How to create new business models with Big Data and Analytics

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An introduction to Big Data, developed for training purposes.

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  • The title of this presentation is misleading. it is a basic discussion of big data concepts and has nothing to do with creating new business models.
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  • In-DB analytics is a trend that is a realityIt makes sense for our product. If they want to use us for analytics but they can’t do in-DB analytics, we’re not a full packageEx: algorithm doing time-series analysis in SAS, customer wants to do time-series algorithm in InfiniDB, we don’t want to code all of the algorithms. We want to integrate with the tools out there
  • How to create new business models with Big Data and Analytics

    1. 1. How to Create New Business Modelswith Big Data & Analytics Aki Balogh Calpont Corporation
    2. 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. 3. What is Driving Big Data?
    4. 4. Where is Big Data today?
    5. 5. What is driving Big Data?1. Rising volumes of data2. Falling cost of data management tools3. Rising number of Data Scientists
    6. 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. 7. #2: Data management tools like Hadoopare driving down cost
    8. 8. #3: Data Science as a discipline is growing
    9. 9. What is Big Data?
    10. 10. Big Data is about turning data into insights to drive decision-makingSource: Allen (1999)
    11. 11. A Simple Framework: 3 Vs of Big Data•Volume•Variety•Velocity 11
    12. 12. #1: VolumeSource: Christopher Bingham, Crimson Hexagon. “Better Algorithms from Bigger Data.”
    13. 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 and drive 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 needs that 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 your campaign mix based on behaviors. 5. Social Media Data + Web Data = Measure traffic to your website from social media campaigns and track actual conversions.Source: “Why Web Analytics is Not Enough.” Quantivo.
    14. 14. #3: VelocitySource: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/
    15. 15. What is Analytics?
    16. 16. A Simple Framework for Analytics•Descriptive•Predictive•Prescriptive
    17. 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 Regression Survival 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. 18. Analytics that are Actually Used Classification and regression trees /… 69% 25% 6% Linear Regression 66% 33% Logistic regression or other discrete choice… 61% 29% 10% Association rules 49% 37% 14% K-nearest neighbors 36% 42% 21% Neural networks 30% 36% 34% Box Jenkins, Autoregressive… 30% 35% 35% Exponential smoothing / double exponential… 22% 43% 34% Naïve Bayes 21% 43% 36% Support vector machines 20% 23% 57% Survival analysis 15% 41% 44% Monte Carlo Simulations 13% 47% 40% Frequently Occasionally Not at all Classification and regression trees / decision trees and Linear Regression are the most popular predictive analytics techniques used.Source: Ventana Research Predictive Analytics Benchmark Research 18
    19. 19. Who does Analytics?You don’t need to have a PhD…
    20. 20. Five Common Types of Analytics• Classify o Segmentation, discriminant analysis o Clustering o Unsupervised and supervised machine learning• Trend o Time-series analysis• Optimize o Find the optimal outcome of an objective function (min/max)• Predict o Predict the outcome of a single event• Simulate o Explore the consequences of different choices to help drive decision- making o Open-ended: Scenario planning, DSS
    21. 21. So, what is Analytics?•Descriptive•Predictive•Prescriptive
    22. 22. What can you do with Big Data & Analytics?
    23. 23. What does Big Data Analytics require?Data: data availability + storage + integration + data management tools+Analytics: analytic formulas + statistical integrity + analytic applications+Interpretation: business problem + domain expertise + visualization + decision-makingThis typically requires a team of people with different skillsets.
    24. 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

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