Knowable Building Framework – building consent – Rennes workshop

En français

The Knowable Building Framework is a consent building framework for the release and sharing of data from sensor data captured in buildings. The Rennes workshop took place on the 18th January with 22 participants who had an interest in energy, internet of things, environment and civic technology. The meeting took place at Telecom Bretagne (IMT Atlantique).

The focus of the discussion was broadly split into technology- looking at the types of sensors being deployed in the pilot project and the data being created- and human- how the data could be used to affect behavioural change and the implications of having a dense sensor network in a building.

Technology

The Knowable Building Framework pilot in Manchester is located in an office building with sensors on a number of floors. The sensors measure temperature, humidity and motion on two floors and another floor also measuring light. The sensors are discrete and battery powered using a LoRaWAN (Long Range Wide Area Network) to send data to an intelligence platform that displays measurements on a dashboard. The information can then be used to determine time of activity within a building and how different areas are used; temperature variation over time allowing the ability to adapt heating so that it is more inline with occupancy; and humidity within areas which can be used to identify underlying issues with ventilation and damp. The light sensor modules allow the detection of room lighting.

Placement of sensors

Placement of the sensors within the building was based upon the need for the system to optimally collect data. Zones on floors, entrance areas, meeting rooms are covered. Importantly the sensors were placed in what could be considered common usage spaces i.e. spaces that were owned and operated by the building company for the benefit of all tenants and users. Areas where a tenant had exclusive access were excluded.

Preliminary insight gained from the network

A small number of sensors has already been installed and we can see from the online interface that useful data is already being created. Even at this stage we can see start to see how the building functions over the course of a number of days. We can deduce when the disparate heating systems kick in, which areas have a thermal lag, when cyclists use the storage and shower facilities and the areas that are more humid than others. As the sensor network becomes denser we will be able to see how different areas get used offering the ability to heat certain areas based upon demand.

This all looks very positive from a building management ‘mechanical’ perspective but we already can see that buildings don’t operate in isolation of the people who inhabit and work within them. At this early stage we can see how certain rooms and areas are used more regularly than others. We can start to infer patterns of activity and behaviours of the small number of people who operate the buildings on behalf of tenants. During the day the building is awash with activity that creates a certain anonymity, but out of hours, areas get cleaned, security personnel check spaces and these show up as isolated trigger events within the building. It does not take a big leap of the imagination to realise that a building optimisation platform could also be used as an instrument of surveillance.

Discussion

There was broad agreement by the group that creating open data from sensor data was a good thing, especially if it could be used to benchmark buildings and enable a greater understanding of how buildings of different types behaved.

Ownership of data was seen as being important. Who is the owner of this data – the building owner or the people who inhabit or work in the space? It was highlighted that there is a relationship between ownership of the data and the power/agency people have within the space. So if you are a building owner you are more likely to feel that the data that you collect is yours as it is your building and a private space even though some of this data may be used to identify individuals. This is analogous to CCTV in that people in CCTV monitored buildings generally have very little say or knowledge of how the systems are operated other than ‘its for your security and safety’, how video is stored or who it is shared with.

Identification of need is really important with creating a sensor network or else initiatives are prone to poor application of technology with the creation of unusable data. There is also a tendency to over collect data or collect the wrong type of data. It was suggested that the forthcoming GDPR regulations might have a bearing on how data is collected within buildings and public spaces.

A criticism of the Knowable Building Framework pilot in Manchester is that the implementation has been undertaken with only the consent of the building managers who can see tangible benefit in seeing how the building operates with little consideration of its occupants.

The group identified that we need to explore how we build networks consensually as this would then be a precursor to building consent. Consensus assumes a number of things that potentially could be challenging for the implementation of sensor networks these might be:

  1. An understanding of the sensor environment’s purpose and how the data will be used
  2. The technology and what it measures
  3. What the safeguards are so that the technology is not abused
  4. Representing concerns if the technology is considered to be abused
  5. Understanding of data sharing and open data

The data that is collected from the sensors in its raw form is a rich sense of information but sharing the data as open data has risks. There are different levels of granularity that could help data owners share data but care would need to be taken in order to identify the appropriate level of aggregation and anonymisation. There is a trade-off between usefulness and privacy.

There was a some discussion on the value of the data created and if it could be used as a commodity to draw down preferential deals for building owners and building users alike. This could have some kind of leverage regarding power suppliers and building insurers.

There exist different classes of stakeholders within the building and it is important that any consent mechanism acknowledges them. These classes roughly break into; owners and management; occupants and users and; maintenance and operational.

Data needs to be available – or at least the intelligence that is derived from the data so that occupants and users of the building can understand for themselves how the building works. This would help in demystifying the technology and make it less threatening but also help people make changes to their own behaviours.

Visualisation could have a big part to play in this process as good visualisation and design enable understanding. By making the data available there is also the possibility to run events around the data to create new ways of creating information and understanding. Rennes has used the Data Remix (a form of hack event) format successfully for a number of challenges and it is suggested that one could be run that includes teams from Rennes in the future.

A further write up of the whole day (in French) can be found here

Faire un compte rendu à partir de l’atelier de travail

« Knowable Building Framework » est un modèle d’ouverture et de partage de données issues de capteurs qui collectent des données au sein des bâtiments. L’atelier de travail à Rennes a eu lieu le 18 janvier 2018 avec la participation de 35 personnes ayant une forte appétence pour les sujets énergétiques, l’internet des objets (IoT), l’environnement et l’appropriation de la technologie par les citoyens.

La rencontre s’est tenue à Telecom Bretagne (IMT Atlantique).

La discussion s’est essentiellement focalisée sur deux volets :

D’abord technologique – il a été question de chercher des types de capteurs déployés dans le projet pilote et les données créées – et d’autre part Humain – comment les données pourraient être utilisées pour induire un changement comportemental et les implications d’avoir réseau de capteurs dense dans un bâtiment.

Technologie

Le projet pilote « Knowable Buildings Framework » à Manchester est situé dans un espace de Co-working contenant plusieurs capteurs répartis sur un certain nombre d’étages. Les capteurs mesurent la température, l’humidité et les mouvements sur deux niveaux et sur un autre niveau, les capteurs incluent également la luminosité.

Les capteurs sont discrets et sont alimentés par des batteries LoRaWAN (Long Range Wide Area Network) pour envoyer des données à la plateforme intelligente qui affiche les mesures sur un tableau de bord.

Les informations peuvent ensuite être utilisées pour déterminer l’activité au sein du bâtiment et la manière dont les différentes zones sont utilisées.

La variation des températures au fil du temps permet d’être en mesure d’adapter le chauffage afin que ce dernier soit plus en adéquation avec l’occupation de l’espace. 

Les informations relatives à l’humidité dans les espaces peuvent être utilisées pour identifier des problèmes sous-jacents tels que des problèmes de ventilation ou d’humidité.

Les modules de capteurs de lumière peuvent être utilisées pour détecter une présence afin d’éclairer la pièce.

L’emplacement des capteurs

Le placement des capteurs dans le bâtiment est basé sur le besoin pour le système de collecter de manière optimale les données. Les espaces au niveau des étages, les zones d’entrée et les espaces de réunion sont couvertes pour les besoins de l’expérimentation.

Point important, les capteurs sont placés dans ce que l’on pourrait considérer comme des espaces communs, c’est à dire les espaces qui appartiennent et sont exploitées par l’opérateur pour le bénéfice de tous les locataires et utilisateurs du bâtiment. Les zones où les locataires avaient un accès exclusif ont été exclues.

Aperçu préliminaire issu du réseau

Un petit nombre de capteurs ont déjà été installés et nous pouvons voir à partir de l’interface en ligne que des données utiles ont déjà été créées. Même à ce stade, nous pouvons voir comment fonctionne le bâtiment en l’espace de quelques jours. Nous pouvons voir les variations de température du système de chauffage, les zones avec un décalage thermique et les zones qui sont plus humides que d’autres.

Au fur et à mesure que le réseau de capteurs se densifie, nous pouvons voir comment différentes zones sont utilisées, offrant la possibilité de chauffer certaines zones en fonction de la demande.

Tout cela semble très positif d’un point de vue « mécanique » de la gestion du bâtiment, mais nous pouvons déjà constater que les bâtiments ne fonctionnent pas isolément des personnes qui y habitent et qui y travaillent.

A ce stade précoce, nous pouvons voir comment certaines pièces et zones sont utilisées plus souvent que d’autres. Nous pouvons commencer à déduire des modèles d’activité et de comportement d’un petit groupe de personnes qui exploitent le bâtiment.

Pendant la journée, le bâtiment est inondé d’activités qui créent un certain anonymat, mais en dehors des heures d’ouverture, les zones sont nettoyées, le personnel de sécurité vérifie les espaces et ceux-ci apparaissent comme des événements déclencheurs isolés dans le bâtiment.

Il ne faut pas faire un gros effort d’imagination pour se rendre compte qu’une plateforme d’optimisation des bâtiments pourrait également être utilisée comme un instrument de surveillance.

Discussion

Le groupe a été largement d’accord pour dire que la création de données ouvertes à partir des capteurs a été une bonne chose, surtout si elle pouvait être utilisée pour comparer les bâtiments et permettre une meilleure compréhension du comportement des bâtiments de différents types.

La propriété des données était considérée comme importante. Qui est le propriétaire de ces données – le propriétaire du bâtiment ou les personnes qui habitent ou travaillent dans le bâtiment – Il a été souligné qu’il existe une relation entre la propriété des données et le pouvoir que les personnes ont dans l’espace.

Ainsi, si vous êtes propriétaire d’un bâtiment, vous avez plus de chances de penser que les données que vous collectez vous appartiennent, car il s’agit de votre bâtiment et d’un espace privé, même si certaines de ces données peuvent être utilisées pour identifier des individus.

De manière analogue aux caméras de surveillance dans la mesure où les personnes se soucient peu de la manière dont la vidéo est stockée ou avec qui elle est partagée, dans les bâtiments, les personnes ont généralement peu de choses à dire ou à savoir sur le fonctionnement des systèmes.

L’identification des besoins est vraiment importante avec la création d’un réseau de capteurs pour éviter que les initiatives soient sujettes à une mauvaise application avec pour conséquence, la création de données inutilisables.

Il y a aussi une tendance à trop recueillir de données ou à collecter le mauvais type de données. Il a été suggéré que les règlements RGPD à venir pourraient avoir une incidence sur la façon dont les données sont collectées dans les bâtiments et les espaces publics.

Une critique du projet pilote « Knowable Building Framework » à Manchester est que la mise en œuvre a été entreprise avec juste le consentement des gestionnaires de l’immeuble qui peuvent voir un avantage tangible sur le fonctionnement du bâtiment. Ces derniers n’ont cependant pas pris la peine de recueillir l’avis des occupants du bâtiment.  

Le groupe a identifié que nous avons besoin d’examiner la manière dont nous construisons les réseaux de manière consensuelle, car cela serait précurseur de la construction du consentement.

Un consensus suppose un certain nombre de choses qui pourraient potentiellement être difficile à mettre en place pour la mise en œuvre des réseaux de capteurs. Ils pourraient s’agir de :

  1. La compréhension de l’objectif de l’environnement du capteur et de la façon dont les données seront utilisées.
  2. La technologie et ce qu’elle mesure.
  3. Des garanties pour que la technologie ne soit pas utilisée à mauvais escient.
  4. La représentation des préoccupations si la technologie est dévoyée.
  5. La compréhension du partage de données et des données ouvertes.

Les données collectées à partir des capteurs dans leur forme brute sont riches en informations, mais le partage des données en tant que données ouvertes présente des risques. Il existe différents niveaux de granularité qui pourraient aider les propriétaires de données à partager des données, mais il faudrait prendre soin d’identifier le niveau approprié d’agrégation et d’anonymisation. Il y a un compromis entre l’utilité et la vie privée.

Il y a eu une discussion sur la valeur des données créées et si elles pouvaient être utilisées comme une marchandise pour tirer des accords préférentiels pour les propriétaires de bâtiments. Cela pourrait avoir une sorte de levier en ce qui concerne les fournisseurs d’énergie et les assureurs du bâtiment.

Il existe différentes catégories de parties prenantes dans le bâtiment et il est important que tout mécanisme de consentement les reconnaisse. Ces parties prenantes sont pleinement impliquées dans le bâtiment. Nous avons les propriétaires et les gestionnaires ; les occupants et utilisateurs et la maintenance et les opérationnels.

Les données doivent être disponibles – ou au moins les renseignements dérivés des données afin que les occupants et les utilisateurs du bâtiment puissent comprendre par eux-mêmes comment fonctionne le bâtiment. Cela aiderait à démystifier la technologie et la rendrait moins menaçante, mais aiderait aussi les gens à modifier leurs propres comportements.

La visualisation pourrait jouer un grand rôle dans ce processus, car une bonne visualisation et un bon design permettent la compréhension. En rendant les données disponibles, il est également possible d’exécuter des événements autour des données afin de créer de nouvelles façons de créer l’information et de la comprendre.

Rennes a utilisé avec succès le format Data Remix (sous le format d’un hack event) pour relever un certain nombre de challenge et il est suggéré d’en faire un dans le futur en incluant les équipes de Rennes.

Vous trouverez c-dessous des informations complémentaires en français de notre atelier de travail, sur le lien suivant.

Knowable Building Framework

Helping building owners save energy, money and the environment through data

En français

The Knowable Building Framework is a UK – France collaboration project funded by the Open Data Institute that seeks to strengthen commercial opportunities and tackle societal challenges through data. It is a collaboration between Open Data Manchester, Rennes Metropole, Sensorstream and Things Manchester.

The Knowable Building Framework will develop an internet of things consent framework for monitoring the performance of older commercial buildings in a non-invasive way using discrete low power sensors, and if appropriate publishing the data from these sensors as open data. Unlike modern stock, older buildings often fall behind as far as the utilisation of new technology is concerned. Many landlords undertake a certain amount of retrofitting such as zonal heating or movement detection systems but these tend to be ad hoc and unconnected, with no ability to monitor how effectively these systems are working either singly or together. The internet of things and the analysis of data derived from sensors can give landlords, building management and tenants insight into the performance of buildings, enabling adaptations that can be economically and environmentally beneficial, whilst also creating opportunities for behaviour change within those buildings.

The project will utilise the Things Network that covers a large proportion of Greater Manchester and communities across the North with free and open Internet of Things connectivity and will allow the project team to design and connect sensors and analytics platforms seamlessly to the internet. The power of the project will come from the ability to share an appropriate amount of data across portfolios of buildings and also to the wider community as open data. Enabling insight to be gathered across the city.

The sensors

Designed and provided by Sensorstream, the sensors will have the capability to measure temperature, light, humidity and occupancy as well as a variety of other relevant conditions. The sensors are discrete measuring approximately 90mm x 130mm, lightweight and powered by a 3V AA batteries that can, depending on setup, operate over many years.

The network

The sensors connect to Manchester’s public Long Range Wide Area Network managed by Things Manchester. This commons-based network provides the capability for communities throughout Greater Manchester to connect internet of things enabled devices for free.

The analysis

Data from the sensors is aggregated into a dashboard interface that shows the operating characteristics over time, enabling the planning of control measures or behavioural change initiatives.

The Framework

The main focus of the project is the development of a framework that will help building owners and operators understand the data that buildings can produce and create a consent mechanism so that data can be shared and released as open data. There are many reasons why the release of this data may be contentious and the Knowable Building Framework seeks to work with building owners to identify and understand these reasons, the risks and the mitigations.

The How

Over the next two months the pilot sensor environment will be installed in Federation and will be used as the basis of the framework. Open Data Manchester will also be running a series of workshops in Rennes and Manchester with building owners, technologists and city officials to try and understand the challenges and utility of sharing building performance data.

The framework will be designed as an open source tool that can then be used to develop similar consent mechanisms for sensor data in other scenarios.

For more information contact Julian Tait julian[at]opendatamanchester[.]org[.]uk

Knowable Building Framework

Aider les Propriétaires de bâtiments à faire des économies d’argent, d’énergie et à préserver l’environnement.

The Knowable Building Framework est un projet de collaboration entre la France et UK financé par l’Open Data Institute qui vise à renforcer les opportunités commerciales et à relever les défis sociétaux à travers l’utilisation des données. C’est une collaboration entre Open Data Manchester, La métropole de Rennes, Sensorstream et Things Manchester.

The Knowable Building Framework développera un modèle de consentement pour l’internet des objets afin de surveiller la performance des bâtiments commerciaux anciens dans une logique non-intrusive, en utilisant des capteurs discrets de faible puissance et dans la mesure du possible, en diffusant les données de ces capteurs comme données publiques.

Contrairement aux bâtiments modernes, les bâtiments anciens sont souvent en retard dans l’utilisation des nouvelles technologies. De nombreux propriétaires procèdent à certaines rénovations notamment sur les zones de chauffage ou les systèmes de détection par mouvement, mais ceux-ci ont tendance à rester ponctuels et non connectés, sans la possibilité de contrôler l’efficacité de ces systèmes seuls ou ensemble.

L’internet des Objets et l’analyse des données issues des capteurs peuvent donner aux propriétaires, aux exploitants et aux locataires un aperçu des performances des bâtiments permettant ainsi de faire des ajustements qui seront bénéfiques tant d’un point de vue économique qu ‘écologique, tout en créant des opportunités de changement de comportement dans ces bâtiments.

Le projet utilisera Things Network qui couvrira une grande partie du Grand Manchester et des communautés du Nord, avec une connectivité internet gratuite et ouverte qui permettra à l’équipe projet, de concevoir et de connecter les capteurs et les plateformes analytiques à internet.

La puissance du projet viendra de la capacité à partager la quantité appropriée de données, à travers les portefeuilles de bâtiment, à une communauté plus large en tant que données ouvertes. Permettre que des idées puissent être recueillies à travers la ville.

Les capteurs

Désigné et fourni par Sensorstream, les capteurs auront la possibilité de mesurer la température, la lumière, l’humidité et l’occupation de l’espace ainsi que divers autres éléments pertinents. Les capteurs sont discrets, mesurant à peu près 90mm x 130mm, légers et alimentés par une pile AA 1,5 V qui, selon la configuration, peut fonctionner plusieurs années.

Le Réseau

Les capteurs se connectent au réseau public étendu de Manchester géré par Things Manchester. Ce réseau commun permet aux communautés à travers le Grand Machester de connecter gratuitement des appareils compatibles à internet.

L’Analyse

Les données des capteurs sont agrégés dans l’interface d’un tableau de bord qui montre les caractéristiques de fonctionnement dans le temps, permettant la planification de mesures de contrôle ou des initiatives de changement de comportement.

Le Modèle

Le but principal du projet est de développer un modèle qui aidera les propriétaires de bâtiment et les exploitants à comprendre les données que les bâtiments peuvent produire et de créer un mécanisme de consentement afin que les données puissent être diffusées et partagées en tant que données ouvertes.

Il y a plusieurs raisons pour lesquelles la diffusion de données est controversée et le Knowable Building Framework cherche à travailler avec les propriétaires de bâtiments pour identifier et comprendre ces raisons, les risques et les assouplissements qui pourraient exister.

Comment ?

Au cours des deux prochains mois, l’environnement du pilote capteur sera installé dans le bâtiment “Fédération”  et sera utilisé comme un modèle de base. Open Data Manchester réalisera une série d’ateliers à Rennes et à Manchester avec les propriétaires de bâtiments, des techniciens et des responsables de la Métropole pour tenter de comprendre les défis et l’utilité de partager les données de performance des bâtiments.

Le modèle sera désigné comme un outil open source qui, par la suite, puisse être utilisé pour développer des mécanismes similaires de consentement pour les données issues de capteurs dans d’autres scénarios.

Knowable Building Framework

Open Data Manchester working with Sensorstream Ltd and Things Manchester is developing a platform for gathering, analysing and sharing insight from sensors within buildings.

The Knowable Building Framework is an Internet of Things framework for monitoring the performance of older commercial buildings in a non-invasive way using discrete low power sensors, and if appropriate publishing this data as open data. Unlike modern stock, older buildings often fall behind as far as the utilisation of new technology is concerned. Many landlords undertake a certain amount of retrofitting such as zonal heating or movement detection systems but these tend to be ad hoc and unconnected, with no ability to monitor how effectively these systems are working either singly or together. The internet of things and the analysis of data derived from sensors can give landlords, building management and tenants insight into the performance of buildings, enabling adaptations that can be economically and environmentally beneficial, whilst also creating opportunities for behaviour change within those buildings.

The initiative will harness the connectivity of the public Things Network, that covers a large proportion of Greater Manchester and across the North, and will allow the project team to design and connect sensors and analytics platforms seamlessly to the internet. The power of the project will come from the ability to share an appropriate amount of  data across portfolios of buildings and also to the wider community as open data, enabling insight to be gathered across the city. This will have the further benefit of not only measuring building performance but connecting other sensor data as as well.

It is a collaboration with the City of Rennes in Brittany, seen as a centre of excellence regarding the development of Low Power Wide Area Networks and open data, and is funded through the Open Data Institute.

We will be running a workshops in Rennes and Manchester with building owners and technologists in January and February, to understand how better to design and implement the framework. If you would like to be involved, email us at hello [a] opendatamanchester.org.uk

Internet of Things and Open Data Publishing

Tuesday October 3rd 10.30 – 13.30

FACT
88 Wood Street
Liverpool
L1 4DQ

Register for free here

If you have an interest in internet of things and how the data produced can contribute to the broader data economy, this is your chance to have a say.

The internet of things offers unparalleled means to create data from sensors, devices and the platforms behind them. This explosion of connectedness is creating huge opportunity for building new products and services, and enhancing existing ones. With these opportunities come some gnarly challenges. These exist around standards in data and protocols, security, discoverability, openness, ethics and governance. None of these are trivial but all of them need to be understood.

This workshop is for people involved in open data, Smart Cities and the internet of things who are starting to come up against and answer some of these challenges.

It is being run by Open Data Manchester and ODI Leeds for the Open Data Institute to look at the future of open data publishing and IoT

The Open Data Institute (ODI) is always working towards improvements in open data – from making it easier to find and use right through to refining and implementing standards. They are very keen to work with people who use open data to see what they can be doing to help and improve open data for everyone.

The workshops are open to everyone who wants to join in, contribute, or work with us. The output from the workshops will be put forward to the ODI and the UK government with recommendations on how open data should be published.

Refreshments and lunch will be provided.

If you can’t make it but would still like to contribute, we have an ‘open document’ available here. We encourage people to add their questions, comments, suggestions, etc.

After the workshop there is the launch of LCR Activate a £5m project led by Liverpool John Moores University with the Foundation for Art and Creative Technology (FACT) and the LCR Local Enterprise Partnership. A three-year European Regional Development Fund (ERDF) initiative using AI, Big Data/High Performance Computing, Merging Data and Cloud technologies for the benefit of SMEs in the Liverpool City Region. Register here.

Provisional programme for 2017

IMG_0066From last night’s planning meeting we now have a provisional programme for 2017 and it is quite an ambitious one. What is great from our perspective is that there is a continuation of a number of themes that we have been looking at over the last year and a resurfacing of perennial ones. Highlights include the ‘making and doing’ workshops that have been developed as part of the Echo Chambers and ‘Post-Fact’ Politics programme and the Visualising Data workshops. There are a number of sector and technically specific events but one to watch out for is alternative ways of looking at the world which will be a day of walks, talks and explorations. As always there is a large dose of how data and technology impact on society and much more.

This is a provisional programme and we are looking for as much input as possible (Dates and sessions are subject to change). Please click on the Google Doc and add comments. We are looking for people who can contribute, sponsors, venues and partners.

Link to Google Doc

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Making data useful and other stories – How GM authorities are using data to help their citizens

6.30pm – 8.30pm, Tuesday 27th September 2016
Greenheys Business Centre
Manchester Science Park
Pencroft Way
Manchester M15 6JJ

Map here

Sign up on Eventbrite here

This month’s Open Data Manchester looks at how two local authorities are using data to deliver service.

Alison Mckenzie Folan and Alison Hughes from Wigan Council will show how they are using data and open data to help them engage the community, target resources and enhance services. Wigan Deal has been seen as an exemplar of engagement between the public sector, local businesses and community.

Jamie Whyte leads Trafford Innovation Lab which has been developing new and innovative ways to make open data understandable. The insight created has enabled community groups to use data to help them apply for funding, created resources for councillors and shown a spotlight onto the complex world of school admissions

Open Data Manchester events are spaces for learning, discussion and collaboration. The events are open and free

Open Data Cooperation – Building a data cooperative

Last year Open Data Manchester held two workshops one in Berlin and the other in Manchester to explore whether cooperative structures could enable the creation of open data and personal data stores for mutual benefit. The idea of the mutual came out of an ongoing conversation between people within the cooperative movement and the open data world about the role of cooperatives, and the possibility that they could rebalance what many perceive as asymmetric relationship between data subjects (people with personal data) and data users (people who use data to develop services and products).

Report is downloadable here

Slide presentation here

Background

Our modern technologised societies exist on data. Mostly these data are invisible and unknown to us. The services that we interact with, the daily transactions that we make and the way we negotiate through our everyday generate data, building a picture of who we are and what we do.  In the age of the Quantified Self there is a growing trend for self monitoring allowing us to track what we do and how feel when we do it. These data are valuable. Aggregated they enable organisations to predict, personalise and intervene seamlessly and sometimes invisibly. Even for the most technically literate, keeping track of what we do and don’t give away is daunting. Personal Information Management Services (PIMS) are starting to emerge offering people the chance to stem the unbridled exploitation of personal data by both public and private organisations whilst also creating monetary rewards for their users. Many of these commercial organisations seek to act as a brokerage service for personal data. The creation of data cooperatives that can act as PIMS have the potential to empower individuals to have more control over their data, creating value for themselves and their communities, and for people to have more of a say in the services that are built.

The sensational revelations by Edward Snowdon shone a spotlight on the personal data that is collected through the IT software and hardware infrastructure that we rely on today. Although highlighting that we unintentionally give away a lot, it perhaps hasn’t built  a wider popular discussion around protection and usage of personal data. It is inevitable that as the awareness about the data that we produce rises there will be a demand for services that give people more control. PIMS offer to deliver monetary value to users, but how much value is up for debate as there are differing methodologies to quantify it [OECD 2013]. Value can also be context dependant – data about someone exhibiting behaviours that might indicate a large purchase might be deemed more valuable by companies that manufacture or sell that item.

Data cooperatives are starting to emerge that have a broader social and ethical outlook than simple monetary transaction. The Good Data which allows people to control data flow at a browser level with benefits going to social causes and the Swiss-based Health Bank where personal health data is aggregated for the advancement of medicine, are examples of this. As the principles of data custodianship for social good become understood there becomes an opportunity for more to emerge.

Data cooperatives can represent the interests of data subjects and data users

Cooperatives come in many flavours, traditionally coming out of the needs of the membership who subscribe to them. Structures of these cooperatives have generally been organised around a single class of member – workers, producers, consumers, etc. The single class structure although creating an equitable environment for members, can tend towards self interest and even though they may be bound by the notion of common good, the mechanism for the creation of the common good or commons is seldom explicit.

Internationally the creation of new forms of cooperatives that explicitly express the development of common good, across multiple classes of stakeholders are more abundant. Social co-ops in Italy and Solidarity coops in Canada often provide services such as health and social care, education as well as community infrastructure projects.

The ability to have multiple classes of stakeholders within a data cooperative has the potential to create a more equitable environment for both data users and data subjects to exchange data. The influence of different classes within the organisations could be managed by fair distribution of voting rights with a user such as a research organisation having the same voting rights as a data subject.

Michel Bauwens founder of the P2P Foundation talks about the creation of these new forms of cooperatives, and how they can build a commons both material and immaterial. This commons would be subscribed to by other commons creating entities and licenced to non-commons creating organisations. This suggests a federated relationship between such organisations where commons is shared, could exist. But the challenge would be how to define the exchange within this system and if a cooperative contained both producers and users how does this affect the production of commons?

Would a data cooperative necessarily adopt these newer forms of distributed and commons creating structure? There appears to be a consensus that commons creating, multi-stakeholders cooperatives are positive, but they come with increased complexity. Can individual circumstances especially when dealing with communities based around sensitive issues, create an environment for sharing beyond a single class of stakeholder? A single class cooperative may seem to be a simpler, immediate solution for a community of people who have specific needs and issues and where strong trust relationships need to be maintained.

The scale of data cooperatives

Data cooperatives have the potential to work at scale generating and trading in bulk, high worth data as well as forming around smaller communities of interest, such as around a particular health issue, to draw down or negotiate for a better services.

Creating a critical mass of data subjects that would allow the data cooperative to operate at scale would be challenging. Marcos Menendez from The Good Data sees that for PIMS such as themselves would need to create a minimum data subject base of around 500,000 people to be viable. There is potential for data cooperatives to partner with organisations or charities with a similar ethical outlook to build the data subject base.

It may be easier to form cooperatives around single issues as the community the cooperative seeks to represent will already be in existence. The value of such an organisation might be that it can help create a more informed decision making process with views of the data subject being represented. Within a multi-stakeholder model the service provider might also be part of the data cooperative such as a local authority or other public sector organisation

Making the purpose of the data cooperative understandable is key. Although single issue cooperatives are relatively simple to understand, the representation of data at scale may be challenging. Data cooperatives could act as a platform that builds consent and allowing the representation of personal data across a broader portfolio of interests.

Building trust and consent within the data cooperative

Trust and consent should be the foundations on which PIMS are built and data cooperatives have the potential to create both. Mutuality offers an opportunity – especially with a multi stakeholder model – to represent the interests of all stakeholders from individual data subjects to data users – creating an environment of mutual understanding and trust. The benefits of enhanced trust between the individual data subjects and data users could enable better data and context to be created by data subjects. Through understanding the ways that the data is being used and trusting that the data user understands the needs and concerns of the data subjects, could create a more enlightened and responsive relationship. Even without data users being part of the organisation, the data cooperative would be able to take on the role of trusted representative which in turn could create consent.

Informed consent across all data subjects in a cooperative could be challenging. It would be easy for a data organisation to empower those that already have knowledge and agency to maximise their data, but the data cooperative should have an interest in empowering everyone.

Increasing data literacy amongst members

Raising the level of data awareness amongst cooperative members would create more informed decision making, but this task would need to be delivered in a sympathetic and nuanced way. Ultimately some people may not engage because of service dependency, lack of choice. or a perception that it isn’t relevant or useful to engage.

For a data cooperative to represent its membership and control the flow of data it needs to have legitimacy, know and understand the data assets of the membership, and have the authority to negotiate with those data assets on the members behalf.

Decisions around data sharing and understanding the potential consequences are difficult and complex. As an intermediary the cooperative would need to ensure that individual members were able to give informed consent. Data literacy goes some way to achieving this but also mechanisms need to be created that can allow people to have agency over the way that their data is used.

Creating consent

Can one organisation be representative of the broader range of ethical positions held within a membership structure? For practical reasons the data cooperative might have a high level ethical policy but individuals within the cooperative may make data sharing choices based on their personal ethical standpoint. This could be enabled by proxy or preset data sharing preferences. The alternative could be to have smaller federated or distributed niche organisations that have specific restrictions on data reuse.

There exist many mechanisms for the creation of consent. These by and large create the environment for proxy voting in decision making processes. A mechanism such as Liquid Feedback – popularised by the Pirate Party, where an individual bestows voting rights to a proxy who aligns to their position, with the ‘liquid’ element allowing proxy rights to be revoked at any point. Other mechanisms might follow along the lines of the Platform Preferences initiative developed by W3C, which sought to create privacy policies that could be understood by browsers – ultimately considered too difficult to implement. A potentially easier solution might work on the basis of preset preferences based on trusted individuals or the creation of archetype or persona based preferences that people can select.

Creating a more equitable data relationship

How would the argument for greater individual data rights be made when service providers see that personal data mediated through their products as their intellectual property? Work has been done through the midata initiative and the developments of personal data passports – where individuals grant rights to organisations to use the data for delivery of service. UK Government has supported this initiative, but has backed away from underpinning the programme with changes in legislation. The lack of regulatory enforcement may limit the efficacy of any initiative that seeks to grant individuals’ rights and agency over their data.

At present there is a certain level of cynicism around voluntary codes of practice where power imbalances exist between stakeholders. The lack of legislation might also create a chilling effect on the ability of data cooperatives to gain the trust of their membership due to their inability to totally control the flow of data.

Existing UK data legislation does give data subjects rights to access personal data held by external organisations through Subject Access Requests. A data cooperative could act as a proxy for individual members automating regular Subject Access Requests. This model is being explored by Our Data Mutual in Leeds, UK. There are challenges with using Subject Access Requests at present. Organisations can charge up to £10 for each request and although provision of the data in digital format may be specified, responses usually take the form of reams of paper print outs with responses taking up to 40 days.

It has been mooted by the UK Government that the cost of Subject Access Requests will be reduced – potentially to zero and that organisations will be compelled to supply the data in digital format. This would go a long way to making the process of automated Subject Access Requests viable but in an ideal world data should be pushed rather than pulled.

Data supply

A challenge that all data cooperatives would face would be how they maintain a relationship with their membership so that services based upon, or value that is extracted from the data is not subject to unforeseen supply-side problems. If a data cooperative represented its membership and entered into licensing relationships with data users on behalf of its membership, what would be reasonable for a data user to expect, especially if data subjects had the right to revoke access to data at anytime? With larger scale data cooperatives this may not be too much of a problem as scale has the potential to damp down unforeseen effects. The Good Data proposes to get around these issues by only holding data for a limited amount of time essentially, minimising disruptions in data supply by creating a buffer. It may be necessary for the data cooperative to create terms and conditions for data subjects to minimise sudden supply-side issues.

Smaller data cooperatives, especially ones that are created around single issues may have difficulty in engaging in activity that requires service guarantees. Developing a mechanism for federation, cumulatively creating data at scale might be a potential solution, but creating a federated system of consent may be more difficult to achieve. As suggested previously economic activity might be a low priority for such organisations where the main purpose might be to represent members and create the environment for informed service provision.

The challenge facing federated data cooperatives and how they interact is undefined. It has been noted that building distributed and federated systems is difficult, and that centralised systems persist due to operational efficiencies. The advent of alternative forms of ‘block chain’ transaction could enable distributed organisations to coexist using ‘rules based’ or algorithmic democracy. But alternative transaction systems and currencies often face challenges when they interface with dominant and established forms of currency and value. How data cooperatives could practically use these new mechanisms for exchange needs to be explored.

The data cooperative and open data

Although the much of the discussion in the Berlin and Manchester meetings was based on rights and uses of personal data, data cooperatives do offer an interesting model for organisations that create open data, or those that seek to enhance open data with personal data.

An open data cooperative might be a good model for stakeholders who create and use data for public access. It may be a single class model where data suppliers such as public bodies work together or more interestingly a multi-stakeholder model where public data providers work with organisations that manage personal data – these in themselves could be data cooperatives

In summary data cooperatives;

  1. are owned by their membership and therefore should be more accountable;
  2. have the potential put a halt to the over collection of personal data through representing data subjects and advocating on their behalf;
  3. can create value for their membership;
  4. can form around single issues or scale with many data subjects;
  5. can become representative and be used to create change;
  6. could help their membership to understand how data is used – data literacy;
  7. can liberate personal data on members behalf through Subject Access Requests;
  8. can encourage better data and context to be produced by data subjects;
  9. build trust and consent within the organisation and
  10. can be a blend of open data and personal data organisations

Open Data Manchester – November 2014

6.30pm – 8.30pm Wednesday 26th November 2014
Greenheys Business Centre
Manchester Science Park
Pencroft Way
Manchester M15 6JJ

Map here

Sign up on Eventbrite here

There is a more general theme to this month’s Open Data Manchester although a lot to cover.

Open Data Manchster will have been going for 5 years in April and as a voluntary, unincorporated group that doesn’t even have a bank account, it hasn’t done too bad. Does ODM need to become more formalised? How can we become more representative of the membership? And what do we need to do to be relevant for the coming years? Turn up and have a part in the future of ODM

We will be feeding back on the Open Data Cooperation work that we’ve been involved with over the past few months and will be a chance to add your thoughts

Like always it will be a chance to share ideas, discuss projects and find out what’s happening with open data in Manchester and further afield.

Open : Data : Cooperation – 20th October 2014

Open Data Manchester Special – Open : Data : Cooperation – Building a data cooperative
20th October 2014, 1pm – 5.30pm

The Shed,
MMU John Dalton Building
Chester Street
Manchester
M1 5GD – Detailed directions here

An afternoon of scene setting and workshops creating a framework for building data cooperatives. This event is for anyone working in this area and is a must for people who are interested in new forms of cooperative structure, open data, data custodianship and data rights.

The event follows on from previous sessions run in Manchester and Berlin. It is open and free to participate.

Why create a data cooperative?

Our modern, technologised society exists on data. Our everyday interactions leave a trace that is often invisible and unknown to us. The services that we interact with, the daily transactions that we make and the way we negotiate through our everyday generate data, building a picture of who we are and what we do. This data also enables aggregators to predict, personalise and intervene seamlessly and sometimes invisibly. Even for the most technically literate, keeping track of what we do and don’t give away is daunting. There is a need to stem the unbridled exploitation of personal data by both public and private organisations, to empower individuals to have more control over the data they create, and for people to have more of a say in the services that are built upon and informed by this data. Data cooperatives may help rebalance the relationship between those that create data and those that seek to exploit it whilst also creating the environment for fair and consensual exchange.

A synopsis of the discussion that took in Berlin can be found here

Sign up on the Eventbrite page here

An agenda will be circulated nearer the event date

This event is sponsored by Cooperative News

Open Data Manchester – July Edition

6.30pm – 8.30pm Tuesday 29th July 2014
Greenheys Business Centre
Manchester Science Park
Pencroft Way
Manchester M15 6JJ

Map here

Sign up on Eventbrite here

This month’s Open Data Manchester is a chance to see some of the open data initiatives being led by Salford and Trafford Councils, and hear about some of the highlights of the Open Knowledge Festival taking place in Berlin 14-17th July

Amongst the data initiatives taking place, Trafford are looking to develop an Intelligence and Innovation Lab, which will take the principles by which InfoTrafford was developed, and use them to bring together a greater range of datasets from Trafford’s organisations. Accompanying these datasets will be the people from the respective organisations who understand the data – where it comes from, how to get it, and, crucially, what stories the data tells. This means that the right people will be sitting and working together – using their collective insight and knowledge to give greater understanding of the needs and opportunities in Trafford. The Lab will be focussed on the release of data as 5* linked data. Trafford is looking to create an environment where digital social innovation methods can be used to help people get things done – from giving practitioners a space to test innovative ideas that may help shape services, to allowing developers the opportunity to see and request datasets, and test apps with a potential user base. Jamie Whyte from Trafford will talk about the Intelligence and Innovation Lab and how you can get involved.

John Gibbons from Salford City Council will talk about the work they are doing relating to the European Commission INSPIRE regulations. INSPIRE seeks to create a common and shared geospatial infrastructure to allow strategic and sustainable development. It is a little understood initiative outside the GIS community, but as it is underpinned by European Commission legislation to which the UK has signed up to, it has the potential to have a large impact on the release of geospatial data.

As always, there will be opportunity to discuss and share ideas, and hear about the latest opportunities in open data.