At the height of the pandemic, many businesses were struggling with the dilemma of reopening their sites and trying to return to normal levels of operation, without compromising the safety - and privacy - of staff and customers.
Parallel was commissioned by Sightline Innovation to help them develop a vision for Safe at Work - a product that uses building telemetry data to provide digital twins of workplaces to calculate COVID-19 exposure risk.
Given the global scale of the COVID-19 threat, it was not surprising that there were already a number of solutions on the market. Competitor research and analysis led to the identification of four product categories, which in turn helped us to evaluate where Safe at Work should play and how it could win.
Sightline Innovation have deep experience in the domain of Data Trusts, which provided two key competitive factors that provided an opportunity for differentiation: enhanced privacy and probabilistic risk modelling.
These factors became key components for an enhanced value proposition for Safe at Work and provided a set of principles for developing a vision for the product.
Physical risk - Spatial Surveying tools
Given that staff many facilities would not have existing location analytics data available, survey teams could assess the levels of different types of risk in each zone - either remotely or by walking the facility and inputting data on the move.
By using bluetooth beacons or UWB devices attached on or near objects, an accurate picture of employee behaviour patterns and proximity events can be observed and analysed.
For an entire facility - or a smaller zone within it - anonymised aggregations of proximity events could be observed - from brief passing contacts that represent little risk, to riskier sustained contact and the use throughout the day of shared objects, such as tools.
With staff movements modelled using actual data or synthetic simulations, it becomes possible to model how behaviour affects the level of risk within a space.
The original vision of the Safe at Work product was devised around a graph database - and it was assumed that a graph visualisation would be the best way to indicate if contact had occurred between people.
Instead - for reasons of both cognitive load and privacy - we proposed a simpler, timeline based view to help provide context and rationale behind any actions to be taken and to give an at-a-glance view of the scale of the issue.
This contact visualisation could be toggled between unprocessed events / people and those that have already been dealt with / resolved.
Lists of proximity based contacts could be organised according to a company’s policies, making use of the underlying data that specifies the duration and frequency of contacts (without overloading users with too much information upfront).