© 2013 IBM Corporation Version 1.0 The New Eye Insight through Big Data and Analytics: A Case Study on Citizen Sentiment Analysis Sandipan Sarkar, Executive Architect Global Government Center of Competence, IBM Mobile:

© 2013 IBM Corporation2 The world is changing – there is an explosion of data The volume, variety, and velocity of data is growing at an unprecedented rate. 1.3 Billion RFID tags in billion RFID tags today 1 trillion devices are connected to the Internet Twitter processes 12+ terabytes of data every day 80% of world’s information is unstructured content 25+ terabytes of log data every day 4.6 billion camera phones world wide 76 million smart meters in 2009 … 200M by 2014 The Information base of the world doubles every 11 hours

© 2013 IBM Corporation3 Why the data is “big” now? Characteristics of Big Data Source: IBM methodology

© 2013 IBM Corporation4 The challenge is also an opportunity: move analytics closer to big data BI / Reporting Exploration / Visualization Functional App Industry App Predictive Analytics Content Analytics Analytic Applications IBM Big Data Platform Systems Management Application Development Visualization & Discovery Accelerators Information Integration & Governance Hadoop System Stream Computing Data Warehouse New analytic applications drive the requirements for a big data platform Integrate and manage the full variety, velocity and volume of data Apply advanced analytics to information in its native form Visualize all available data for ad- hoc analysis Development environment for building new analytic applications Workload optimization and scheduling Security and Governance

© 2013 IBM Corporation5 Governments are trying to move closer to citizens – sentiment analysis from social media can be a useful vehicle in this journey  How do citizens feel about the agency’s new programmes and policies?  What are the most talked about programmes? Is it good or bad?  What are the most positively talked about attributes in the agency’s programmes? Can the agency replicate it to other programmes?  Is there negative chatter that the agency should respond to?  Who are advocates and skeptics of the agency?  Where the agency should be actively listening? Source: Gartner Open Government Maturity Model Building such insight is a daunting task because of the volume, variety, velocity and veracity of information that social media can generate.

© 2013 IBM Corporation6 Citizen sentiment analysis in social media: a confluence of big data, natural language processing, information extraction and visual analytics

© 2013 IBM Corporation7 Citizen sentiment analysis in social media for a major social benefits organisation in US revealed valuable insights  Key Observations – Benefits and Services received more than double the amount of coverage than Healthcare related buzz – Disability Compensation and Employment Benefits are the most talked about topics among all the benefits and services offered by the agency. Mental Health is the most talked about topic among Healthcare initiatives – Disability Compensation, Insurance, and Pension contribute heavily towards negative sentiments, whereas Employment Benefits, Dependent’s Assistance, and Home Loan Benefits are talked in positive light. – July 2012 hit all time high negative sentiment, because of a single news  Root Cause Analysis – The agency was suffering from huge back- logs in claims processing – Awareness of benefits and services was little among its clients. Agency needed to transform its outreach activities. – Agency had a poor social media strategy.

© 2013 IBM Corporation8 Questions? Thank you! Sandipan Sarkar