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Webinar Big Data Predictive Analytics 101

Webinar Big Data Predictive Analytics 101. Mike Gualtieri, Principal Analyst. September 7, 2012. Please call in at 10:55 a.m. Eastern time. Twitter: @mgualtieri. Outlook. Business intelligence is the top business app. Real-time analytics is rising fast. Data continues to grow.

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Webinar Big Data Predictive Analytics 101

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  1. WebinarBig Data Predictive Analytics 101 Mike Gualtieri, Principal Analyst September 7, 2012. Please call in at 10:55 a.m. Eastern time Twitter: @mgualtieri

  2. Outlook

  3. Business intelligence is the top business app

  4. Real-time analytics is rising fast

  5. Data continues to grow

  6. The past three years have shown an increased adoption of predictive analytics

  7. “Knowledge is power.” Francis Bacon (1561–1626) Francis Bacon described a rational procedure to establish causation between phenomena based on induction.

  8. “Big data can lead to an explosion of knowledge . . .” - Big Data Predictive Analytics, Forrester Research

  9. Forrester estimates there will be more than 1 billion people using smartphones and tablets by 2016. 1B

  10. Cisco IBSG predicts there will be 25 billion devices connected to the Internet by 2015. 25B

  11. More people using more technology means more big data. 7B

  12. What exactly is big data?

  13. Volume, velocity, and variety are measures of big data

  14. It all comes down to how well you can handle it. It’s relative.

  15. Big data activities are as important as the three V’s measures

  16. What’s your big data score? Activities Measures 5 = Not required or handled perfectly 3 = Handled but could be improved 1 = Handled poorly but with frequent negative business impact 0 = Current or future needs exist but are not handled. Source: Mike Gualteri, “What’s Your Big Data Score?” Mike Gualtieri’s Blog For Application Development & Delivery Professionals, May 17, 2012

  17. What’s your big data score mean? Source: Mike Gualteri, “What’s Your Big Data Score?” Mike Gualtieri’s Blog For Application Development & Delivery Professionals, May 17, 2012

  18. Do you have the tools and technologies to handle big data? Big data

  19. It is not surprising that transactional data is most popular for big data “What types of data/records are you planning to analyze using big data technologies?” (Multiple responses accepted) Base: 60 IT professionals; Source: June 2011 Global Big Data Online Survey

  20. Why do Forrester clients consider or implement big data? “What are the main business requirements or inadequacies of earlier-generation BI/DW/ET technologies, applications, and architecture that are causing you to consider or implement big data?” Base: 60 IT professionals; Source: September 20, 2011, “How Forrester Clients Are Using Big Data” Forrester report

  21. Most Forrester clients are using commercial technology for big data “What technology do you use for big data applications?” (multiple responses accepted) Base: 60 IT professionals; Source: September 20, 2011, “How Forrester Clients Are Using Big Data” Forrester report

  22. Predictive analytics can find meaning in big data. Predictive analytics

  23. Just a few examples of how organizations use predictive analytics today

  24. Predictive analytics finds a model that acts like a formula to find the answer

  25. Predictive models can be represented in many different ways depending on the technique used Formula Decision trees Code Combination of any of the above

  26. Big data comes in many varieties

  27. Predictive analysis is powered by statistical and machine learning algorithms K-means clustering Association rules Boosting trees CHAID Cluster analysis Feature selection Independent components analysis Kohonen Networks (SOFM) Neural networks Social network analysis (SNA) Random forests Mars regression splines Linear and logistic regression Naïve Bayesian classifiers Optimal binning Partial least squares Response Optimization Root cause analysis Support vector machines Natural language processing This is just a sample. There are hundreds of algorithms, variations, and combinations.

  28. The predictive analytics process must be continuous to insure effectiveness Business goal

  29. The right data and right talent are the key to predictive analytics success Source: August 8, 2012, “The State Of Customer Analytics 2012” Forrester report

  30. Real-time predictive is the next big differentiation opportunity in customer engagement

  31. Predictive analytics has its limits. Predictive analytics

  32. Predictive analytics has limits There are lots of stock price data, but causative data is elusive. Note: Red line is AAPL Apple stock price; blue line is RIMM Research In Motion stock price.

  33. Predictive analytics has limits Can a butterfly flapping its wings in Asia drastically alter the weather in the Gulf Of Mexico?

  34. Predictive analytics has limits There have only been 56 presidential elections and 44 presidents.

  35. What do great predictive analytics use cases have in common? Evidence-based methods don’t exist or are sub-optimal. Relevant data is available. The environment changes with moderate frequency. The business outcome is significant.

  36. Predictive analytics is hard to do

  37. Big data reinvigorates the use of predictive analytics to achieve business outcomes Big data means more potentially causative variables. Big data means more experience data for training algorithms.

  38. Big data predictive analytics solutions range from coding tools to specific business solutions. Tools

  39. General purpose enterprise big data predictive analytics tools must have an extensive feature set Architecture Data Discovery Evaluation and optimization Deployment User tools Integration, solutions, standards, and extensibility Innovation

  40. Data import and pre-processing R is an open source programming language that can be used for predictive analytics. R User-defined functions Internet API interface XML parsing Grant awards to homeless veterans FY09 Data: Data.gov Analysis: Drew Conway Iterative data processing

  41. Can you prevent Melissa from switching to a competitive mobile plan? Churn

  42. Prepare data from different sources. Churn

  43. Find the predictive variables. Churn

  44. Find a predictive model. Churn

  45. Evaluate the effectiveness of the model. Churn

  46. How can you provide Melissa with nearly perfect song recommendation? Million Song Dataset

  47. Prepare data from 48 million songs listened to by 1.2 million users. MSD

  48. Create a bipartite graph to find out what bands users like. MSD

  49. These are all the users who listen to Aerosmith. MSD

  50. Find communities of listeners. MSD

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