Mashing up IoT technologies for Commercial Real Estate

Othmane Zrikem is the Chief Data Officer and a founding member of A/O PropTech, where he designs and executes innovative investment strategies combining data-driven methodologies and financial engineering.
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Mashing up IoT technologies for Commercial Real Estate

Othmane Zrikem
January 13, 2020

Commercial real estate tenants, specifically retailers, have been early adopters of several IoT technologies. Large technology companies are trying to be more granular in the insights they provide to CRE tenants.

For instance, to determine shelf and product attractiveness in retail, Microsoft combines sensor data from in-store shelves with surveillance cameras footage on its Azure IoT Central platform.

The goal is to correlate live inventory with footfall. In the United States, some retailers are using this technology to optimise logistics and in-store analytics, a use that is still in its infancy in Europe.

Microsoft is not the only one to rely on this practice. SES Imagotag (through its smart labels) and Cenareo (with dynamic displays), intend to link information from various sources to provide their customers with enriched data.

These technologies make it possible to aggregate and analyse data from a variety of equipment. In the retail sector, this makes it possible to couple image recognition, ultrasound and RFID in particular.

The combination of technologies has continued to improve over several years in all sectors of activity.

Nevertheless, some manufacturers have recently been mixing BtoC solutions with BtoB solutions to address the specific needs of Commercial Real Estate.

It opens up the field to a large number of possibilities. CRE landlords and tenants can now have multi-technology reasoning incorporating various contributors.

It is indeed necessary to strengthen and federate all stakeholders, as the added value of IoT lies in heterogeneous data. The power of insights derived from data collected through various sources allows improving operating modes, performances and customer relationships equally.

To achieve this combination of technologies, manufacturers must rely on a platform capable of implementing different data. The standard best practice is to federate the solutions within an IoT Data Hub to collect data from sensors, enrich them with contextual elements, and apply data science algorithms on top of these curated data sets.

Several start-ups are developing platforms to reconcile images and raw data from connected objects. These make it possible to automate visual inspection.

To monitor the state of the power grid, for example, drones monitor vegetation while lidar feeds back information on pylons. It is also possible to coupling drone’s inspection with truck data to facilitate operations in large construction sites.

However, this assembly of data requires strong organisation and senior management support from companies. The data is heavy and heterogeneous; it must be geo-localised, historicised, structured and analysed. It is necessary to think about the algorithmic processes to know where to apply machine learning in information processing.

The cost could also represent a hurdle; it is necessary to deploy the sensors, the cameras, then to equip oneself with the right platform that supports all the technologies. Depending on the project, this could cost millions of euros.

5G could subsequently facilitate the combination of technology. Its ability to handle extensive data flows would encourage best practices to benefit from the best of each technology.

This combination is essential because siloed data analysis limits the potential of IoT insights.

Implementing interconnected and interoperable solutions will allow the emergence of end-to-end applications. We think this would enhance the value proposition in the Commercial Real Estate sector.

About the author
Othmane Zrikem
Senior Advisor & Venture Partner

Othmane Zrikem is a Moroccan-French Data Science & Digital technologist. He is the Chief Data Officer and a founding member of A/O, where he designs and executes innovative investment strategies combining data-driven methodologies and financial engineering.

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