As technology takes a greater hold of people’s lives – extending from gadgets and personal devices to their entire living and working environments – the corresponding ability to collect and analyse data increases.
While people are aware of this collation and analysis of personal data, and relatively willing to enter the value exchange that allows that data to be gathered in return for more personalised experiences, it brings with it both positives and negatives.
For instance, as connected cars and electric vehicles (EVs) gain in popularity, automotive brands which previously had little access to data identifying details of how their product was used, are now gathering new insight into drivers’ habits.
Data will now be acquired as people interact across multiple digital touchpoints. So, while people will still go into dealerships to look and touch and test drive cars, connected cars and EVs will also offer a wealth of product data. Valuable information on how the car is used can now be accessed offering a new level of engagement and extending the relationship between the product and the customer.
One potential benefit of sharing your data with your car manufacturer is predictive maintenance – if they can see hundreds of thousands of their cars on the road at different stages of their life cycle, better insight can be gleaned into when specific maintenance is needed or replacing car parts before they fail. This sort of predictive maintenance already happens in large manufacturing plants and could extend to new sectors with better data collection and use.
Technology and its associated data allow the world around us to be more responsive and adaptive than ever before, and this is increasingly prevalent in our homes as the Internet of Things (IoT) connects multiple devices. Although the IoT has been around for some time it is taking off as smart homes and their satellite offerings gain momentum – making IoT data a major trend in 2022.
With IoT data and apps, people can monitor and control their smart homes – from lights and solar to energy consumption – on a room-by-room level.
Some of it is temporal – how much energy is being used right now; some of it is planning – so in the winter, it recommends the smart light comes on a bit earlier. There will be a spectrum of how IoT data is used. With greater awareness of the climate crisis, watching energy consumption, and being able to act on it will gain traction with people trying to live more sustainably.
There’s a lot of excitement around this data; it’s not just that we collect data from individuals, now we are collecting data from products and surfacing to consumers. These data points serve as a digital extension of a physical product that people probably wouldn’t have thought of previously. With this data, brands can gain better insight into not just people’s behaviour but their use of a product on a micro level.
If we take this a step further and consider the metaverse, it could provide people with a digital representation of their home and cars. In that metaverse individuals could control things that happen in their physical world. The data would allow that more immersive experience with brands and products, all powered by data collected in the physical world by IoT and other product devices.
However, we are approaching a barrier in terms of what we can do with customer data. Brands are cautious about what they collect as the privacy and legal implications constrain what they can and cannot collect. With the rise of cybercrime, brands are also cognisant of losing that data through hacking . But your product data is yours – it’s more likely to be anonymous and safe.
Yet with the rise in data comes the rise of algorithms and AI. What is becoming increasingly clear is that while the algorithms might be excellent in terms of their business operating model, they are not in terms of a social operating model – and the regulators are aware of this too.
Google, Facebook and other platforms’ algorithms boost their commercial models – they encourage people to read more content and make more ad revenue – but the social contract has broken down.
While the time may not yet have come where any government is aware enough, or brave enough, to take on the big tech companies, the regulators will increasingly keep coming down on the data collected by companies, and little can or will be done about the algorithms being used.
We may see a form of corporate social responsibility around algorithms emerging – where companies make their algorithms more explainable (they won’t be fully transparent because that’s their IP) to show how they are avoiding bias and not encouraging unhealthy behaviours, when using data for personalisation.
People are beginning to realise it’s not the data, but rather the use of data, that’s the problem. But we are some way from the likes of Facebook or Google voluntarily going down this path.