1.How to Create New Business Modelswith Big Data & Analytics Aki Balogh Calpont Corporation

2. Agenda1. What is Driving Big Data?2. What is Big Data?3. What is Analytics?4. What can you do with Big Data & Analytics? 3. What is Driving Big Data? 4. Where is Big Data today? 5. What is driving Big Data?1. Rising volumes of data2. Falling cost of data management tools3. Rising number of Data Scientists 6. #1: Data volumes are growingGrowth in Unstructured Data Types of Unstructured Data Social Media Clickstream data Machine-Generated Data (e.g. logs) Internal Documents Notes (e.g. Patient Charts) Images Video Sound 7. #2: Data management tools like Hadoopare driving down cost 8. #3: Data Science as a discipline is growing 9. What is Big Data? 10. Big Data is about turning data into insights to drive decision-makingSource: Allen (1999) 11. A Simple Framework: 3 Vs of Big DataVolumeVarietyVelocity 11 12. #1: VolumeSource: Christopher Bingham, Crimson Hexagon. Better Algorithms from Bigger Data. 13. #2: Variety A few examples how combining data can dramatically change the way marketers gain customer intelligence and measure campaign effectiveness: 1. CRM Data + Web Data = Understand actual lead quality not just lead quantity anddrive more intelligent drip marketing, lead nurturing and re-marketing programs 2. Call-Center Data + Web data = Analyze calls you can avoid and calls you should avoid.(Example; calls to the Call-Center for simple customer-support or operational needsthat are already serviced online) 3. Past Purchase Data + Web Data = Segment customers based on past buying behavior,and use this to drive targeted web campaigns to loyal customers. 4. Campaign Data + Web Data = Understand multi-touch attribution and optimize yourcampaign mix based on behaviors. 5. Social Media Data + Web Data = Measure traffic to your website from social mediacampaigns and track actual conversions.Source: Why Web Analytics is Not Enough. Quantivo. 14. #3: VelocitySource: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/ 15. What is Analytics? 16. A Simple Framework for AnalyticsDescriptivePredictivePrescriptive 17. Types of Analytics you Could Use ARMA Logistic/Lasso Regression CART Logistic Regression with Adaptive CIR++ Platform Compression Nets Monte Carlo Simulation Discrete Time Multinomial RegressionSurvival Analysis Neural Networks D-Optimality Optimization: LP; IP; NLP Ensemble Model Poisson Mixture Model Gaussian Mixture Model Random Forests Genetic Algorithm Restricted Boltzmann Machine Gradient Boosted Trees Sensitivity Trees Hierarchical Clustering SVD Kalman Filter Support Vector Machines K-Means KNN Linear Regression 18. Analytics that are Actually Used Classification andregression trees /69%25%6% Linear Regression 66% 33% Logistic regression or other discrete choice61%29% 10%Association rules 49%37% 14%K-nearest neighbors 36% 42%21%Neural networks 30% 36%34%Box Jenkins, Autoregressive30% 35%35% Exponential smoothing / double exponential22%43% 34%Nave Bayes 21% 43%36%Support vector machines20%23%57%Survival analysis 15%41% 44% Monte Carlo Simulations13% 47% 40%Frequently Occasionally Not at allClassification and regression trees / decision trees and Linear Regression arethe most popular predictive analytics techniques used.Source: Ventana Research Predictive Analytics Benchmark Research18 19. Who does Analytics?You dont need to have a PhD 20. Five Common Types of Analytics Classifyo Segmentation, discriminant analysiso Clusteringo Unsupervised and supervised machine learning Trendo Time-series analysis Optimizeo Find the optimal outcome of an objective function (min/max) Predicto Predict the outcome of a single event Simulateo Explore the consequences of different choices to help drive decision-makingo Open-ended: Scenario planning, DSS 21. So, what is Analytics?DescriptivePredictivePrescriptive 22. What can you do with Big Data & Analytics? 23. What does Big Data Analytics require?Data: data availability + storage + integration + data managementtools+Analytics: analytic formulas + statistical integrity + analyticapplications+Interpretation: business problem + domain expertise + visualization+ decision-makingThis typically requires a team of people with different skillsets. 24. What can you do with Big Data & Analytics?1. New revenue models o Ex: Rapleaf scraping the web, collecting contact information and selling full datasets1. New user experiences o Ex: Gmail recommendations for people to CC: on your email2. Cost optimization (i.e. deliver same product or service at less cost) o Ex: Give your financial advisors tools to help automate your investment decisions