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Open Data for Financial Innovations in the Developing World

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Financial innovations are key to getting developing countries like India their rightful role in the global economy. However, such innovations depend on data, advanced analytics and timely access to insights. In this talk, we will discuss open data collected by governments and people and how it can bootstrap innovations that matter. Open data helps not only overcome initial data bottleneck but also helps standardize solutions for scalability and wider adoption.

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Open Data for Financial Innovations in the Developing World

  1. 1. OPEN DATA FOR 
 FINANCIAL INNOVATIONS IN THE 
 DEVELOPING WORLD DR. BIPLAV SRIVASTAVA A C M D I S T I N G U I S H E D S C I E N T I S T , A C M D I S T I N G U I S H E D S P E A K E R S E N I O R R E S E A R C H E R A N D M A S T E R I N V E N T O R , I B M R E S E A R C H – I N D I A 11Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  2. 2. Why This Talk? Main Messages —  Financial Innovations are key for a developing country like India to provide better opportunities to its citizens ¡  Impacts not only finance (Banking, Insurance, …) ¡  But all other areas of a society (Healthcare, Transportation, Industry) —  Innovations depend on data, analysis and timely access —  Open data is often the most promising source to start making quick impact —  Eventual aim should be to scale innovations with other data sources and reach production scale to people seamlessly 2Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  3. 3. Actions to Take Tutorial on 27 July 2015 @ IJCAI 2015 —  Join: “AI in India” google group – ¡  https://groups.google.com/forum/#!forum/ai-in-india —  Participate in machine learning competition on using open data for health area (disease, finance, …) ¡  Start: https://www.facebook.com/dataview2016 ¡  Competition page: http://gator3080.hostgator.com/~sigdata//comad2016/ data_challenge_competition.html ¡  Data and insights sought: http://gator3080.hostgator.com/~sigdata//comad2016/ data_sources.html 3
  4. 4. Motivating Examples 4Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  5. 5. Europe GDP Growth Source: http://ec.europa.eu/ eurostat/web/national- accounts/statistics- illustrated 5Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  6. 6. Complexity and Innovation —  Complexity ¡  Many countries: 28 in EU, 19 use Euro ¡  Changes within Europe; Yugoslavia broke up during 2004-2010 ¡  There have been continuous currency changes since 1999 when Euro was introduced; since 2001, Cyprus, Slovenia, Malta, Slovakia … have joined or changed currency —  Innovation ¡  Linked data to represent data, metadata and relationships ¡  Contexual and holistic visualization 6Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  7. 7. Indian Reality – Kingfisher Airline Case —  A two-term Rajya Sabha MP ¡  Heading company and taking loans from banks ¡  Leading airline to collapse ¡  Delaying repayment —  The airline (company) ¡  Not paying employees and vendors ¡  Not even paying income tax deducted from employees —  Consequence ¡  Airline collapses leading to loss to travellers and employees ¡  Banks suffer heavy losses ¡  Little impact on company leader 7Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  8. 8. Data on Defunct Kingfisher Airlines 8Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  9. 9. Reality in a Developing Country —  In private sector, hard to know about genuineness of ¡  Individuals and companies ¡  Their needs and expenses —  In government sector, hard to know about ¡  Spending – budgeted and actuals ¡  Effectiveness of their spending ¡  Benchmarking with best practices, e.g., return of investment —  Consequence ¡  Little loans available to the needy ¡  High non-performing assets (NPAs) of banks ¡  Lower performance of markets since investors stay away ¡  Lower country growth, high unemployment and poverty 9Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  10. 10. Resources for Finding About a Person —  Public encyclopedia: Wikipedia ¡  Example: http://en.wikipedia.org/wiki/Vijay_Mallya —  Specialized databases ¡  Indianboards: http://indianboards.com/pages/index.aspx ÷  Example: Infosys ( http://indianboards.com/pages/companyprofile.aspx? code=C0000604) ¡  US CEOs: http://ceo.com ¡  Forbes profile: ÷  Example: http://www.forbes.com/profile/ginni-rometty/ 10Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  11. 11. Resources for Finding About a Company —  Market regulators ¡  SEC (USA): Edgar filings - http://www.sec.gov/edgar/searchedgar/companysearch.html ¡  Ministry of Corporate Affairs (MCA) database: http://www.mca.gov.in/DCAPortalWeb/dca/ MyMCALogin.do?method=setDefaultProperty&mode=31 —  Private market intelligence companies ¡  EMIS: ÷  Example: http://www.securities.com/php/company-profile/KR/ Samsung_Electronics_CoLtd_en_1651328.html 11Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  12. 12. Snapshot: Financial Innovations Needed for Developing Countries —  [Individuals] Data-based generation of ¡  Credit profile of individuals ¡  Criminal profile of individuals —  [Entities] Data-based generation of ¡  Credit profile of legal entities – Companies, NGOs ¡  Ranking of companies in an industry —  [Governments] Data-driven automatic ¡  Audit of government programs for effectiveness ¡  Ranking of cities, state governments ¡  Corruption assessment —  Prediction of ¡  Stocks ¡  Initial public offers (IPOs) ¡  Tax collection 12Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  13. 13. Outline —  Motivating Examples —  Open Data —  Analytical Techniques —  Discussion ¡  Pattern in Building Usable Systems ¡  Smart City – What to Solve? ¡  Call to Action 13Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  14. 14. Open Data 14Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  15. 15. Open Data —  Open data is the notion that data should not be hidden, but made available to everyone. The idea is not new. —  Scientific publications follow this: “standing on the shoulders of giants” ¡  Science stands for repeatability of results and hence, sharing ¡  The scientific community asserts that open data leads to increased pace of discovery. (See: Ray P. Norris, How to Make the Dream Come True: The Astronomers' Data Manifesto, At http://www.jstage.jst.go.jp/article/dsj/6/0/6_S116/_article, Accessed 2 Apr, 2012) —  Governments are the new source for open data ¡  Data.gov efforts world-wide; 400+ governmental bodies, including 20+ national agencies, including India, have opened data ¡  In India, additional movement is “Right to Information Act” 15Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  16. 16. Not to Be Confused With Orthogonal Trend – Big Data —  Volume —  Variety —  Velocity —  Veracity —  … Cartoon critical of big data application, by T. Gregorius. http://upload.wikimedia.org/wikipedia/commons/thumb/b/b3/ Big_data_cartoon_t_gregorius.jpg/220px- Big_data_cartoon_t_gregorius.jpg 16Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  17. 17. 400+Data Catalogs of Public Data As on 21 July 2015 17Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  18. 18. Data.gov (USA) As on 16 June 2015 18 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems
  19. 19. City Level – Chicago, USA 19 As on 16 June 2015 Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  20. 20. Data.gov.in (India) As on 16 June 2015 20 Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  21. 21. Peek into the Future - Amsterdam http://citydashboard.waag.org/ 21Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  22. 22. Illustration of Levels Source: http://5stardata.info/ Does Opening Data Make It Reusable? No 1 2 3 4 5 22Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  23. 23. India: Right to Information Act —  Any citizen “may request information from a "public authority" (a body of Government or "instrumentality of State") which is required to reply expeditiously or within thirty days.” ¡  Passed by Parliament on 15 June 2005 and came fully into force on 13 October 2005. Citation Act No. 22 of 2005 —  Lauded and reviled ¡  Brought transparency ¡  Also, ÷  Increased bureaucracy ÷  Shortcomings in preventing corruption —  More information ¡  http://en.wikipedia.org/wiki/Right_to_Information_Act ¡  http://rti.gov.in 23Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  24. 24. Data Quality in Public Data in India —  Right to Information ¡  Not even 1* ¡  Information available to requester, but no one else —  Data.gov.in ¡  2-3* ¡  Available in CSV, etc but not uniquely referenceable —  Open data movements are moving to linked data form for semantics 24Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  25. 25. Semantics for Published Data 25 Classify data in public domain. Use schema.org as illustration. ¡  Select an area (e.g., food, news events, crime, customs, diseases, …) ¡  Build + disseminate the catalog tags via a website ¡  Encourage publishers to use meta-data tags and enable search Catalog/ ID General Logical constraints Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part-of… Credits: Ontologies Come of Age McGuinness, 2001 From AAAI Panel 99 – McGuinness, Welty, Uschold, Gruninger, Lehmann Plus basis of Ontologies Come of Age – McGuinness, 2003 Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  26. 26. Still Confused on Semantics? Start with Linked Data Glossary 26Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  27. 27. Open Data References —  Concept ¡  Open Data, At http://en.wikipedia.org/wiki/Open_data, ¡  Open 311, At http://open311.org/ ¡  Catalog of Open Data, At http://datacatalogs.org/dataset ¡  Data City Exchange: http://www.imperial.ac.uk/digital-city-exchange —  India specific ¡  Open data report in India, At http://cis-india.org/openness/publications/ogd-report —  Standards ¡  W3C, At http://www.w3.org/2011/gld/ ¡  5 Star Linked Data ratings, At http://www.w3.org/DesignIssues/LinkedData.html —  Applications and ecoystems ¡  Introduction to Corruption, Youth for Governance, Distance Learning Program, Module 3, World Bank Publication. Accessed on June 15th 2011, At http://info.worldbank.org/etools/docs/library/35970/mod03.pdf ¡  Dublinked, At http://dulbinked.ie 27Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  28. 28. Analytical Techniques: Shades 28Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  29. 29. Advanced AI Techniques (Analytics) like Planning & Machine Learning make use of data and models to provide insight to guide decisions Models Analytics Data Insight Data sources: Business automation Instrumentation Sensors Web 2.0 Expert knowledge “real world physics” Model: a mathematical or algorithmic representation of reality intended to explain or predict some aspect of it Decision executed automatically or by people 29Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  30. 30. Example: Talks —  Are they useful? (Descriptive) ¡  Answering needs an assessment about the event —  If it happens next time, how many will attend? (Predictive) ¡  Above + Answering needs an assessment about unknowns (e.g., future) —  Should you attend? (Prescriptive) ¡  Above + Answering needs understanding the goals and current status of the individual 30Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  31. 31. Analytics Landscape Degree of Complexity CompetitiveAdvantage Standard Reporting Ad hoc reporting Query/drill down Alerts Simulation Forecasting Predictive modeling Optimization What exactly is the problem? What will happen next if ? What if these trends continue? What could happen…. ? What actions are needed? How many, how often, where? What happened? Stochastic Optimization Based on: Competing on Analytics, Davenport and Harris, 2007 Descriptive Prescriptive Predictive How can we achieve the best outcome? How can we achieve the best outcome including the effects of variability? 31Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  32. 32. ML References —  WEKA ¡  Website: http://www.cs.waikato.ac.nz/~ml/weka/index.html ¡  WEKATutorial: ÷  Machine Learning withWEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka. ÷  A presentation which explains how to useWeka for exploratory data mining. ¡  WEKA Data Mining Book: ÷  Ian H.Witten and Eibe Frank, Data Mining: Practical Machine LearningTools and Techniques (Second Edition) ÷  http://www.cs.waikato.ac.nz/ml/weka/book.html ¡  WEKAWiki: http://weka.sourceforge.net/wiki/index.php/Main_Page —  Jiawei Han and Micheline Kamber, Data Mining: Concepts andTechniques, 2nd ed. —  http://www.kdnuggets.com/2015/03/machine-learning-table-elements.html 32Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  33. 33. Discussion: A Pattern in Building Usable Systems 33Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  34. 34. Recap of Key Points from Finance Scenarios —  Very difficult to find about persons, companies, states reliably —  This is leading to wastage, e.g., non-performing assets in banking system —  Outside finance: wastage in public spending (healthcare, transportation, industrial production, …), business and individual spending —  Information technology (IT) and financial innovations are needed, especially in developing countries 34Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  35. 35. Real-World Applications of ICT Follow a Pattern n Value (from Action, Decisions) – Providing benefits that matter, to people most in need of, in a timely and cost-efficient manner. Going beyond technology to process and people aspects. n Data + Insights – Available, Consumable with Semantics, Visualization / Analysis n Access - Apps (Applications), Usability - Human Computer Interface, Application Programming Interfaces (APIs) 35Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  36. 36. Example – Financial Innovations —  Decision Value – To individuals, businesses, government institutions ¡  Individuals Examples – Which person to financially trust? Which bank to trust? ¡  Govt Examples – What company to give contracts? ¡  Business Examples – Which companies and individuals to give credit to? What discounts to give? —  Data – Quantitative as well as qualitative ¡  Open data ¡  Social data ¡  Transactional data —  Access – ¡  Today, little, reliable information Key Idea: Can we make insights available when needed and help people make better decisions? 36Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  37. 37. Example – Public Health Innovations —  Decision Value – To individuals, businesses, government institutions ¡  Individuals Examples – Which doctor should I go? Which hospital should I go? What health policies should I take? ¡  Govt Examples – What diseases should be of focus? Which hospitals should be given grants? Which health programs should be discontinued? —  Data – Quantitative as well as qualitative ¡  Past incidents – Cases, deaths, spending ¡  Health trends – vaccines, epidemics, health instruments ¡  Financial trends – insurance, policies, social behaviors —  Access – ¡  Today, little, and that too in health / technical jargon ¡  In pdf documents, website Key Idea: Can we make insights available when needed and help people make better decisions? 37Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  38. 38. DataView 2016 Tutorial on 27 July 2015 @ IJCAI 2015 Data and insights sought: http://gator3080.hostgator.com/~sigdata//comad2016/data_sources.html 38 Insights sought 1. What diseases are most prevalent in a given area (e.g., state, district, city, by keyword)? 2. Which diseases have been better controlled than others in India? What states have done better than others? Are there approaches which have worked for controlling / reducing instances of diseases better than others? 3. How much money has been allocated to tackle specific diseases compared to others? Which regions do better than others in controlling diseases relative to money spent? 4. Is their a relationship between water-borne diseases and their relation to water pollution? Datasets Health • H-DS-1: http://data.gov.in/catalog/number-cases-and-deaths-due-diseases , AllIndia (from 2000 to 2011) and State-wise (2010 and 2011) number of cases and deaths due to specified diseases (Acute Diarrhoeal Diseases, Malaria, Acute Respiaratory Infection, Japanese Encephalitis, Viral Hepatitis). • H-DS-2: http://data.gov.in/catalog/cases-and-deaths-due-kala-azar , Cases and Deaths due to the illness Kala-Azar in Bihar, West Bengal and Country during the years 1996 till 2000. • H-DS-3: https://data.gov.in/catalog/cases-and-deaths-due-japanese-encephalitis-and-dengue-dhf-during-tenth-plancases and deaths due to Japanese Encephalitis and Dengue / DHF during Tenth Plan. • H-DS-4: https://data.gov.in/catalog/water-quality-affected-habitations, Water Quality Affected Habitations • H-DS-5: Hospital Directory with Geo Code as on September 2015, https://data.gov.in/catalog/hospital-directory-national-health-portal Expenditure • F-DS-1: https://data.gov.in/catalog/outlays-and-expenditure-aids-control-programme-during-ninth-plan, outlays and expenditure of AIDS Control Programme during Ninth Plan. • F-DS-2: https://data.gov.in/catalog/public-sector-outlaysexpenditure-during-eleventh-five-year-plan, public sector outlays and expenditures during Eleventh Five Year Plan (2007-12) under various Heads of Development (Rs. Crore). • F-DS-3: http://data.gov.in/catalog/outlays-department-health-agreed-planning-commission-during-tenth-plan , data related to 9th Plan Allocation, 9th Plan Anticipated Expenditure, 10th Plan Allocation as Agreed by Planning Commission. • F-DS-4: https://data.gov.in/catalog/percentage-share-household-expenditure-health-and-drugs-various-states-during-eleventh-five, data related to percentage share of household expenditure on health and drugs in various states during Eleventh Five Year Plan. • F-DS-5: https://data.gov.in/catalog/state-wise-plan-outlays-and-expenditure, table provides state-wise plan outlays and expenditure during 2011-2012. • F-DS-6: https://data.gov.in/catalog/outlay-tenth-plan-tenth-plan-sum-annual-outlay-and-tenth-plan-actual-expenditure-department, data related to Outlay Tenth Plan, Tenth Plan (200207) sum of Annual Outlay and Tenth Plan (2002-07) Actual Expenditure for Department of Health and Family Welfare. Water Quality • W-DS-1: https://data.gov.in/catalog/status-water-quality-india-2012, http://data.gov.in/catalog/number-cases-and-deaths-due-diseases , status of Water Quality in India in 2012 • W-DS-2: https://data.gov.in/catalog/status-water-quality-india-2008-and-2011, status of Water Quality in India - 2008 and 2011
  39. 39. Example –River Water Pollution —  Decision Value – To individuals, businesses, government institutions ¡  Individuals Examples – Can I take a bath without getting sick? What crops should I grow? What water should I drink and pay for? ¡  Govt Examples – How should govt spend money on sewage treatment for maximum disease reduction? How should it inspect industries? —  Data – Quantitative as well as qualitative ¡  Dissolved oxygen, ¡  pH, ¡  … 30+ measurable quantities of interest —  Access – ¡  Today, little, and that too in water technical jargon ¡  In pdf documents, website Key Idea: Can we make insights available when needed and help people make better decisions? 39Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  40. 40. Discussion: Smart City 40Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  41. 41. What is a Smart City? Smart city can mean one or more of the following: —  As a resource optimization objective, it is to know and manage a city's resources using data. —  As a caring objective, it is about improving standard of life of citizens with health, safety, etc indices and programs. —  As a vitality objective, it is about generating employment and doing sustainable growth. A city leadership can choose among these or define their own objective(s) and manage with measurements to pro-actively achieve it 41 See other FAQs at: https://sites.google.com/site/biplavsrivastava/research-1/intelligent-systems/scfaqs Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  42. 42. 42 Smarter Cities solution paths leverage a similar approach Uniquevaluerealized Use of Smarter Cities capabilities Manage
 Data1 Analyze
 Patterns2 Optimize Outcomes 3 Integrate service information to improve department operations Develop integrated view to improve outcomes and compliance Leverage end-to-end case management to optimize service delivery Ç Improve service levels È Reduce fraud and abuse Ç Focus on the citizen Ç Savings from overpayment Ç Assistance with compliance Ç Integrated case management Ç Automation of citizen support È Reduce operating costs Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  43. 43. India’s 100 Smart Cities 43Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015 Details: https://sites.google.com/site/biplavsrivastava/smart-cities-in-india
  44. 44. Comments on India’s 100 City Plans —  A much-needed, much-delayed, start ¡  JNURM and earlier initiatives did not show impact —  However selection criteria was non-technical ¡  Focus was on funding feasibility (center-state) and administrative considerations ¡  No commitment on measurable improvement of any metric in any city domain —  Opportunity to impact India’s transformation (theoretically) ¡  However, environment to try out India-specific, new innovations needs to be created ¡  Focus has to be on improvement metrics; accountability for money spent; quality outcomes 44Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  45. 45. Discussion: Call for Action 45Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  46. 46. Smart City Challenges —  From resource angle, decrease waste/ inefficiency while improving service delivery to citizens —  Problems are old but accentuated today by population growth and reducing resources —  Open Data, effectiveness of analytical methods hold promise —  Challenges ¡  Provide value quickly ¡  Use value synergies from different domains (e.g., finance, health, environment, traffic, corruption …) ¡  Grow to scale 46Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  47. 47. Common Descriptive Analytics Patterns, Accelerated with Open Data —  Correlation of outcomes, across ¡  Data sources in same domain ¡  Different domains —  Return of investment analysis ¡  Money invested v/s Metrics to measure improvement in domain ¡  Comparison of performance with history ¡  Comparison of performance with other regions 47Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  48. 48. Employing All Data – Data Fusion —  Open Data is one source ¡  Often easiest to get but with issues (e.g., at aggregate level, with gaps, imprecise semantics) —  Social is another promising data ¡  People are anyway generating it (People-as-sensors) ¡  However, social sites have varying data reuse permissions, license costs, access limits ¡  Big data techniques already being used here —  Use sensor data if available ¡  Internet of Things (IoT) and big data techniques are relevant ¡  Most prevalent in health, environment and transportation —  Key is to release the fused data also for reuse 48Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  49. 49. Building Community for Innovations —  Multi-disciplinary ¡  In AI ¡  In Computer Science ¡  In science: domain (finance, health, transport, …), techniques (CS, engg.) and evaluation (public policy, …) —  Multi-stakeholder ¡  Citizens ¡  Government ¡  Academia ¡  Business/ Industry ¡  Non-profits, … —  Getting to scale is key 49Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  50. 50. Main Messages —  Financial Innovations are key for a developing country like India to provide better opportunities to its citizens ¡  Impacts not only finance (Banking, Insurance, …) ¡  But all other areas of a society (Healthcare, Transportation, Industry) —  Innovations depend on data, analysis and timely access —  Open data is often the most promising source to start making quick impact —  Eventual aim should be to scale innovations with other data sources and reach production scale to people seamlessly 50Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015
  51. 51. Thank You Merci Grazie Gracias Obrigado Danke Japanese French Russian German Italian Spanish Portuguese Arabic Traditional Chinese Simplified Chinese Hindi Romanian Korean Multumesc Turkish Teşekkür ederim English Dr. Biplav Srivastava, sbiplav@in.ibm.com http://www.research.ibm.com/people/b/biplav/ 51Talk at IDRBT Doctoral Consortium, Hyderabad 11 Dec 2015

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