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A PREDICTIVE
ANALYTICS
PRIMER
PREDICTIVE ANALYTICS
Predictive analytics is not magic.!
It is done with a lot of past data.
Predictive analytics encompasses a variety of
statistical techniques from predictive
modelling, machine learning, and data
mining that analyze current and historical facts
to make predictions about future or otherwise
unknown events
Using data from the past to
predict the future is predictive
analytics. There are three
elements on which the quality
of predictive analysis depends.
1.data: Finding quality data from the
past is very difficult – especially one that
can support attribute-based forecasting.
One has to have information of the past
transactions that includes the attributes
at a very granular level. Most
organizations do not have such quality
data to support predictive analytics.
2.Statistics: regression analysis is the
most common tool used for predictive
analytics. There are multiple variants of
regression analysis. This analysis finds
the correlation between a set of
independent variables. So, the quality of
the analysis is based on the variables
identified.
3. Assumptions: a number of assumptions
go into the selection of the variables.
These assumptions could change over
time and models would have to be
updated for accurate predictions. Also,
some variables may not be included in the
analysis and the model may not predict
accurately when that variable becomes a
significant contributing factor.
What are the barrier for this
prediction?
• Poor historical data
• Assumptions should hold good through out the
prediction
• Data Collection through multiple channels
when not streamlined will end up being of no
use
• It is extremely impossible to create a single
warehouse for housing all the data.
Uses of Statistics for prediction:
• Linear Regression
• Non-linear Regression
• Logistic Regression
• Generating Hypothesis
• Correlation
With these fundamentals in mind, here are a few
good questions to ask your analysts:
• Can you tell me something about the source of
data you used in your analysis?
• Are you sure the sample data are representative
of the population?
• Are there any outliers in your data distribution?
How did they affect the results?
• What assumptions are behind your analysis?
• Are there any conditions that would make your
assumptions invalid?
Uses of Statistics for prediction:
• Linear Regression
• Non-linear Regression
• Logistic Regression
• Generating Hypothesis
• Correlation

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Analysis of "A Predictive Analytics Primer" by Tom Davenport

  • 2. PREDICTIVE ANALYTICS Predictive analytics is not magic.! It is done with a lot of past data.
  • 3. Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events
  • 4.
  • 5.
  • 6. Using data from the past to predict the future is predictive analytics. There are three elements on which the quality of predictive analysis depends.
  • 7. 1.data: Finding quality data from the past is very difficult – especially one that can support attribute-based forecasting. One has to have information of the past transactions that includes the attributes at a very granular level. Most organizations do not have such quality data to support predictive analytics.
  • 8. 2.Statistics: regression analysis is the most common tool used for predictive analytics. There are multiple variants of regression analysis. This analysis finds the correlation between a set of independent variables. So, the quality of the analysis is based on the variables identified.
  • 9. 3. Assumptions: a number of assumptions go into the selection of the variables. These assumptions could change over time and models would have to be updated for accurate predictions. Also, some variables may not be included in the analysis and the model may not predict accurately when that variable becomes a significant contributing factor.
  • 10. What are the barrier for this prediction? • Poor historical data • Assumptions should hold good through out the prediction • Data Collection through multiple channels when not streamlined will end up being of no use • It is extremely impossible to create a single warehouse for housing all the data.
  • 11. Uses of Statistics for prediction: • Linear Regression • Non-linear Regression • Logistic Regression • Generating Hypothesis • Correlation
  • 12. With these fundamentals in mind, here are a few good questions to ask your analysts: • Can you tell me something about the source of data you used in your analysis? • Are you sure the sample data are representative of the population? • Are there any outliers in your data distribution? How did they affect the results? • What assumptions are behind your analysis? • Are there any conditions that would make your assumptions invalid?
  • 13. Uses of Statistics for prediction: • Linear Regression • Non-linear Regression • Logistic Regression • Generating Hypothesis • Correlation