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Similar a Lecture week 5 -(20)

Lecture week 5 -

  1. Open Data-Driven Innovation and Smart Cities Fatemeh Ahmadi-Zeleti Insight Centre for Data Analytics National University of Ireland, Galway (NUIG) @fatemehahmadi_
  2. Open Data Data that are freely available to everyone to use and republish as they wish without restrictions from copyright, patents or other form of control mechanisms Share public data for transparency, participation, and stimulate new services based on the data e.g. Public Sector Information (PSI) Open Science Data Open Data gained popularity with launch of Open Data Initiatives such as and
  3. Open Data Cont. [Open Data is] going to help launch more businesses… It’s going to help more entrepreneurs come up with products and services that we haven’t even imagine yet.
  4. Open Data Cont. The World Bank’s Open Data Initiative, which was launched in April 2010, provides free, open, and easy access to development data, and challenges the global community to use the data to create new solutions to eradicate poverty. Today, the World Bank’s Open Data Catalog includes over 8,000 development indicators, of which 1,400 for 252 countries and 36 aggregate groupings, going back over 50 years, in 50 languages, and is continuously expanding
  5. Open Data-Driven Innovation Data can enable any kind of innovation. Data-driven innovation can be a sustainable source of economic growth but capturing its full potential will require a concentrated effort from governments, businesses and individuals Open Data-Driven Planning Data can be used to make robust decisions on the basis of facts, trends and patterns rather than the more variable tools of management expertise or ‘gut feel’. e.g. Queensland Health Data-Driven Goods and Services Data can be used to help businesses create new products and services that respond to customer needs faster than ever before e.g. SocietyOne Open Data-Driven Marketing Businesses can radically improve cost efficiencies and market agility through the data they capture about their processes and products. e.g. Amazon Open Data-Driven Operations Data can be used by businesses to identify new customers, or increase satisfaction and spend. e.g. Tip Top Bakeries
  6. Open Data-Driven Innovation Data driven innovation... is the value from using any kind of data to innovate Data itself is not inherently valuable. Value is created by working more intelligently with it to innovate, invent, change business processes, and enhance decision-making Data-driven innovation can differ from industry to industry in terms of the rates of innovation and types of innovation. Some industries are characterised by step- change innovations and others by smaller, incremental improvements.
  7. Open Data Innovation Ecosystem: World Bank
  8. Innovation in City • Improve the way citizens live in a city • Cities are the most important innovation platform • Innovation, most of all, is driven by collaboration. So it takes more than just smart people, but diversity as well • Design + Technology = eco city, green city, sustainable city and etc.
  9. Innovation Dimension in Cities
  10. Waves of Open Data Innovation Approach in Cities Networks of Civic Innovation Offices Need- driven Programs Hack Events “Direct” engagement of residents, city managers, other stakeholders Freedom for bottom up innovation, techno-centric with “token”-level participation of city management and residents +t
  11. Wave 1 Exemplar – Dutch Open Hackathon Available datasets including airport shuttle bus events, job data, flight data, supermarket, order etc. http://www.dutchopen
  12. Wave 2 Exemplar – Summer of Smart in San Francisco Engage mayoral candidates in San Francisco (2011) on solutions by Hack Teams to pressing problems in areas including 1) Community Development, 2) Buildings. Transportation and Sustainability, 3) Public Health, Food and Nutrition Focus is on real needs and involvement of major stakeholders in solutions Source:
  13. Wave 3 Example : New Urban Mechanics Boston UtahPhilly A Network of civic innovation offices in Boston, Philadelphia and Utah. Each of the innovation offices serve as the in-house research and development group for the respective mayors. They build partnerships between internal agencies and outside entrepreneurs to pilot projects that address the needs of residents Px_TuF-Js
  14. Smart Cities Initiative Development Framework (SCID) SCID developed from the studies of smart city programs in 10 countries. Links Smart City initiatives to concrete city domains and associated stakeholdersA. Ojo, E. Curry, T. Janowski, Designing Next Generation Smart City initiatives, ECIS 2014, Isreal
  15. City Cases Chicago, Helsinki, Amsterdam, Barcelona and Manchester
  16. Chicago Economy: Data Science Chicago, Chicago Shool of Data Governance: Data Science Chicago, Chicago Shool of Data Health & wellbeing: Chicago Shool of Data Environment: Chicago Shool of Data Transportation & mobility: Chicago Shool of Data Education: Chicago Early Learning Portal, Chicago Shool of Data Tourism: Chicago Shool of Data
  17. Helsinki Economy: Smart Kalasatama, Helsinki Region Infoshare, Apps4Finland, Helsinki Loves Developers Governance: CitySDK Health & wellbeing: CitySDK Environment: CitySDK Transportation & mobility: CitySDK Education: CitySDK Tourism: CitySDK apers/7367c326.pdf
  18. Amsterdam Economy: Code4Europe Governance: Apps for Amsterdam Health & wellbeing: Apps for Amsterdam Environment: Apps for Amsterdam Transportation & mobility: Apps for Amsterdam Education: Apps for Amsterdam Tourism: Apps for Amsterdam
  19. Manchester Economy: Greater Manchester Data Synchronization Program (GMDSP), Greater Manchester Datastore, Transport for Greater Manchester Governance: GMDSP, Greater Manchester Datastore Environment: Transport for Greater Manchester Transportation & mobility: Transport for Greater Manchester
  20. Impact Domains Governance and Economic Domains standout …
  21. Impact Domains Domain Impact Patterns Economy Creation of marketplace for society relevant applications; Availability of data products and services based on city operational data and; Scaling up the adoption of open data innovations across city functions through tools provision. Education Availability of innovative digital services for the education domain. Energy Availability of innovative digital services for the education domain. Environment Greener environment. Governance Better information sharing; open innovation for co-created services; open engagement in policy and decision making; and interoperation within city-network. Tourism Co-created services based on available open data. Transportation Better City Park Management; and Shorter transit time for commuters.
  22. Governance Mechanisms Five governance mechanisms: 1) Collaboration – enabling collaboration between city and stakeholders 2) Participation – enabling participation of residents and developers 3) Communication – enable better policy outcomes through publication of relevant data 4) Data exchange – Enabling data sharing among city authorities and network of cities 5) Service and application integration – to provide software development tools (e.g. CitySDK) to build OD-based applications
  23. Data Ecosystem Specific datasets that are associated with major SCs domains – number of datasets include in the ff sectors: 1) Transport and Mobility – OpenStreetMapdata, CurrentCarParks… 2) Health and wellbeing – UKFoodHygiene, DrugTreatmentStatistics… 3) Environment and safety – FloodMap, EnergyUsage… 4) Education – CookCOunty, AdultEducation… 5) Tourism – Cultural and Leisure… More focus on Transport and mobility as well as Environment and safety datasets, which are both characterised as innovation cluster data.
  24. Stakeholders “Open Data Ecosystems in these cities have the active participation of residents, different city authorities, software developers, and SMEs in providing, curating and consuming the datasets … ” Participation of non-technical stakeholders are minimal – “token”
  25. Major Issues Two significant issues: 1) Cities-> “Open Innovation Economies” Emerging 2nd generation open data based smart city initiatives are redefining the respective cities as “Open Innovation Economies”. This is significantly different from the emphasis of first generation initiatives which are strongly linked to physical environment and infrastructure. 1) Need-driven open data initiatives in smart cities such as those described earlier are exceptions
  26. Conclusion 1) There are still huge potentials and gaps on how open data can impact smart cities aspects. In particular, need driven, stakeholder-led data driven innovation programs are still relatively few. 2) There are currently no rigourous model to fully analyse this opportunity gap. We are currently investigating such models. 3) Interviews and discussions with City Managers and Open data program officers in cities may explain and identifies barriers to need-driven approaches in open data projects in smart cities.
  27. Emerging Open Data Business Model Fatemeh Ahmadi-Zeleti Insight Centre for Data Analytics National University of Ireland, Galway (NUIG)
  28. Business Model A business model describes how value is created and captured by an organization through the decisions made and the resulting consequences A business model is a conceptual tool that contains a set of inter-related elements that allows a company to generate money It comprises a description of the value a company offers to one or several segments of customers, the architecture of the firm, and its network of partners for creating and delivering this value in order to generate profitable and sustainable revenue streams
  29. Business Model Cont. Shafer, Smith and Linder Business Model
  30. Business Model Cont. Hamel Business Model
  31. Business Model Cont. Kamoun Business Model Basic building blocks of a business model and the external forces that have an affect on these blocks
  32. Business Model Cont. Osterwalder and Pigneur Business Model
  33. Open Data Business Model (ODBM) • The demand for Open Data is increasing the idea for businesses to use Open Data to generate value and revenue • Utilizing Open Data can help companies improve the productivity of current business processes and can lead to new products, services • ODBM should be designed and developed accordingly so that businesses can generate value and revenue from utilizing Open Data
  34. 15 ODBMs Freemium: Offering is given for free Premium: Offering is high end products and services and customer willing to use the offer has to pay Dual Licensing: [open source + proprietary licenses] Offering is provided as open license for certain purposes and under a closed license for others Support and Services: Offer is provided in a full package with complete support and service of the business. E.g. Availability, bug fixing, etc. Charging for Changes: Charges applied for changes in the offer Increasing Quality through Participation: Increasing integration and participation of the customer is a new organizational choice aimed at generating higher margins Supporting Primary Business: Releasing an offer naturally supports the primary goal of a business or organization Demand-Oriented Platform: Charging for demand side of the offer [charging developers the added value such as advanced services and refined datasets or data flows provided upon the original raw open data]
  35. 15 ODBMs cont. Supply-Oriented Platform: Charging for the supply side of the offer [presence of an intermediary business actor having an infrastructural role] Open Source: The offer if provided in a complete open format [all source codes are open] Sponsorship: Offer is provided for free to customers and obtaining revenue from some sponsors Infrastructural Razor & Blades: Selling a product for a low price in order to generate revenues from the complementary products Cost avoidance: Reducing the cost of production [reduces the cost of data publishing by having a sustainable publishing solution. same data to be published a number of times and in different formats] Free, as Branded Advertising: Generate revenue from strong brand advertising [Business delivers commercial messages through visualized data which is also called “display advertising”] White-Label Development: Offer is developed by one business and is sold to other business with white-label
  36. ODBM Conceptual Model
  37. ODBM Main Components Value Proposition Offer Channel Value Knowledge Management Value Adding Process Strategic Operational Value in Return Volume of Sale Income Future Opportunity Value Network Actors Support Infrastructure Value Management GovernanceAdministrationStructure Discipline Value Capture Profit Model Market Size
  38. ODBM Components
  39. ODBM Patterns [15 to 5] • Freemium “Freemium”, “DualLicensing”, “Charging for Changes”, “Open Source”, and “Free as Branded Advertising” models [offer limited data free of charge and apply fees for additional request] • Premium “Sponsorship”, “Support and Services”, “Demand- Oriented Platform”, “Supply-Oriented Platform”, “White- Label Development” and “Premium” models [data is not offered free of charge]
  40. ODBM Patterns [15 to 5] cont. • Cost Saving “Increase Quality through Participation” and “Cost Avoidance” models [Models reduce cost of opening and releasing data] • Indirect Benefit “Supply Primary Business” model [Offer naturally supports the primary goal of the business] • Razor-Blade “Infrastructural Razor and Blades” model [Incomplete offer at a discount and complementary offer at a higher price]
  41. Open Data Business Value Disciplines • Usefulness: tailors value proposition of the business to meet usefulness of the business offer • Process Improvement: tailors value proposition to match to the needs of the customer for improving processes • Performance: tailors value proposition for a better performance • Customer Loyalty: tailors value proposition to target customer loyalty
  42. ODBM Patters and Value Disciplines
  43. Conclusion • All businesses MUST employ particular Business Model • Open Data businesses MUST design, develop and sustain particular (combination of) ODBM/s • Before identifying Business Model, value discipline MUST be identified • ODBM patterns and value disciplines SIGNIFICANTLY AID business to effectively deliver value to the stakeholders and generate revenue
  44. Thank You @fatemehahmadi_

Hinweis der Redaktion

  1. While this is a simplified model, it largely captures the shift OD based open innovation approaches in Cities. Note that major changes across the waves is in the
  2. Common approach, sharing of resources and experiences ….
  3. [58] H. Farhan, J. Alonso, T. Davies, J. Tennison, T. Heath, and T. Berners-lee, “Open Data Barometer,” pp. 1–45, 2013.
  4. Strategic Alignment Models are one such category of models that are likely applicable