1. Internet of behaviours

The IoB consists of multiple approaches to capture, analyse, understand and respond to all kinds of digital representations of behaviours. A range of public- and private-sector organisations will seek to use the IoB’s digital capture ability to affect or influence the behaviours of individuals or collective demographic groups.

The IoB combines multiple sources of intelligence such as commercial customer data, citizen data processed by public-sector and government agencies, social media, public domain deployments of facial recognition and location tracking. Additional sources can include things such as temperature and other physical measurements in both private and public domains. From analysis of data in these myriad resources, it is possible to tag an increasingly broad array of people’s behaviour as an ‘event’.

Emerging technology innovations and algorithm developments enable more precise monitoring and interpretation of behaviours. The IoB combines existing technologies that focus on the individual directly (e.g. facial recognition, location tracking and big data) and connects the resulting data to other indirectly identifiable information (e.g. cash purchases, automotive telemetry, vacuum bot layout data and device usage data). Thus, the IoB is partly based on the Internet of Things (IoT). In the IoT, physical things are ‘instructed’ to perform certain actions under certain conditions. In the IoB, people’s behaviours are monitored and incentives or disincentives are applied to influence them to perform toward a desired set of operating parameters. A program can apply value judgments to behavioural events based on the behaviour desired by the program’s deployer.

In response to the pandemic, organisations are deploying additional behaviour intelligence sources at a faster pace. Examples include temperature measurements, face recognition deployments, contact tracing and location-tracking systems. The focus is on combining physical and digital behaviour data to influence behaviours that will reduce the spread of infection.

2. Total experience

Total experience (TX) is a strategy that creates superior shared experiences by interlinking the multi-experience (MX), customer experience (CX), employee experience (EX) and user experience (UX).

Organisations need a TX strategy because they must continuously enhance their CXs and EXs, especially as these interactions have become more mobile, virtual and distributed, mainly because of COVID-19. TX is about more than improving the experience of one constituent — it improves experiences at the intersection of multiple constituents. These intersected experiences require organizations to rethink how they change behaviour and technologies by addressing the feelings, emotions and memories that make up the CX and EX, as well as the experience of partners and other constituents.

Twenty years into the experience economy, expectations and demands from customers and employees continue to change. That is because CXs and EXs are constantly evolving, driven by new interactions. This leads to participation, then to engagement, and on to satisfaction, loyalty and advocacy. UX is about the usability and design of apps and products to reduce effort, increase engagement and drive satisfaction. MX is about the technical implementation across a wide range of devices, touchpoints and modalities of interaction. The pandemic has increased the need to transform the digital experience, moving from keyboards and screens to multiple modalities using conversational, immersive and touchless environments. The business moments in which experiences between customers, employees, partners and ‘things’ are inextricably interlinked are particularly important.

3. Privacy-enhancing computation

Privacy-enhancing computation comprises three types of technologies that protect data while it is being used to enable secure data processing and data analytics:

  • The first provides a trusted environment in which sensitive data can be processed or analysed. It includes trusted third parties and hardware-trusted execution environments (also called confidential computing).
  • The second performs processing and analytics in a decentralised manner. It includes federated machine learning and privacy-aware machine learning.
  • The third transforms data and algorithms before processing or analytics. It includes differential privacy, homomorphic encryption, secure multiparty computation, zero-knowledge proofs, private set intersection and private information retrieval.

Each technology provides specific secrecy and privacy guarantees and some can be combined for greater efficacy.

Global data protection legislation is maturing and, with the unstoppable pervasiveness of personal data, every organisation that processes personal data faces ever-higher privacy and non-compliance risks.

At the same time, organisations now realise the economic potential of their data repositories. The demand for processing data in untrusted environments and performing multiparty data sharing and analytics is rapidly growing.

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