Blog: Internet of Things and The Elusive New Oil

Data is the new oil of the digital economy!

Have you seen that statement before? Probably yes!

We are constantly seeing stories of companies that managed to traverse the boundaries of “traditional” business models into one where value is created through data. And usually either creating new industries or, as it’s usually put, “disrupt” existing ones.

So, do I think that is incorrect? No! I’m fully behind the notion that the exponential growth of data together with new and stronger analytical possibilities will provide us an extremely fascinating future.

But just like a bow tie, it’s not easy to succeed and it’s not for every occasion. Almost all IoT pitches that I see somehow contains a part where data is intended to be made into a new revenue stream. And often when I dig into it, there is little practical detail on how to do it. This is even more true when looking at “traditional” businesses that want to make sure they don’t fall behind by making their existing products data generating tech wonders. Some do succeed, but taking survivors bias into account, the overwhelming number doesn’t.

And the truth is that a large majority of successful IoT products are not successful due to their analytic capabilities, but due to being connected. It might add to some extent value to the service, but it doesn’t define them. Some connected products are just that, products that are connected, and they do not need to revolutionize how you earn your money.

But of course, while pursuing data driven benefits as the end goal is a risk and can lead down the wrong path, ignoring it is even more of a risk. There is more or less always some value to bring from collected data, and the challenge is coming to an understanding of what that benefit is.

But don’t expect the opportunity to present itself. It requires a lot of hard work and expertise to identify the possibilities. Which is of course a good thing, as otherwise someone else would already have done it.

Moving on to a few practical tips on how to approach data analytics within IoT.

1 - Understand the data that can be or is being collected. What does the gathered data actually measure and what is its granularity and precision?

2 - Map any other data that’s relevant. This can be both internal (e.g. historic consumer data) and external data (e.g. weather data).

3 - Identify opportunities based on the identified available data. This is the hard part and requires an innovative approach or a good understanding of other case studies. Here is the crucial time to remember that not all opportunities will be revolutionary. It’s usually easier starting with something simple that’s easily understood.

4 - Understand the properties of the opportunities. Do they rely on raw or aggregated data? Are they based on real time analytics or do they rely on historical data? It also requires a choice of analytical method (e.g. Machine Learning might be an option?). These choices significantly alters the architecture and infrastructure needed.

5 - Decide which data to gather and in what form to store it. Brian Krzanich (Intel CEO) predicts that autonomous vehicles will consume 4 Terabytes of data every day. Deciding which data is relevant to store is important to not get overwhelmed by it. Should some data only be analyzed in real-time and then discarded? Is some data only valuable when aggregated? For how long is the data useful?

While it’s definitely a challenge, it’s important to have the expertise to be able to understand the opportunities arising with exponentially more data being collected. But at the same time the value of the collected data shouldn’t be inflated. A practical approach with a focus on real opportunities rather than a promise of an elusive golden data driven future is important.

More information:

Partner and Senior Advisor Mikael Rönde,, +46 (0)70 88 66 794

Positioning technologies currently applied across industries:

Global Navigational Satellite System: Outdoor positioning requires line-of-sight to satellites, e.g. GPS: the tracking device calculates its position from 4 satellites’ timing signals then transmits to receiving network
–    via local data network, e.g. wifi, proprietary Wide Area Network
–    via public/global data network, e.g. 3G/4G

Active RFID: A local wireless positioning infrastructure built on premises indoor or outdoor calculates the position based on Time of Flight from emitted signal & ID from the tracking device to at least 3 receivers or when passing through a portal. The network is operating in frequency areas such as 2.4 GHz WiFi, 868 MHz, 3.7 GHz (UWB – Ultra Wide Band), the former integrating with existing data network, the latter promising an impressive 0.3 m accuracy. Tracking devices are battery powered.

Passive RFID: Proximity tracking devices are passive tags detected and identified by a reader within close range. Example: Price tags with built-in RFID will set off an alarm if leaving the store. Numerous proprietary systems are on the market. NFC (Near Field Communications) signifies a system where the reader performs the identification by almost touching the tag.

Beacons: Bluetooth Low Energy (BLE) signals sent from a fixed position to a mobile device, which then roughly calculates its proximity based on the fading of the signal strength. For robotic vacuum cleaners an infrared light beacon can be used to guide the vehicle towards the charging station.

Dead Reckoning: Measure via incremental counting of driving wheels’ rotation and steering wheel’s angle. Small variations in sizes of wheel or slip of the surface may introduce an accumulated error, hence this method is often combined with other systems for obtaining an exact re-positioning reset.

Scan and draw map: Laser beam reflections are measured and used for calculating the perimeter of a room and objects. Used for instance when positioning fork-lifts in storage facilities.

Visual recognition: The most advanced degree of vision is required in fully autonomous vehicles using Laser/Radar (Lidar) for recognition of all kinds of object and obstructions. A much simpler method can be used for calculating a position indoor tracking printed 2D barcodes placed at regular intervals in a matrix across the ceiling. An upwards facing camera identifies each pattern and the skewed projection of the viewed angle.

Inertia: A relative movement detection likewise classical gyroscopes in aircrafts now miniaturised to be contained on a chip. From a known starting position and velocity this method measures acceleration as well as rotation in all 3 dimensions which describes any change in movement.

Magnetic field: a digital compass (on chip) can identify the orientation provided no other magnetic signals are causing distortion.

Mix and Improve: Multiple of the listed technologies supplement each other, well-proven or novel, each contributing to precision and robustness of the system. Set a fixpoint via portals or a visual reference to reset dead reckoning & relative movement; supplement satellite signal with known fixpoint: “real time kinematics” refines GPS accuracy to mere centimetres; combine Dead Reckoning and visual recognition of 2D barcodes in the ceiling.

LoRaWAN: A low power wide area network with wide reach. An open standard that runs at unlicensed frequencies, where you establish a network with gateways.

Sigfox: A low power wide area network reminiscent of LoRa. Offered in Denmark by IoT Danmark, which operates the nationwide network that integrates seamlessly to other national Sigfox networks in the world.

NFC: Used especially for wireless cash payments.

Zigbee: Used especially for home automation in smart homes, for example. lighting control.

NB-IoT: Telecommunications companies’ IoT standard. A low-frequency version of the LTE network.

2-3-4G Network: Millions of devices are connected to a small SIM card, which runs primarily over 2G, but also 3G and 4G.

Wifi: The most established standard, especially used for short-range networks, for example. in production facilities.

CATM1: A low power wide area network, especially used in the United States.