The data stream, fuel for your digital projects
(or... the lake and mills)

The data stream, fuel for your digital projects
Moulin de Lugy, in Hauts-de-France

In communities, digital projects most often start with a preliminary project for capturing various information by means of sensors (Internet of Things – IoT) followed by their storage in a “data lake” of more or less great depth.

This approach, quite natural, comes up against reality: on the one hand the data are perishable goods, on the other hand, once plunged into the abyss of the lake, the data is damaged there: it is no longer possible to recover their energy to supply business scenarios. It is not for nothing that, historically, mills have been installed upstream, before the river flows into the lake, to harvest all its value in real time and convert it into energy.

Bad news: data is perishable

More precisely, it is the practical utility of the data that rapidly diminishes over time. Take the simple example of an electric vehicle charging station. We will easily do the transposition to the parking lot, metering (water, energy, people …), air quality, temperature … just about everything in reality.

In the medium term, the information relating to our terminal (in service / out of order, free / occupied, energy consumed) and its context (location, identifiers, time stamp, etc.) are useful to deduce averages: rate of usage, usage profile (day / night, etc.), seasonality, availability of the service, probability of finding a free terminal, etc. This restated information is necessary to manage investments and measure the impact of the service thus provided to users.

On the other hand, detailed information, minute by minute, loses all usefulness – unless this data is used on the spot, for example to inform owners of electric vehicles of the number and location of the terminals available when they have such need.

Note for those who like small “rule of thumb” calculations (with a big thumb): suppose that the different states of our charging station are read every minute and that this reading corresponds to a recording of 1 kbyte. Over a year, it will need 365 * 24 * 60 * 1kbyte, or around 500 Mbyte / year / terminal. Considering that this information will be duplicated, archived, etc. we will have a final volume of which the order of magnitude will be 1 Gb / year / terminal. The 2030 objective being to be well beyond one million terminals for France alone, we will therefore speak in Petabytes ..

the data stream, fuel for your digital projects

The Iguaçu Falls, between Argentina and Brazil

Good news: data in motion has inherent energy

Like light, data has no mass, but that doesn’t prevent it from having its own energy, quickly dissipated over time. In the previous example, instant knowledge of the condition of a charging station is of value to anyone who needs to recharge their vehicle in a given time.

In industry, immediate information on the setting of a machine will help avoid producing a defective batch (see article on industrial maintenance). In another context, the instantaneous profile of the water consumption of a house will give a useful indication on the activity of an isolated person…

In a current situation, the direct knowledge of the CO2 rate in a public place (school, auditorium, restaurant…) will allow to warn the person in charge (teacher, technical director, head of the auditorium… see video Air quality). And why not to directly control a ventilation system – provided that you have access to this information in real time, know what to do with it and have the necessary means of action.

The data stream, fuel for your digital projects

Data energy: a source of value for digital projects

To perform the transformation between data and useful actions in practice within digital projects, a number of prerequisites are necessary:

  • Obtain that data, of course. And this, whatever the sources: connected objects, applications, web service (weather, traffic, air quality, etc.) – not to mention the people themselves;
  • Correlate these data in real time, before their energy is dissipated, and draw practical decisions, applicable immediately: control ventilation, adjust a BMS, activate doors, close (open) lighting – thanks to bespoke scenarios, easy to define by the responsible officers;
  • Communicate and involve teams, partners, residents, if possible without imposing on them new tools or applications.

Converting the kinetic energy of a stream – before it dissipates – into useful work is the job of the mills. Lakes provide rest and eternal oblivion: don’t you think communities need the former more than the latter?

If this article interested you, you could also read Cross-functionality, a source of performance for a digital city or go directly to our page dedicated to Cities and Territories.

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