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Forrester Research — Big Data Predictive Analytics 101 webinar (September 2012)

57 slides from Forrester Research's conference presentation, Sep 7, 2012

  1. Slide 1: 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

  2. Slide 2: Outlook

    Outlook

  3. Slide 3: Business intelligence is the top business app

    Business intelligence is the top business app © 2012 Forrester Research, Inc. Reproduction Prohibited 3

  4. Slide 4: Real-time analytics is rising fast

    Real-time analytics is rising fast © 2012 Forrester Research, Inc. Reproduction Prohibited 4

  5. Slide 5: Data continues to grow

    Data continues to grow © 2012 Forrester Research, Inc. Reproduction Prohibited 5

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

    The past three years have shown an increased adoption of predictive analytics © 2012 Forrester Research, Inc. Reproduction Prohibited 6

  7. Slide 7: “Knowledge is power.”

    “Knowledge is power.” Francis Bacon (1561–1626) Francis Bacon described a rational procedure to establish causation between phenomena based on induction. © 2012 Forrester Research, Inc. Reproduction Prohibited 7

  8. Slide 8: “Big data can lead to an explosion of knowledge . . .”

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

  9. Slide 9: 1B smartphone and tablet users by 2016

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

  10. Slide 10: 25B devices connected to the Internet by 2015

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

  11. Slide 11: More people using more technology means more big data

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

  12. Slide 12: What exactly is big data?

    What exactly is big data?

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

    Volume, velocity, and variety are measures of big data © 2012 Forrester Research, Inc. Reproduction Prohibited 13

  14. Slide 14: It’s relative.

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

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

    Big data activities are as important as the three V’s measures © 2012 Forrester Research, Inc. Reproduction Prohibited 15

  16. Slide 16: What’s your big data score?

    What’s your big data score? Activities Store Process Access Points Volume 5 5 3 13 Measures Velocity 3 3 0 3 Variety 3 3 0 6 Your big data score: 22 5 = Not required or handled perfectly 3 = Handled but could be improved 1 = Handled poorly but with frequent negative business impact 0 = Current or fu

  17. Slide 17: What’s your big data score mean?

    What’s your big data score mean? Big data Meaning Description score 0–15 Poor Your organization’s inability to handle your big data requirements threatens the business. 16–30 Struggling Your organization is probably handling one measure of big data such as volume well, but ignoring others. 31–40 Win

  18. Slide 18: Big data

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

  19. Slide 19: Transactional data is most popular for big data

    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 © 2012 Forrester Research, Inc. Rep

  20. Slide 20: Why do Forrester clients consider or implement big data?

    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?” Data volume 75% Analysis-driven requirements (Big Dat

  21. Slide 21: Most Forrester clients are using commercial technology for big data

    Most Forrester clients are using commercial technology for big data “What technology do you use for big data applications?” (multiple responses accepted) Commercial source Big Data tools 47% Open source Big Data technology (Hadoop, MapReduce, Cassandra, and the other Apache 37% open source specs) Do

  22. Slide 22: Predictive analytics

    Predictive analytics Predictive analytics can find meaning in big data.

  23. Slide 23: Examples of how organizations use predictive analytics today

    Just a few examples of how organizations use predictive analytics today © 2012 Forrester Research, Inc. Reproduction Prohibited 23

  24. Slide 24: Predictive analytics finds a model that acts like a formula

    Predictive analytics finds a model that acts like a formula to find the answer © 2012 Forrester Research, Inc. Reproduction Prohibited 24

  25. Slide 25: Predictive models can be represented in many different ways

    Predictive models can be represented in many different ways depending on the technique used Formula Decision trees Code Combination of any of the above © 2012 Forrester Research, Inc. Reproduction Prohibited 25

  26. Slide 26: Big data comes in many varieties

    Big data comes in many varieties © 2012 Forrester Research, Inc. Reproduction Prohibited 26

  27. Slide 27: Predictive analysis is powered by statistical and machine learning algorithms

    Predictive analysis is powered by statistical and machine learning algorithms K-means clustering Random forests Association rules Mars regression splines Boosting trees Linear and logistic regression CHAID Naïve Bayesian classifiers Cluster analysis Optimal binning Feature selection Partial least sq

  28. Slide 28: The predictive analytics process must be continuous

    The predictive analytics process must be continuous to insure effectiveness Business goal © 2012 Forrester Research, Inc. Reproduction Prohibited 28

  29. Slide 29: The right data and right talent are key to success

    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 © 2012 Forrester Research, Inc. Reproduction Prohibited 29

  30. Slide 30: Real-time predictive is the next big differentiation opportunity

    Real-time predictive is the next big differentiation opportunity in customer engagement © 2012 Forrester Research, Inc. Reproduction Prohibited 30

  31. Slide 31: Predictive analytics has its limits

    Predictive analytics Predictive analytics has its limits.

  32. Slide 32: Predictive analytics has limits

    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. © 2012 Forrester Research, Inc. Reproduction Prohibited 32

  33. Slide 33: Predictive analytics has limits

    Predictive analytics has limits Can a butterfly flapping its wings in Asia drastically alter the weather in the Gulf Of Mexico? © 2012 Forrester Research, Inc. Reproduction Prohibited 33

  34. Slide 34: Predictive analytics has limits

    Predictive analytics has limits There have only been 56 presidential elections and 44 presidents. © 2012 Forrester Research, Inc. Reproduction Prohibited 34

  35. Slide 35: What do great predictive analytics use cases have in common?

    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. © 2012 Forrester Research, Inc. Reproduction Prohibited 35

  36. Slide 36: Predictive analytics is hard to do

    Predictive analytics is hard to do © 2012 Forrester Research, Inc. Reproduction Prohibited 36

  37. Slide 37: Big data reinvigorates the use of predictive analytics

    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. © 2012 Forrester Research, Inc. Reproduction Prohibited 37

  38. Slide 38: Tools

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

  39. Slide 39: General purpose enterprise big data predictive analytics tools

    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 © 2012 Forrester Research, Inc. Reproduction Prohibited 39

  40. Slide 40: R is an open source programming language for predictive analytics

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

  41. Slide 41: Churn: Can you prevent Melissa from switching plans?

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

  42. Slide 42: Churn: Prepare data from different sources

    Churn Prepare data from different sources.

  43. Slide 43: Churn: Find the predictive variables

    Churn Find the predictive variables.

  44. Slide 44: Churn: Find a predictive model

    Churn Find a predictive model.

  45. Slide 45: Churn: Evaluate the effectiveness of the model

    Churn Evaluate the effectiveness of the model.

  46. Slide 46: Million Song Dataset

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

  47. Slide 47: MSD: Prepare data from 48 million songs

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

  48. Slide 48: MSD: Create a bipartite graph

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

  49. Slide 49: MSD: Users who listen to Aerosmith

    MSD These are all the users who listen to Aerosmith.

  50. Slide 50: MSD: Find communities of listeners

    MSD Find communities of listeners.

  51. Slide 51: MSD: Sample recommendations for a specific user

    MSD View a sample list of recommendations for a specific user sorted by support and confidence.

  52. Slide 52: Big data predictive analytics will make you smarter.

    Big data predictive analytics will make you smarter.

  53. Slide 53: “Big data can lead to an explosion of knowledge...”

    “Big data can lead to an explosion of knowledge that firms can use to make smarter decisions and create differentiated customer experiences.” — Big Data Predictive Analytics, Forrester Research

  54. Slide 54: Francis Bacon (1561–1626)

    “Knowledge is power profit.” Francis Bacon (1561-1626) FRANCIS BACON From a painting

  55. Slide 55: Forrester Wave: Big Data Predictive Analytics Solutions planned Q4 2012

    Forrester Wave™: Big Data Predictive Analytics Solutions planned publication Q4 2012 Forrester methodology limited this Forrester Wave to ten vendors. Many vendor solutions and combinations of solutions exist for a variety of use cases. Publication of the Forrester Wave is expected in Q4 2012. Sched

  56. Slide 56: Thank you

    Thank you Mike Gualtieri [email protected] Twitter: @mgualtieri

  57. Slide 57: Big data predictive analytics solutions range from coding tools to business solutions

    Big data predictive analytics solutions range from coding tools to specific business solutions Alpine Data Labs Zementis Alteryx Google Prediction Pentaho Angoss API Matlab EMC R Rapid – I Teradata KNIME Opera Solutions FICO SAS Revolution Pegasystems IBM Analytics Oracle Cetus Microsoft KXEN Pitney

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