In this week's episode, Matt discusses several reasons why it's a fallacy that IoT is "all about the data". He also looks further at the recent Transforma Insights work done on sustainability, with a particular focus on water consumption (very timely!), as well as some thoughts on how the metaverse might be used by enterprise.
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An approximate transcript of the podcast is available below:
Welcome to this week’s Wireless Noodle. This week I want to continue the theme of sustainability that I’ve spoken about in the last couple of episodes. This time with a look at reducing water consumption. But ahead of that I want to share some thoughts I had recently about how IoT is not (as many say) all about the data. And I want to share a few thoughts on the metaverse and its use for enterprise.
IoT is not "all about the data"
We industry analysts are probably more exposed than most to cliches surrounding technology. Things like “people buy solutions not technology”, “digital transformation is not about technology it’s about people and processes” or “think big, start small, move fast” . So far, so banal, but mostly harmless. There is, however, one which is actually actively unhelpful: “IoT is all about the data”. Here’s some good reasons why that’s not the case, and thinking that it is might be bad for your project.
The main role of most IoT devices is to be a trigger, sending alerts that are of the most basic kind, for instance if a manhole cover is moved or the temperature of a refrigerated container rises above a particular level, or a baby monitor is activated. Of course, this is data, even the binary alarm trigger. But it’s not data in the meaning of something that can be stored and analysed. It’s here-today-gone-tomorrow (or more accurately here-now-gone-in-seconds) stimulus for other things.
In quite a few cases it doesn’t even really generate much data of its own, being used as a mechanism for controlling a device. Connected traffic lights for instance don’t gather much data. They are more there to control the flow of traffic. Basic electronic shelf labels are another great example. There are also instances where further processing of data wouldn’t be appropriate or desirable, for instance in the case of child trackers.
Actually the way to think of this is not necessarily even as the trigger for an alarm, it’s as the initiator for some kind of business process change or outcome.
In plenty of IoT use cases there will also be the opportunity to gain some benefit from exhaust data, for instance a washing machine manufacturer adapting its design to reflect usage patterns, or a car manufacturer trading aggregated vehicle position information on a data exchange. The clue here, however, is in the fact that this is exhaust data. It’s a corollary to the main reason for deploying the devices, i.e. to do something.
Another big category of IoT devices involves consumer media consumption. That might be connected car in-vehicle infotainment, media players, connected TVs, connected cameras and much more besides. In the same way that shelf labels or traffic lights are recipients of largely one-way traffic, so too are media devices. Sure, someone somewhere is probably tracking the media consumption of a particular user, but that is an extremely marginal element of the utility associated with, for instance, a connected TV. These devices account for around 30% of all IoT devices, and certainly a far larger proportion of ‘data’. But this isn’t data that’s there to be analysed.
Based on a segmentation of IoT use cases in Transforma Insights’ IoT Forecast Database, we estimate that less than half (46%) of IoT devices really generate data that is worth analysing. The remainder is either not really generating data that can or should be analysed (15%) or deals with the consumption of media (39%). The proportion creating analysable data will creep up over our forecast period to 51%.
And it’s worth bearing in mind that the half of IoT devices producing data that can be analysed isn’t necessarily going to deliver a huge amount of value through being analysed. Take the cases of applications like smart watches, home weather stations, printers, ATMs, card payment terminals, assisted living solutions, bike sharing schemes, industrial monitoring or any number of other applications. They all produce analysable data, but it’s a moot point how valuable it will actually be to do so. It’s also questionable how valuable the data is for analytics, given that there may well be substitutable data that can also be used for the same purpose. To be truly valuable, IoT data needs to be unique, and often it isn’t. For the most part their value is also ostensibly in the main use case, not in some nebulous data analytics layered onto them.
The increasing prevalence of AI and edge computing, means that much data will not make it out of the device at all, meaning no potential for exhaust-data analysis off-device. In an effort to speed up processing time, many IoT deployments will increasingly rely on AI (and other data processing) performed on the device itself with minimal requirement for delivering large volumes of data back to a server. This also, of course, helps keep down the cost of connectivity where a device relies on a public network to connect, e.g. a mobile network.
With the advent of edge computing and AI, IoT devices are increasingly becoming closed-loop systems where the device receives input from sensors, processes information based on certain rules (potentially using machine learning) and acts accordingly. In most perfectly functioning IoT systems there will be very little ‘data’.
Why would it be a problem for organisations to believe that the value of IoT lies overwhelmingly in the data? The main issue is that it will potentially hold back deployment. The implication is that success in IoT depends on actively doing something with data, in terms of ingestion, storage, management and monetisation. This can be quite intimidating and may well delay deployments.
The obsession with data may also result in adopters making wrong choices about technologies. If you believe that all the value of IoT is in the data you would tend to opt for delivering all data from your IoT device back into the cloud (or similar). This is inefficient in many ways. It’s costly in data traffic and storage charges and leads to much higher latency in applications, meaning probably a lower performance.
It also encourages the adoption of sub-optimal technologies. We have previously discussed the benefits of ‘Thin IoT’, variously networking technologies, protocols, operating systems and so forth that are aimed at optimally supporting IoT deployments in constrained environments. These tend to be cheap and effective ways of connecting IoT devices. But seeking to process and/or transport much larger volumes of data means a different choice of technologies, overwhelmingly more expensive ones. It also generates more data to be processed and stored (and backed-up) ‘in the cloud’, which is not good from a sustainability perspective.
In most IoT use cases, the majority of the value derives from the mere act of connecting the thing. Doing that cheaply and efficiently has a much better ROI than trying to find mechanisms for monetising large volumes of data that can only be harvested using technologies that push up the price of the deployment.
The uses of metaverse for industry
Back in April, we at Transforma Insights published our first stab and identifying the impact of the metaverse on enterprises (blog post here: The metaverse: an opportunity for enterprises?). The last two years have seen significant activities in the virtual space known as the ‘metaverse’. For instance, Facebook changed its name to Meta, and Microsoft and Nvidia partnered with multiple enterprises for metaverse applications. Meanwhile, JP Morgan, Gucci, and Nike purchased a space in Decentraland to sell their products virtually. Driven by Augmented Reality and Artificial Intelligence technologies supported with high bandwidth and low latency networks metaverse is likely to be an extension of AR/VR with significant potential for enterprise applications and benefits in future.
In the report we examined the development stages of the metaverse as an element of Web 3.0 and increasing investments in the space. The report defines enterprise use-cases for metaverse and its corresponding benefits across multiple dimensions (internal vs external, people vs process, multiple interactions, real-time vs hypothetical vs historic process). It further explores in detail examples on how metaverse might be applied across various industry verticals and recommends ways in which enterprises can benefit from the emerging concept.
Metaverse, a still nascent cluster of technologies, is set to make its presence felt across multiple industry verticals serving different dimensions. It can be used for person-to-person communication, either for internal interaction within the organisation, between people, for broadcast communication or for multi-party interaction. The popular enterprise applications/use-cases of metaverse includes launch of new collaboration tools, customer interaction, operational virtualisation and mixed reality. Furthermore, it can be used for interaction with processes and systems, as an extension of the digital twin concept.
Metaverse has a number of horizontal use cases that are applicable across most verticals, including customer care and collaboration. There are also numerous use cases specific to verticals, for instance in manufacturing (for the purpose of simulating production facility, autonomous vehicles, robotic applications and quality assurance), mining (to provide real-time information on mining operations, substitute for workers in harsh environment and remote driving of large mining equipment), retail (virtual retail outlets, product demonstrations and immersive branding experience), transportation (intelligent transportation, in-vehicle VR, entertainment systems and AR-based solution for warehouse workers) and the finance and insurance vertical (virtual customer care and sales, NFTs and crypto-currency).
IoT's role in reducing water consumption
Finally, another little extract from our report on how enterprises can use disruptive techs, particularly IoT, to meet their sustainability goals. Specifically in this instance we’re looking at water savings. Something that everyone is focusing on a bit recently, with high temperatures around the world.
The most significant impact on water consumption is through Agriculture, Smart Grid (mainly smart water meters) and Smart Buildings solutions.
Agriculture solutions include crop management solutions and drones to monitor the humidity and temperature of soil and identify the correct level of water required to maintain crops. On average, Irrigation Management and Soil Monitoring reduces water consumption by 25-30%.
Soil probes can also provide information on which sections of a field need more or less water, allowing farmers to automate, control and schedule irrigation timing and duration accordingly. On average, these solutions can reduce water consumption by 15-30%, but some organisations have experienced water saving benefits as high as 70-75%. Considering that agriculture is often the largest consumer of freshwater reserves in a country and 50% of water used in irrigation is wasted, these savings can have a significant impact on a country’s overall water sustainability. For instance, an avocado farmer in California saved 75% in water usage and cost by monitoring soil condition. In Chile too, implementation of remote sensors in fields has reduced the volume of water used by 70%. In addition to the above, integrating sensors in water tanks to monitor levels remotely and taking appropriate action can reduce water overflowing cases.
Smart water meter solutions decrease water consumption by enabling changed consumer behaviour resulting from frequent monitoring and increased awareness. On average, smart water (residential and commercials) meters can reduce water usage by 6-9%.
Turning to Smart Buildings, here we see around 10% of reduction in water consumption by installing water flow monitoring devices.
Other areas where we see savings include those related to reducing vehicle usage and therefore less car washing. It might seem like a marginal use case but the Volvo mobility car service survey reported 3.8 million litres of water savings due to fewer car washes in Stockholm in one year. Also Drone solutions with soil sensing technologies can reduce water consumption by 15-20%.
As ever, a link to the report is provided on the wirelessnoodle.com website, as well as a link to getting hold of a sample of the report, specifically focused on smart buildings.
Next week (and a few requests)
Just a reminder: if you’re enjoying the podcast I’d be obliged if you could leave a review. It’s much appreciated.
A quick plug now for the events we’ll be at in the next few months.
If you’ll be at Industry of Things World in Berlin, IoT Tech Expo in Amsterdam, the Things Conference in Amsterdam, Mobile World Congress Las Vegas, or IoT Tech Expo in Santa Clara, let me know. All in September/October.
Link to our events page, where we’ll be speaking, is posted on the wirelessnoodle.com page: Transforma Insights events.
And another request that I mentioned a few weeks ago. The request relates to 2G and 3G switch-off as mentioned a couple of episodes ago. The request is to hear from anyone who has been through a 2G (or 3G) switch off process themselves. We want to hear about experiences from real world customers. So if you know of anyone who has been through one or you are someone who has, I’d love to speak with you. Can be completely anonymous and we’re happy to share results of the findings of the research.
Next week I’ll be talking a bit about work we did on benchmarking the capabilities of different service providers in the digital transformation space. You may recall that we did this for the cloud hyperscalers a while back and we extended it over the course of the last year to include consultancies and industrial-focused organisations. And I’ll talk a bit about whether communications service providers ought to spin out their IoT business units.
I hope you can join me.