1 3 Principles for Maximum Participation in Open Data on the Web Bob Schloss, IBM Research April 19, 2013 with John M Cohn, Kelvin Lawrence, Susan Malaika, John Reinhardt, David Rook, Biplav Srivastava
2 Today s Discussion In a Nutshell 0 Background on our experience (2 slides, more online) 1 Incentives must be in place to have economically significant amounts of Open Data 2 Trust (Multiple Levels) with appropriate Security (where needed) 3 Data Ownership and Provenance 2
3 Background on our experience (slide 1 of 2) Open data for innovative communities: Dublinked (powered by Open Innovation Platform) A city intelligence platform sharing information between Dublin s local authorities, Universities and major businesses. Launched in October 2011 with over 3,000 individual data sets across environment, planning, water and transport. The purpose of the partnership is to exploit the intelligence platform to transform city systems and stimulate the creation and growth of high-value businesses with international markets. 10 s of applications have been implemented from the Open Data platform resulting in the first new businesses backed by VC with global markets The partnership has generated specific business cases for Dublin to invest in transformations of city systems with proven social, economic and environmental benefits.
4 Background on our experience (slide 2 of 2) What We Have Learned The Key Lies in Delivering Trusted Information -- On Multiple Levels Useful Combinable yielding meaning for your stakeholders Can receiver merge & deliver to make it all valuable? Governed & Secure Rules are in place and tools are deployed to limit visibility, secure sensitive information, and protect privacy Accurate & Complete Complex and disparate data transformed, cleansed, reconcile and delivered Can I Trust My Partner and My Own Agency? Can I Trust The Information?
5 Background on our experience (slide 2 of 2) What We Have Learned The Key Lies in Delivering Trusted Information -- On Multiple Levels Useful Combinable yielding meaning for your stakeholders Can receiver merge & deliver to make it all valuable? Governed & Secure Rules are in place and tools are deployed to limit visibility, secure sensitive information, and protect privacy Can I Trust My Partner and My Own Agency? Accurate & Complete Complex and disparate data transformed, cleansed, reconcile and delivered Prior Path to Success Sense and Respond Instinct and Intuition Skilled Analytics Experts Back Office Decision Support Automated Processes Predict and Act Real-time, Fact-driven Everyone Point of Impact Optimized Can I Trust The Information?
6 1 Incentives must be in place to have economically significant amounts of Open Data 1 Recognition: At this level, the producer wants simply to be recognized for the data produced and found valuable. 2 Indirect Benefit: At this level, the data producer wants the consumer to benefit using open data and credit for participating. 3 Direct Benefit: At this level, the data producer wants value from usage by consumer. 6
7 1 Incentives must be in place to have economically significant amounts of Open Data Recognition: At this level, the producer wants simply to be recognized for the data produced and found valuable. Indirect Benefit: At this level, the data producer wants the consumer to benefit using open data and credit for participating. Direct Benefit: At this level, the data producer wants value from usage by consumer. For Data Providers: -a way (if they choose to use it) to guarantee that their (multimedia) Logo appears in front of the relevant consuming enterprise participant as a function of time, volume, and location of the consuming programmatic logic. -a way (if they choose to use it) to receive identities (minimally, addresses or URIs) when their data is fetched. Current web technologies, such as Browser-held Cookies, can simplify the annoyance factor to web users of providing this identity information repeatedly. - a way (if they choose to use it) to charge for the data delivered, not only as a function of the data itself, but as a function of the requester organization and the technical protocols used to transfer the data (which could vary considerably in their resources used, time to execute, etc.) 7
8 2 Trust and Security Mechanisms for Ensuring Data Quality 1 2 who collected this data (including what versions of what systems did any preprocessing of it), was it opt-in who authorized distribution of this data which version of this data was actually transferred to satisfy a particular request has any assertion of "exclusive access" or "exclusive until after date-time access" be made by the data supplier has any assertion of "only complete redistribution permitted" (no partial redistribution) been made 8 6 has any assertion been made as to the authenticity of the data and are there guarantees in place that it has not been tampered with.
9 2 Trust and Security Mechanisms for Ensuring Data Quality 1 2 who collected this data (including what versions of what systems did any preprocessing of it), was it opt-in who authorized distribution of this data which version of this data was actually transferred to satisfy a particular request has any assertion of "exclusive access" or "exclusive until after date-time access" be made by the data supplier has any assertion of "only complete redistribution permitted" (no partial redistribution) been made 9 6 has any assertion been made as to the authenticity of the data and are there guarantees in place that it has not been tampered with.
10 2 Trust and Security Mechanisms for Ensuring Data Quality 1 2 who collected this data (including what versions of what systems did any preprocessing of it), was it opt-in who authorized distribution of this data which version of this data was actually transferred to satisfy a particular request has any assertion of "exclusive access" or "exclusive until after date-time access" be made by the data supplier has any assertion of "only complete redistribution permitted" (no partial redistribution) been made 10 6 has any assertion been made as to the authenticity of the data and are there guarantees in place that it has not been tampered with.
11 2 Trust and Security Mechanisms for Ensuring Data Quality 1 2 who collected this data (including what versions of what systems did any preprocessing of it), was it opt-in who authorized distribution of this data which version of this data was actually transferred to satisfy a particular request has any assertion of "exclusive access" or "exclusive until after date-time access" be made by the data supplier has any assertion of "only complete redistribution permitted" (no partial redistribution) been made 11 6 has any assertion been made as to the authenticity of the data and are there guarantees in place that it has not been tampered with.
12 3 Data Ownership and Provenance 1 Before open data transfer time, data consumers understand their obligations with respect to later 3rd party allegations of improper distribution or use. 2 Before open data transfer time, data providers understand the obligations with respect to later 3rd party allegations of improper distribution they will need to manage because data consumers are not taking full responsibility to handle them. 12
13 Today s Discussion In a Nutshell 1 Incentives must be in place to have economically significant amounts of Open Data 2 Trust (Multiple Levels) with appropriate Security (where needed) avoids maldata 3 Data Ownership and Provenance We look forward to working with you to turn open data into Plentiful Big Open Data! 13
14 Backup Big Data Commission Getting Started Recommendations For Government Agencies Big Data is here -- and it is accelerating. For government agency leaders focused on taking the first steps, the Commission makes the following recommendations: 1. Understand the Art of the Possible. 2. Start with a clear mission or business requirement, and fully define a discrete set of use cases. 3. Take inventory and understand your data assets. 4. Assess your current set of capabilities and technical architecture against what is required to support your initial use cases. 5. Explore which data assets can be exposed for public consumption, to drive innovation and the development of Big Data solutions.
15 English.pdf Open data creating change: Helsinki The IBM Smarter City Challenge A team of six IBM experts worked for 3 weeks to deliver recommendations on visualizing open data to Mayor Jussi Pajunen and city stakeholders. The challenge addressed the challenge of making digital data from the city s open data initiative accessible and useful to citizens through engaging visualizations to support the World Design Capital event in Helsinki in The IBM team developed recommendations for: Defining a vision for the future based on citizen engagement through visualization of open data Defining the components necessary to grow a sustainable, repeatable platform, process and ecosystem to leverage the principles of open data, and turn data into information, into action and into change. The City have announced that it plans to adopt the recommendations made in the Challenge report. Examples of design sketches which were produced as part of the vision for visualization based on open data
16 Open Data and Visualisation for Research: Engage Engage is an EU funded e-infrastructure project providing an open data platform for the Researchers community. Supports dataset publishing and provisioning activities such as curation, linking, visualization, searching and more. IBM developed: Data visualization services for tabular data (grid, charts, maps). Integration with Google Refine for cleansing, curation and linkage. Engage data publishing and provisioning components
17 Open data and the business of weather: Deep Thunder Provides customizable, accurate, high resolution weather predictions up to three days ahead. Models, analyses, visualises and disseminates insight from open data, organisational data, commercial data and crowdsourcing. Forecasts the impact of weather on businesses and systems such as energy, cities, transportation, and agriculture. Cities, water, energy, and transportation systems can be Smarter, safer and more efficient if the effects of weather on underlying business processes can be predicted and optimized: Rio de Janeiro - flooding model at 1m resolution Energy: foresee outages; predict supply from renewable resources; proactively manage load and maintenance... Cities: foresee emergencies; proactively manage demand, water quality and maintenance... Transport: foresee disruption to road, rail, air and marine transport; proactively manage capacity and demand; proactively manage maintenance... Agriculture: foresee flooding and droughts; proactively manage planting, harvesting, fertilisation and supply chain activities... Others: insurance, mining, retail, sports, tourism... Tropical Storm Irene - Forecasted Outages per Substation
18 City Forward a free web platform for sharing open data City Forward is a free, webbased platform that enables users city officials, researchers, academics and interested citizens world-wide to view and interact with open city data while engaging in an ongoing public dialogue IBM Corporation Quick Facts: 129 Cities 44 Countries 122 Datasets 147 City Stories 18
19 Quick Summary Big Data in Govt Commission Report Released 2012 October 3 The Commission was formed in May 2012 IBM & SAP Named as Chairs Brought together experts from Government, Academia and Industry The report seeks to De-Mystify Big Data, and focus on the business and mission value it will deliver Intent is to Provide clear recommendations and a roadmap for getting started Copyright
20 Gov t Agencies, Businesses, NGOs Leading The Way Optimize decision-making with actionable insights from blended data Variety Inability to Volume predict Velocity insight Variety Inability Lack to predict of insight Volume ent access Volume Velocity Velocity efficient access Variety Velocity Prior Path to Success Sense and Respond Instinct and Intuition Skilled Analytics Experts Back Office Decision Support Today s Leaders Predict and Act Real-time, Fact-driven Everyone Point of Impact Automated Processes Optimized 20
21 What We Have Learned Big Data Requires A Different Approach It Morphs The Traditional Analytics Process Big Data Approach Traditional Approach Business Users IT Determine what question to ask Delivers a platform to enable creative discovery IT Business Structures the data to answer that question Combines Open with Proprietary Data Explores what questions could be asked Structured & Repeatable Analytics Query Based -- Question Drive Data Citizen Surveys - Push Monthly, Weekly, Daily Data At Rest VS. Iterative & Exploratory Analytics Autonomic -- Insight Drives Answers Citizen Sentiment -- Pull Persistent, Just-in-Time & Ad Hoc Data In Motion
22 Open data with impact: what we have learned so far Open is expanding how governments manage relationships with citizens and the data that both need The understanding of open is uneven and definitions have evolved Many jurisdictions are already adopting this new, evolving style of public administration Providing raw data 2. Seeding innovation 3. Enabling collective problem solving 4. Creating the bazaar The potential benefits for both citizens and government are compelling A different mental model is needed to guide leaders in their open journey Source: IBM Institute for Business Value What Strategically integrate, execute on and manage four areas Information Engagement model Digital Platform Analytics competency How Take a series of three steps and repeat progressively over time 1. Define and measure openness 2. Open up : 3. Capitalize on Open To best position for realizing potential benefits, leadership and flexible, sustainable engagement model(s) are essential To sustain the journey, set key indicators, measure and manage the balance between different engagement models
23 What We Have Learned The Key Lies in Delivering Trusted Information -- On Multiple Levels Useful Combinable yielding meaning for your stakeholders Can receiver merge & deliver to make it all valuable? Governed & Secure Rules are in place and tools are deployed to limit visibility, secure sensitive information, and protect privacy Can I Trust My Partner and My Own Agency? Accurate & Complete Complex and disparate data transformed, cleansed, reconcile and delivered Can I Trust The Information?
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