Some Economics of Cultural PSI: the Micro Perspective

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1 Some Economics of Cultural PSI: the Micro Perspective Massimiliano Nuccio Research Affiliate ASK Bocconi Research Centre Bocconi University Milan - 10 October

2 Agenda Which new sources of data can we access and what is their value? How do companies and (cultural) organizations extract value from open and big data? How can companies/(cultural) organizations organize to exploit data potential and manage their threads? 2

3 Crawling the Internet of Things The dimension of structured information is limited if compared to the total unstructured information Topic Based Industry Specific Company Specific DISCUSSION GROUPS Product Specific New Portals Forum Radar Satellites Industrial process monitor SENSOR DATA GIS Mobile Webcam ERP IT System LEGACY SYSTEM Internal DB Management Systems Business Datawarehouse Web Feeds NEWS New Sites Blogs INFORMATION SOURCES CRM STRUCTURED INFORMATION Partner Pages Job Pages News Pages COMPANY WEBSITES Events Product Pages Press Releases Blog Search Custom Search SEARCH ENGINES Code Search Vertical search Semantic Search Web Search Facebook SOCIAL NETWORK LinkedIn Twitter MySpace 3

4 Value of Open and Big Data DEFINITION Open describes how liquid and transferable data are and big describes size and complexity of data sets VALUE How to increase declining productivity in in public sector? Europe s public sector could potentially reduce the costs of administrative activities by 15 to 20 percent, creating the equivalent of 150 billion to 300 billion ($223 billion to $446 billion) or even higher in new value (MGI, 2011) PSI (open government data) are supposed to deliver against three main objectives (LAPSI): 1. Economic growth innovation in products and business models customized services 2. Transparency and participation 3. Efficiency performance management decision making SOURCE: McKinsey Global Institute analysis,

5 Contribution and challenge of Big Data for public sector What is the contribution of Big Data for the public sector? What is the challenge of Big Data for the public sector? Survey on 152 public-sector employees Source: Govloop, 2014 Survey on 103 executives at small, midsize and large public agencies worldwide Source: Bloomberg Businessweek Research Services (BBRS), 2013 Public sector shows a gap between the goal they hope to achieve with Big Data and actual performance Public sector is not able to quantify benefits and value of Big data 5

6 Data Value Creation in Business Industries BANKING INSURANCE LAW ENFORCEMENT SPORT HEALTH TELCO Applications PROCESS OPTIMIZATION RISK MANAGEMENT SECURITY MARKETING PROCESS AUTOMATION Tools SOCIAL NETWORKS GEOGRAPHICAL ANALYSIS INFORMATION SYSTEM WEB & TEXT MINING ADVANCED ANALYTICS BIG DATA Research & Development COMPUTATIONAL INTELLIGENCE VIDEO & IMAGE PROCESSING GRAPH THEORY STATISTICS 6

7 Applying a Big Data approach to Marketing Intelligence How to use Information on consumers Acquisition Development Retention Consumer s Life Cycle: Time Examples of commercial activities: Member Get Member Direct Marketing Cross Selling Upselling Loyalty Programmes Reward System Anti-churn Campaigns Examples of useful information: Sentiment Analysis on similar products Influencer detection Costumer behaviour in purchase Web Mining Costumer Satisfaction Warning Signs Web Mining 7

8 Applying a Big Data approach to Marketing Intelligence 5 Steps in Predictive Analytics IDENTIFICATION OF BUSINESS NEED ETL DATA ENRICHMENT MODEL VALIDATION Definition of the marketing objective Definition of KPI for measuring performance Definition of the marketing framework(keyw ords, topics, products) (e.g. ROI Maximixation, Upselling, Acquisition, Next best product, Next best action, etc.) Extraction of customer information from CRM Processing of variable to feed downstream activities (e.g. general data, products, price, campaign, loyalty, churn, geographic info, socioeconomic, etc.) Web data acquisition (data harvesting and sieving) Data matching with Social Data (Sensor, enterprise apps, social media, mobile, search, etc.) Processing the information gathered from all sources available with algorithms for text mining, network analysis, pattern recognition, sentiment analysis, spatial analysis, etc. Development of clustering models, prototyping, causal analysis for churn prevention, upselling, acquisition., etc. Evaluation of the results and trade-off analysis (efficiency vs. effectiveness). Simulation of the impact of the models developed on the KPI. Identification of areas of potential development and performance improvements 8

9 The challenge of Big Data for cultural organizations Key questions Although we all share meaning and potential value of Big Data, it is pivotal how to activate and manage the process of transformation required by this revolution. Where do Big Data add value to our company/cultural organizations? Analysis and Strategy> Operations > Business/Service Model Which organizational structure can support the development of a Big Data strategy? Leading business units Leading business units with a central support Leading central team Independent specialized Centre Which technological investment should support analytical capabilities? Manage, process and analyze Big Data MPP database, NoSql, Hadoop, Cuda Cluster Develop an environment separate and parallel to the actual systems 9

10 A roadmap to implement a Big Data strategy EDUCATE EXPLORE ENGAGE EXECUTE Knowledge collection and market monitoring Development of a strategy and roadmap based on business needs and market challenges Pilot projects on big data for testing their value Deploying big data projects and ongoing use of advanced analytics ANALYSIS The screening phase identifies the major areas of business which will have an impact on the adoption of big data (Marketing, Risk,..), the degree of maturity of the company, the positioning of competitors and defines the priorities for development. IMPLEMENTATION The implementation phase iteratively develop tools to support big data and performs analysis in accordance with a defined roadmap: gradual improvement of skills, organization and technology. 10

11 Final Remarks The implementation of an open data / big data strategy should be carefully planned according to specific needs and objectives of organizations in the public sector A clear roadmap should be underpinned by the proof of one or more tests The full exploitation of Big Data requires and expects customer/citizens engagement The challenge of Big Data in the public sector (including cultural organizations) calls for: cultural change organizational change (including skills in data science) technological change access to data 11

12 References Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of big data. McKinsey Quarterly, 4, Kim, K. Y. (2014). Business Intelligence and Marketing Insights in an Era of Big Data, KSII Transactions on Internet and Information Systems (TIIS), 8(2), McKinsey Global Institute (2013), Open data: Unlocking innovation and performance with liquid information McKinsey Global Institute (2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity Mullich, J. (2013). Closing the Big Data Gap in Public Sector. Bloomberg Business Week Nessi, (2012) Big Data, a World of opportunities SAS Institute, 2013, Big Data in Big Companies Yan, J. (2013). Big Data, Bigger Opportunities. White Paper, April, 9 12

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