Internet of Things.
A popular term for an ecosystem of machines, sensors, chips, vehicles, servers etc exchanging information.
The ability to gather high volumes of data and do something useful with it. One step further, have machines do something with it.
Adoption of ‘new’ digitally fueled processes allowing a competitive advantage.
So we have machines (not just computers and servers, but also cars, washing machines, mobile phones, security gates, trucks, camera’s, boats, warehouses etc) and these machines all communicate. Mostly, they will give status updates such as ‘ok/nok’, ‘open/closed’, ‘on/off’, ‘broken/service’. This can be a continuous status update, a frequent status update, on request or on event.
In case of sensors, this could be temperature, humidity, contact to water, movement, location etc.
This is not really new, but there is still work to be done.
Actionable eventsThe information sent from these machines is all gathered, and depending on the defined processes, can lead to an action.
Simple examples would be:
• ‘A car detects a crash > 911 is called’.
• ‘The washing machine is broken > a service engineer is notified’.
• ‘Smoke is detected > sound the alarm’.
• ‘The container is moved > Update the shipment document’.
Research and analysisIn some cases, IoT data is not used for specific actionable events as mentioned above, but exist specifically for research and analysis reasons. This can be for instance:
• Temperature measurements to predict weather
• Website traffic analysis
• Movement patterns of mobile phones
• Location tracking of chipped animals.
Pattern-based AIWith all this data, in an ideal world, we would be able to generate artificial intelligence based on data patterns.
Simplified, this would make that the system analyses the data, creates an actionable event and analyses the changed state in order to get an optimal result. For instance, in web traffic analysis, what if a system could find the perfect way to re-attract customers to their abandoned shopping cart solely based on big data analysis and self-initiated experiments. Still a dream, right? In some cases it is, in some cases it isn’t. Ever wondered why Google sometimes tells you the travel time to your next destination without you ever asking for it? Or why Facebook asks you if you want to check in to an event, and sometimes doesn’t?
Digital transformationIn some industries, digital transformation is already well on its way. Think of logistics, food industry, climate and soil control in hightech agriculture etc, where sensors and communicating machines are already embedded.
In the service industry for instance, it is merely starting up. We have seen Uber as often-used example getting a competitive edge from their super-user-friendly app.
We can all understand that improving our products using big data insights or extra digital services creates a competitive edge. The same goes for spending our marketing budgets most efficiently (using for instance SAP Hybris Marketing) and keeping track of customer's preferences.
With recent improvements, doors are open for the next steps.
In-memory big data analysis allows more complex and faster analysis, allowing better A/B testing bringing us closer to our A.I. Utopia.
Connected data has been increasing with tremendous speeds over the last decade.
The LORA-protocol and network will enable us to equip more and smaller objects with a communicating chip with a proper battery lifetime.
Hardware prices are still declining solving the businesscase for 'smart' common goods .
Think big.What if your shoes would have a chip. Or rented bikes.
What if every window could communicate it’s status (open or closed) when you leave the house.
What if your car would know the closest free parking spot in the centre of Amsterdam?
What if the elevator at your company already knows which floor you are heading?
What if your glass could tell the waitress it’s empty. Or if we would know which brand of larger would result in the fastest empty glass?
Data to be gathered, information to be found.
As I said. Work to be done!