with cloud and agile. Unlike relational databases, graph databases are designed to store interconnected data that's not purely hierarchic, make it easier to make sense of that data by not forcing intermediate indexing at every turn, and also making it easier to evolve models of real-world infrastructures, business services, social relationships, or business behaviors that are both fluid and multi-dimensional.

Machine learning is increasingly being employed as a tool to help companies collect billions of data points, boil them down to what is actually meaningful, and predict what is likely to happen in the future. Simply stated... Machine learning helps make data-driven decisions. Machine learning offers practical solutions that can maximize resource utilization, prolong the lifespan of IoT sensors, platforms and networks, and enables dynamic services architecture. Our connected world is increasingly dependent on big data -- at rest, and in years to come, streaming fast data -- in motion." With real-time predictive models, once a streaming fast data point has been observed it might never be seen again.