This content was created by the Data Sharing Coalition, one of the founding partners of the CoE-DSC.
The Data Sharing Coalition supports organisations with realising use cases at scale to exploit value potential from data sharing and helps organisations to create required trust mechanisms to share data trusted and secure. Organisations from different domains collaboratively define and realise use cases that create new value by sharing data. Besides new value creation, use cases provide real-life insights into necessary requirements to achieve mutual agreements and interoperability between different organisations, domains and/or data sharing initiatives.
In our blog section ‘use case insights Q&A’, you learn more about a data sharing use case from one of our participants or from organisations outside of the Data Sharing Coalition. Wouter Los, Consultant and Coordinator at our participant Amsterdam Data Exchange (AMDEX), provides insights. He also received input from Dirk Tuip, CEO of FacilityApps and Hayo Schreijer, Product Manager/Managing Director of Dexes Data Exchange.
1. Can you briefly introduce your organisation and use case?
This use case started when FacilityApps asked Amsterdam Data Exchange (AMDEX) to assist with the sharing of cleaning related data via the AMDEX infrastructure. FacilityApps develops and manages several software functionalities for the cleaning sector – one of the largest economic sectors in Europe – covering cleaning industries in buildings, trains, and ships. AMDEX is managing an emerging (hardware/software) infrastructure upon the existing Amsterdam Internet Exchange (AMS-IX). The cleaning industry is increasingly dealing with an array of relevant streaming data, such as space occupancy by humans, check-in and check-out data of people traveling with public transportation, the use of facilities/equipment or data from sensors in cleaning machines. Sharing of such data will stimulate more efficient cleaning and improve management (e.g. decide which cleaning company to contact, which contract to agree on) by the owners or operators of buildings, trains, etc. For example, if the public transport check-in and check-out data is combined with e.g. camera surveillance data, you know how busy it is in a specific location at a certain moment and therefore, the cleaners can work more efficiently.
While discussing the use case, it became apparent that upcoming sensors for microorganisms, including the detection of COVID viruses, might become a crucial additional alarm signal for health authorities. Since AMDEX focuses on the digital enforcement of data sharing agreements via its infrastructure, it was decided to involve the Data Sharing Coalition and ask if they could conduct an analysis. The result was that we discovered that there are six different actors who are or may be involved in cleaning in different ways: a cleaning company, cleaner(s), owner of the building, user of the building, manufacturer of sensors in cleaning devices and companies that facilitate data processing for cleaning companies or for the owner of the building. With the Data Sharing Coalition, we discovered these actors and discussed the roles of all actors – who can share what data, with whom and for how long (permissions). In the end, we distinguished the components that should be included in a standard data sharing agreement model. Others can use this model and adapt it by including the components that fit their specific situation and needs. Discover more about this use case.
2. Can you explain the current use case status?
The Data Sharing Coalition pursued the use case because it was a new challenge to study the sharing of streaming data. In this case, AMDEX and FacilityApps are involved, as well as a major cleaning company. This exercise was an eye-opener with respect to e.g. understanding the roles of involved actors, their required interactions and the implications of establishing data sharing agreements. The initial analysis was conducted with the Data Sharing Coalition on a restricted case with a few actors and data streams (e.g. how often are toilets being used). To see if the standard model we created works in a real-life situation, we are testing it in a realistic scenario: in an existing multipurpose building with a variety of users (offices, e-gaming facilities and others). Here, we are scaling up, for example by combining cleaning-related data with other data to make processes more efficient.
Furthermore, AMDEX has had interactions with other market sectors. These conversations revealed that there is a lot of interest in the applicability for the wider area of facility management – not just cleaning data. Amongst these are energy management, space usage conditions, rent, etc. Such cases are considered by several owners of larger buildings or compounds.
3. What challenges did you face when developing the use case?
One important challenge was the identification of ownership of different data streams. For example, is the data generated by a sensor in a vacuum cleaner owned by the sensor manufacturer, the building owner, the building user, the cleaning company, the cleaner or the data processor? Often, when you aren’t sharing data, you are also not interested in or thinking about such questions. But this is expected to change rapidly once the value of data is recognised. Rather than waiting for someone to take the lead, it is better to analyse applicable rights prior to emerging disputes.
Another challenge was dealing with the different frequencies of streaming data signals that data owners allowed, in relation to the preferred frequencies as needed by the different actors. In other words: with what frequency is the data produced, what does the owner allow and what do actors need? Such challenges have also been discussed with KPN (also a participant of the Data Sharing Coalition), since this company is interested in managing and sharing streaming data, for example from vehicles in relation to mobility challenges.
4. Which best practices/lessons learned would you like to share?
Since this case was the first one on streaming data, both the Data Sharing Coalition and the stakeholders involved learned by analysing all aspects when sharing such data. A lesson learned was that it is important to explain to the different actors involved how they will benefit from streaming data. Apart from data processing, this may cover issues such as combining streaming data, delivery of new data streams based on data combinations, and the issue of data ownership.
For the cleaning sector, this resulted in new views that need to be turned into standards. To this end, FacilityApps presented its insights to the EFSI association (European Cleaning and Facility Services Industry), which are now being discussed for adoption. Another spin-off is the extension to the much larger use case “Sharing Sensitive Smart Building Sensor Data on AMDEX”, with contributions of iShare, to facilitate improved interoperability and upscaling, e.g. to more users and bigger buildings that have more data and different kinds of data (not just cleaning data, but also energy or security related data). The focus is on facility management with data from private and public spaces.
5. What type of use case support or expertise did (do) you receive from the Data Sharing Coalition? How can the Data Sharing Coalition be of assistance in the future?
The Data Sharing Coalition was very supportive in guiding the way to analyse the use case based on the Data Sharing Canvas. By untangling the different components as identified in this Canvas, a better picture emerged of what to arrange in data sharing agreements. It was also an eye-opener with regards to new data sharing opportunities. As mentioned before, one of these is scaling up to the wider challenge of facility management – thus focus on various kinds of data, not just cleaning data. Clearly, next steps would include involving the Data Sharing Coalition and/or interested coalition members with a focus on applicable model agreements and services for cross sectoral data sharing in support of facility management.
Do you want to know which use cases we are realising within the CoE-DSC? Discover them here.