For years, new mobile phones got the most public attention at Mobile World Congress. Now, hardly anyone notices. Sure, many of the usual OEMs are launching their latest smartphones, but people in the Fira Gan Via aren’t talking about them. They are talking about data and AI, as well as related topics like IoT, edge, wearables and all smart services powered by data. Indeed, data has taken over this show, as it has many other things in the world.
Let’s start with consumers devices. Wearables such as smartwatches and many kinds of sensors, are attracting much more attention than phones. These devices are very much about data. They collect data, and they offer applications using that data that come to guide our lives. IoT and smart homes are another area where we see a lot of concrete development. Of course they require connectivity, e.g. 5G, but they are closely linked to data, platforms, clouds and applications.
Devices are needed, but the really fundamental question is how data and AI can be utilized so consumers can get useful and actionable services. People are not interested to just look at graphs and data tables. For example, we know that Apple Health already has too much data and too few actionable instructions for people. At the same time Apple is not very transparent about how it pre-processes the data, so it is also hard to make reliable health apps using that data.
There is a clear need for platforms for wearable, personal and home data. We also need different layers of platforms. Hardware, connectivity and security need their own solutions, as well as data lakes to store data, services to pre-process data and finally environments to build applications and services on data.
Data and AI on the edge
Edge is definitely coming up in data and AI discussions. There are practical technical reasons like latency and availability. There is also the trend of decentralized services, although it is not totally clear yet how decentralization (e.g. blockchain) is in line with edge. There are also commercial reasons – network vendors and telco carriers could win a lot of new business with edge.
Consumer data architecture is one of the most important issues not only for network companies, but also cloud services and any company that build platforms and services using data. We have all sorts of network and technology architecture questions, but at the same time we also have questions about data ownership that also have an impact on architecture.
Federated learning as a service (FLaaS) and data clean rooms (DCR) are being mentioned as a step toward better privacy. Federated learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. DCRs offer neutral, safe places to keep consumer data. They are also responsible for data regulation and offer regulatory rights to consumers. But the market is still very fragmented, and when companies like Meta, Google and Amazon want to offer these kinds of services, it raises questions about real privacy and consumer’s control.
Both CDR and FLaaS indicate that all companies must now start to think about data regulation, privacy and consumer’s rights. Some say they are steps in the right direction, since we cannot expect a full user-centric data model immediately. At the same time, there are many examples in the history of technology that show compromises between old and new rarely work.
We also heard about examples about how people have been able to anonymously share their heartbeat and body temperature data to help and fight the pandemic in Germany. These kinds of things have become reality, but they would be more useful if that data could be combined with other health data. For that, we need a totally different architecture than having some companies – such as Meta’s or Google’s DCRs – combine this data. User’s should have their own data storage in their personal cloud or edge cloud.
The value of consumer data
It is still somehow strange to listen to these discussions about the privacy angles of data and AI, where people talk about steps to better privacy, yet the value of data to consumers is often totally ignored. The thinking is still centered on companies collecting data from consumers to offer services to consumers, rather than offering tools for consumers to utilize their own data. Many traditional companies are missing this trend – empowering consumers with their data is a major market opportunity.
The consumer market isn’t the only big opportunity for data and AI – the enterprise market is also attractive. But enterprise data and AI have different challenges from consumer data. There are some similarities, like how to break down data silos, but consumers don’t have the challenges of many legacy systems. As I have written many times, legacy IT and old processes are a real challenge for digitization and AI use cases. Edge may now have an ubiquitous presence in discussions about data-driven consumer services, but it remains a mystery for most just how to get edge to work in reality with all these legacy IT systems. In particular, when many corporations are still afraid to use the cloud, then should they have data in many decentralized edge nodes?
As we can see there are many interesting data and AI discussions going on in MWC 2022. Naturally we don’t yet have answers to all the questions, but it is clear that data and AI are developing rapidly and together have possibly become the most important and fundamental core of all technology development for all devices, products and services. That’s why it will dominate MWC as well.