It can be accessed through the Europa server (http://europa. eu). Cataloguing data can be found at the end of the publication.
and Co-Creation Of value 95 Oulu Innovation Alliance an Open Innovation Ecosystem 105 Smart Fabric to Big data:
@umich. edu Rannou Herve Cityzen Data & ITEMS International herve. rannou@items. fr Rantakokko Mika University of Oulu, Center for Internet Excellence, Oulu
Big data, Youth Innovation, Smart Cities and two very special, but interesting, topics on Lawyers in Innovation as well as Drivers for Creativity Based on Humor!
Carrol interlinks existing youth unemployment solutions with modern approach of using data (and especially big data) as driver for future growth.
Open data and open platforms create a strong raw material basis for new enterprises and young people to create their own jobs.
Quite an interesting example on wearables and citizen-generated data, including data management which provides additional value for the community,
is discussed. Important boost for this sector to grow is the open plug-in platform for devices both from hardware and system level.
How do we transform our organisations from data-to design-driven innovation? Or do need we to transform along other axis?
We believe that the intersection of mega-trends such as digitisation, mass collaboration, and sustainability needs is creating a unique opportunity to enable an explosive increase in shared value due to innovation.
OI2 is enabled by the collision of three mega trends digitisation, mass collaboration, and sustainability. Across the world, Moore's law is colliding with virtually every domain.
and data mining that allow identifying patterns of behaviour and usages. see the latest version of the three description tables presenting the experience types,
It is targeted not only to evaluate the UX with collected data during the experimentation but also to anticipate it during the co-creation
The application use has been monitored through various types of data logs, making it possible to collect data on frequency of attendance and quality of usage.
The activity in the Media scenario experimentation phase consisted of the investigation of the outcomes gained from the co-creation stage and the subsequent development into prototypes and mock-ups for validation.
the issues and considerations emerging from the experimentation stage were discussed regarding the way data was collected,
Personalised Service and Public transport Scenarios were set up in a temporary store where data were collected and analysed together.
help user to become more aware regarding nutritional behaviours (i e. showing nutrient data and healthy diets;
UX model analyses have been carried out based on data acquired from vending machine (such as products or bounce rate.
offering various green services such as the visualization of environmental data collected by citizen, the alert services via mail or SMS, the ability to download data, the gamified forum for sharing ideas and best practices
Each participant can access to more details of his sensed data. In addition to the equipped city car, two types of citizen air sensors were provided during the two 16-days experiments:
which runs local Linksmart middleware and the Nosql system database. Figure 9: The Environmental Services Use Case The Healthcare Services Use Case The Cardiovascular diseases (CVDS) are globally number one among those causing death:
the patient data were Figure 8: The Retail Services Use Case 31 collected in it and Medical Doctor had access to them after certain amount of time.
methods and techniques for collecting necessary data and for analysing these data in their specific context.
While the degree of coverage of the holistic model appears quite complete and comprehensible, its complexity in terms of structure and simplicity to instantiate was rated less positively.
The good point is the coverage of the model that was rated as being high. However
and related data that they have to collect and analyse for the UX evaluation. Several experiments demonstrated that the iterative nature of the Experiential Design process
and collection/analysis of data whether it is anticipated about, momentary, episodic or even cumulative use.
Who will own that data and who can you trust? On the US market, there is a great bias in National ID card,
the extension of many big data projects to get more out of the datasets governed by financials.
Building an increasingly rich data set with new sensors and measurements will provide enhanced intelligence, customer insights and accuracy.
The user base will create more meaningful data and provide rich community sharing that will only further bolster user loyalty and trust.
and finalised with the representatives of all FI-PPP projects (Figure 2). The collaboration model emphasised transparency and access to data for all parties.
and data records (3). In addition, government policies did not introduce distortions in specific economic sectors;
In fact there are plenty of data-driven companies, companies whose raw material is information and whose boundaries are no longer geographic.
Data as Raw material Innovation models and paradigms exist to help in the process of reinventing. One of these models is the‘combination innovation model'consisting of the mixture of elements already existing in a way that had not previously been done before.
Can we imagine a Europe that is leading in the analysis of the data? We can prepare our students to be the leaders in extracting advantage of data analysis Europe is a knowledge-intensive society
but mostly is a data-intensive society. The data begin to be seen as a commodity very capable of generating wealth
and under the new‘Big data'phenomenon lays an opportunity to create value and benefits for society, business and citizens.
According to an IDC study only 1%of the world's data are analysed (6), while organisations are increasingly dependent on them (7)
and experience indicates that when business decisions are based on analysis of data they are smarter,
more precisely targeted and therefore can be translated into economic benefit. However, the main drawback for data analysis at this time is the lack of trained people.
the data and the academic strength to provide analytical skills to fill the gap between offer
data from cell phones are particularly interesting because they are the only way people with fewer resources interact with technology.
Analysing this data can help us to understand behaviour patterns of the excluded sectors of the population,
Otherwise the particularities are buried within the global population data. The health sector continually strives to reconcile cost reduction to sustainable terms
while must meet a growing demand for an aging society with great expectations in the care of older people is a good example of how can be based on analysis of these data to better understand patterns in the field of health and stop bad
if US healthcare were to use big data creatively and effectively to drive efficiency and quality,
government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data,
not including using big data to reduce fraud and errors and boost the collection of tax revenue.
Leading and Managing Patient theirhealthcare through EHEALTH (11) which is a compound of 7 demonstration pilots based on the concept of secure and user friendly online access by citizens to their medical/health data.
From the analysis of this data it will be possible to extract useful patterns of behaviour.
the job for data scientists. First we should define what a data scientist is. One of the most complete definitions is from Jeffery Stanton,
Syracuse university (12) who refers to the Science of Data as an‘emerging area of work related to the collection, preparation, analysis, visualisation, management and preservation of large amounts of information'.
'This definition gives a rough idea of the variety of knowledge that includes this new discipline:
Computer skills as query languages, database design, mining and interactive data analysis, scripting or programming languages, expert systems and machine learning, etc.
One of the main problems in this area is how to translate the sea of data to information to the decision.
The data scientist is a specialist in handling the information and his purpose is to exploit the data to extract information.
The intensive exploration of bulk data has become a key to competitiveness and growth in Europe.
It is required to place the workforce in an advantageous starting point providing them with the necessary analytical skills.
Big data, Bigger Digital Shadows, and Biggest Growth in the Far east; IDC; December 2012; Available from:
http://www. emc. com/collateral/analyst-reports/idc-the-digitaluniverse-in-2020. pdf (8) Big data Big Impact:
http://www. weforum. org/reports/big data-bigimpact-new-possibilities-international-development http://www3. weforum. org/docs/WEF TC MFS Bigdatabigimpact briefing 2012. pdf (9
) Talbot D. Big data from Cheap Phones (Internet. 2013; Available from: http://www. technologyreview. com/lists/breakthrough-technologies/2013/10) Manyika J, Chui M.,Brown B.,Bughin J.,Dobbs R.,Roxburgh C
.,Hung Byers A. Big data: The next frontier for innovation, competition, and productivity. Mckinsey Global Institute; 2011.
making it already the largest and most advanced biometric database in the world. At a fundamental infrastructure level, UIDAI Aadhaar illustrates how the public sector can potentially enable
Enterprises that become part of the UID applications ecosystem get an authentication service via UIDAI confirming almost immediately the identity of any individual through an advanced technology infrastructure that checks incoming UIDS and biometric information against its database.
in some states, existing databases of the Public Distribution System, the Indian food security system, exist only in the form of offline document files.
These files must be converted into an online database before they can be linked to Aadhaar. There is resistance to this change from some quarters.
Airports, like cities, also require a common open operating system that allows for the sharing of data between artifacts
and presenting that data as information in the right way and on the right devices to benefit
City of Oulu has opened also its databases in the open innovation spirit to be used for example for product development purposes (12.
/en/info (11) http://www. panoulu. net/(12) http://www. ouka. fi/oulu/english/open-data (13) http://www. cnbc
to Big data: from One Innovation to Two Promising Businesses Introduction The Internet of things is now a reality.
On the technical side, Cityzen Data developed relationships with CEA LETI in Grenoble. A consortium was finally set up to apply for public funds from OSEO (which is now BPI France) with the name of Smart Sensing.
The Critical value of Data Management Cityzen Sciences understood early on that the value of technology and services would come from data analytics such as:
How to combine data from different sources? How to consider the historical profile of each user?
Finally, Jean-luc Errant and myself decided to launch a company dedicated to addressing these key issues focused on the Smart Fabric market with Cityzen Sciences,
We were fortunate to meet Mathias Herberts who has a solid background in data management and analytics.
'Time series are going to become the new key paradigm for data originating from sensors. Traditional databases are adapted not to this market in spite of claims to the contrary.
Cityzen Data has developed a very innovative solution for managing data and geolocation in the same series:
a set of advanced functions and a language to clean, manipulate and analyse data; to detect patterns or weak signals. visualisation tools, a library of APIS.
Today Cityzen Data is in negotiation with several major groups including some that already have a platform to manage Data.
Cityzen Data does not address vertical markets. It just provides an advanced technology to manage data to any players that want to use data analytics across all business sectors.
Towards European Partnership Cityzen Sciences has established high level partnerships with major groups in Europe who consider that the technology developed by Cityzen data is advanced more than other tech providers.
Some initiatives should be announced in the next few months in Europe and China. Finally with the help of BPI France, we have succeeded in launching two promising companies by setting up one innovative project.
We have now to prove that customers will make them a reality. This is a challenge
which Cityzen Sciences and Cityzen Data are excited to face. Horizon 2020 is now coming with its priorities and its series of calls.
It could give us new opportunities to develop new innovations that could strengthen our position on the market.
For Cityzen Data, the point is to balance our time with our trust on the evaluation process.
Our geo time series technology does not address the final usage of Big data but the way we manage the data itself.
It is not necessarily the most sexy even we consider that the key factor of success after discussion with potential clients.
2) versatile sensors and actuators integrated in electronic textiles 3) platform, architecture, big data analysis and visualisation solutions for novel sport and health solutions,
Contact Sébastien Lévy Partner Items international slevy@items-int. eu Herve Rannou CEO, Cityzen Data CEO, ITEMS International.
Cityzen Sciences and Cityzen Data strategic consulting partner. herve. rannou@items. fr 116 O P E N I N N O V
such as Big & Open Data, Smart Cities, Space enabled Services and Digital Social Innovation. Background, Concept and Objectives EYIF's Openeyif leverages Open Innovation processes
such as Big & Open Data, Smart Cities, Space enabled Services and Digital Social Innovation. The best ideas and early-stage projects will be awarded seed grants (below EUR100 000) and crucially,
such as Big & Open Data, Smart Cities, Space enabled Services and Digital Social Innovation using a three-stage integrated framework approach based on Open Innovation mechanisms targeting new constituencies of young innovators i e. between 18 and 30 years,
i e. the Young Innovators Community, Tech and Open Data Communities, the Open Innovation, Open source and ICT Infrastructure Community and the Start-up Ecosystems in different networks around Europe to submit their innovative ideas within the subsequent Open Calls.
such as Big & Open Data, Smart Cities, Space enabled Services and Digital Social Innovation. The Openeyif foresees a cross-border,
With the growing availability of all kinds of data on the one hand and flexible lighting systems (with sensors and controls) on the other there are many opportunities for new business with services in lighting.
and communicate all kinds of data. Devices The number of devices is growing rapidly. Traditionally, devices in public lighting contain public lighting luminaires and traffic lights.
or use data and are connected therefore (the Internet of things). ICT On the ICT level the connection is made with data and software applications.
The data that is collected through different devices contains e g. time, people counting or proximity measurements, weather information, movements, energy consumption, camera data, etc.
Mashups and data analytics will lead to insight in emerging patterns or correlations that can be used for various software applications.
Services At this level meaningful services are developed that provide value for the relevant stakeholders. In urban lighting there are often different stakeholders that use the area, with different needs and wishes.
1. Open platforms, open data and open knowledge make new connections possible. By linking data
and integrating various perspectives new solutions for societal needs emerge. Data analytics becomes an important element to identify emerging patterns and spot new opportunities.
It also enables to determine the impact of solutions. The technical challenge lies in the selection of the required devices to efficiently
and effectively collect data and integrate all data into a total system. 2. Innovation driven by societal needs requires the active involvement of all stakeholders to find solutions that cater for their different needs.
In the living lab a‘base camp'has been opened recently where data from various sources is collected
The data that is collected contains a number of real time measurements such as: 3d sound measurements to identify noise levels and the direction of the noise,
Other data is collected with a delay, such as: police reports on incidents, determination of origin and counting of mobile devices to establish where groups of people come from,
litres of beverages consumed by collecting data from the breweries or amount of waste thrown in the street measured by the cleaning service.
Correlating the data on the incidents to specific parameters is done to predict when there is a higher risk for escalation.
Historical data from past incidents is used now to find such correlations. Based on the determined risk level, lighting scenarios are activated.
The analysis of data of different nature and combining patterns to create new insights is a key element in this case.
This requires new skills for data scientists. With these insights lighting scenarios can be designed and tested on their impact on the mood and behaviour of people.
the data scientist and the dynamic lighting service designer. Data Scientists Data scientists know how to gather data with the Internet of things.
They know what combination of 124 O P E N I N N O V A t I O N y E A r B o O k 2 0 1 4 sensors
and data gathering is required to obtain relevant data and how to register the data. They also know to apply the various models,
theories and tools to add and extract value from sets of the gathered heterogeneous data.
They turn data into information. What is also relevant in the context of smart urban lighting is to use this information to understand
and influence human behaviour. The data scientists bridge the technical competences and the social sciences. Dynamic lighting service designers These designers need to be able to empathise with the different stakeholders.
In comparison with traditional designers, who focus mainly on users, they need to extend their scope and research the needs of a wider range of stakeholders.
The data scientist might find new emerging patterns that spark the development of new applications.
Data or Design Innovation has always been important for organisations, but nowadays it is crucial for maintaining a competitive advantage in many markets;
Data is one of them, design another. Typically, data is where Google stands for. Numerical analysis of what works best.
Apple is the other side of the virtual spectrum. Intuition, designing and molding the wishes of the customer.
being driven data or design-driven. Data-driven Innovation How do organisations come up with new ideas?
Most of the time fresh ideas occur from happy accidents or by using techniques such as brainstorming.
If you are part of the big data movement, you would say that brainstorming is unreliable. With data-driven innovation, innovators generate ideas by exploiting existing
or new data sources and analytics to develop novel insights, particularly by answering queries. More data is generated today than ever. 90%of the data in the world today was created in the last two years alone.
Several researchers call data‘the innovation story of our time'as analysing large sets of information
and cutting-edge experimentation will become a key driver of competition underpinning new waves of productivity growth and data-driven innovation.
Probably the biggest difference between enterprises that are native to data and others is how they approach strategy.
Non data-driven companies tend to undertake research in order to gain a deep understanding of the marketplace.
Then strategy consultants spend months interpreting the data, decide what it means and suggest a course of action.
Data driven firms like Facebook, Amazon and Google, on the other hand, take the hacker way. They run experiments thousands upon thousands of them.
From colours used on a button to different websites to see which site will increase sales, all in real life and with real customers.
Based upon quantified results the experiments determine what the strategy will be. Design-driven Innovation On the other side of the spectrum, you can find design-driven innovation.
Where data-driven focuses on facts, design relies more on intuition and interpretation. Design has become a decisive advantage in countless industries,
Data-driven and Design-driven are both great in many innovation strategies. When designers lack influence,
If our data is currency, who's the bank? It's a question that every innovator should be thought giving serious to.
Those who don't may soon find themselves on the outside looking in at a data-centric economy that has moved on without them.
Our data is hot property and everyone wants a piece of it. For consumers, it begins to feel like around every corner there's yet another company, service,
or app. that takes our data for their use. Consumers start to question the real,
other than being perceived as entangled in the big data game. Thanks to the crisis and existing management techniques
and advocacy require a larger palette of insight than design or data alone. So how to overcome these challenges?
We would like to introduce an adjacent territory to‘fix'the flaws of choosing a data-driven or designdriven innovation process.
designers and data scientists are people before being designers or data scientists. Culture Defines Us We are influenced all by the social and economic context where we live in.
Products are easily scalable thanks to the culturally neutral data-driven and design approach. Scalability used to be a plus, a scarcity only possible for the big companies.
the creation of adequate collaboration structures, consulting services and targeted marketing support. One recent study focused on the application of social technologies of the Web 2. 0 as tools to open up the innovation process of SMES (Piva et al.
As financial data is rarely available for SMES, we relied on the number of employees
and the collected data broadly categorized according to our three research questions. Within the first round of content analysis
and the coders reworked the data searching for significant quotations, which are presented below. Another goal of the third step was to filter out the most essential and topic-related findings among a variety of interesting ideas.
and try to comprehend the data as well as the most important findings. Next, the results are presented with meaningful statements from the interviews. 3. Results 3. 1. Current sources of innovation in SMES.
To convert these minimal pressure differences into a convenient tool for recording weather data the metal cells were brought into contact with a liquid that reacts to these small differences accurately
awards, lectures at conferences, press coverage, and other inexpensive means. 54 4 How SMES build new business models through open innovation?
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
Clusteranalysisrevealedthreegroupsofsmes, clustering firms intogroupswithsimilaropeninnovationpractices. Theirfeaturesconfirm Lichtenthaler's (2008) conclusionthat companies seldomfocusoneithertechnologyexploitationor technologyexploration. Rather, openinnovatingcompanies tend tocombinethesetwoaspectsofopeninnovation.
including cultural and organizational problems as the most important items. 4. DATA AND METHODS 4. 1 Survey description To analyze trends,
The population of firms was derived from a database of the Chambers of Commerce, containing data on all Dutch firms.
The data were collected in December 2005 over a period of three weeks, by means of computer assisted telephone interviewing (CATI.
All respondents were small business owners or managers and innovation decision-makers. Attempts to contact reference persons were made five times before considering persons as non-respondents.
The survey data contained a summary variable indicating customer involvement i e. a dummy coded 1
The survey data allowed distinguishing between employees that belong to the R&d department and those that are coming from other organizational parts of the company.
and they experience stronger growth in adapting open innovation practices than their smaller counterparts 5. 3 Clusters To explore patterns of open innovation among SMES we relied on cluster analysis techniques.
since the addition of irrelevant variables can have a serious effect on the results of the 25 clustering (Milligan and Cooper, 1987).
Next, we applied cluster analysis techniques to explore patterns of open innovation practices among SMES. Finally, we used oneway analysis of variance to validate the taxonomy.
as a way to reduce the number of dimensions to be used in the clustering. In general
and therefore no variable would implicitly be weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al.,
We first tested if our data were suitable for a component analysis, by calculating Measures of Sampling Adequacy (MSA) for the individual variables (Hair et al.
In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and Sokol, 1980;
For each number of clusters (k), we perform a k-means‘nonhierarchical'cluster analysis, in which SMES were divided iteratively into clusters based on their distance to some initial starting points of dimension k
the data did not contain enough records to provide reliable insights about respondents'motives and challenges on this topic.
The results of the cluster analysis furthermore show that there are different open innovation strategies and practices among SMES.
Cluster analysis. Oxford university Press, London. Fontana. R.,Geuna, A.,Matt M.,2006. Factors affecting university industry R&d projects:
clustering methods. Applied Psychological Measurement 11,329-354. Milligan, G. W.,Sokol, L. M.,1980. A two-stage clustering algorithm with robust recovery characteristics.
Cluster analysis in marketing research: review and suggestions for application, Journal of Marketing Research, 20: 134-148.
Evidence from Global Entrepreneurship Monitor Data H200809 25-7-2008 The Entrepreneurial Adjustment Process in Disequilibrium:
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
Clusteranalysisrevealedthreegroupsofsmes, clustering firms intogroupswithsimilaropeninnovationpractices. Theirfeaturesconfirm Lichtenthaler's (2008) conclusionthat companies seldomfocusoneithertechnologyexploitationor technologyexploration. Rather, openinnovatingcompanies tend tocombinethesetwoaspectsofopeninnovation.
To convert these minimal pressure differences into a convenient tool for recording weather data the metal cells were brought into contact with a liquid that reacts to these small differences accurately
awards, lectures at conferences, press coverage, and other inexpensive means. 54 4 How SMES build new business models through open innovation?
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