The document discusses predictive analytics and data-driven decision making. It notes that predictive modeling is necessary to make sense of large data sources, as managerial intuition alone is insufficient. Statistical and machine learning techniques can be used to develop predictive models from vast amounts of structured and unstructured data. Once developed, predictive models can be implemented on predictive platforms and used to power decision services and dashboards to guide business decisions. Revenue opportunities exist throughout the development and use of predictive analytics solutions.

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2. Predictions are very difficult, certainly if they are about the
future!
Niels Bohr
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3. data - explosion
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4. data - explosion
example:
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5. data - explosion
new applications enhance applications
data - mining dashboards
predictive analytics
network information
scada massive data sources smart devices data
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6. data - explosion
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7. analytics maturity
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8. analytics maturity
•Plan based on “past” data
•Model future expectations
Predicting •Decision suggested
•Limited or big data sets
•Machine learnng
•Plan based on “past” data
•Visualization of plans
Planning •No decision forced
•Limited data sets
•Human intuition
•Report on the past
Reporting •Visualization of past data
•No decision forced
•Limited data sets
•Human intuition
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9. how to achieve this?
initiate modeling implement use
company
decision suite
the world
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10. initiate modeling implement use
In a data explosion context:
We do not know which inputs relate to an output of interest
We do not know how these inputs relate to an output of interest
Managerial intuition only fails!
Statistical modeling = statistical assumptions + machine
learning
Statistical assumptions are user driven
Machine learning is data driven
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11. initiate modeling implement use
•History of modeling techniques:
•parametric models (white-box)
•semi-parametric models (gray-box)
•non-parametric models (black-box)
• stat. expansions, neural nets, etc.
• extreme flexibility of certain models
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12. initiate modeling implement use
•Modeling techniques:
•Variable selection mechanisms - stability gain in models
•Statistical expansions - information gain - accuracy increase
•Non-temporal expansions
•Spatio-temporal expansions
•Information geometry, Information criteria
•Non linear classification
•Probability density function
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13. initiate modeling implement use
•Applications
•Pattern detection, Event detection
•Forecasting, conditional clustering
•Time-series analysis
•On-line factor analysis
•Model adequacy checks
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14. initiate modeling implement use
Future portential with SAP PA + 2BridgZ
first wave of predictive analytics
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competitors PA
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PP icti v
SA p red
No predictive analytics
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15. how to achieve this?
initiate modeling implement use
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16. Predictive model in memory computing
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17. data sources platform decision services
predictive platform
decision suite
on premise
predictive suite
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on premise

18. data sources platform decision services
predictive platform
decision suite
in the cloud
predictive suite
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on premise

19. predictive platform
decision suite
predictive suite
revenue sources
data sources platform decision services
model development platform installation decision dashboards
model optimization platform in the cloud decision services
predictive services mobile services
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on premise

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