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The First Step in Information Management
www.firstsanfranciscopartners.com
Produced	by:
MONTHLY SERIES
Brought	to	you	in	p...
Polling	Questions
§ What type	of	statistical	analyses	do	you	use	or	plan	to	use	(can	choose	multiple	answers)?
− Descripti...
Polling	Questions
§ What type	of	statistical	analyses	do	you	use	or	plan	to	use	(can	choose	multiple	answers)?
− Descripti...
Topics	For	Today’s	Webinar
pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
§ Overview	of	statist...
The	Process	of	Statistical	Analysis
pg 5© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Form	
Hypoth...
Step	1:	Forming	a	Hypothesis
§ In	statistical	analysis,	we	have	two	hypotheses:
− Null	hypothesis:	Claims	that	any	irregul...
Step	2:	Identifying	Appropriate	Sources
§ Remember,	you	don’t	need	Big	Data	for	every	decision!
§ Sometimes,	knowing	what	...
Step	3:	Proving/Disproving	the	Hypothesis
§ Establish	a	confidence	level	prior	to	analysis.
§ Confidence	levels:
1. Determ...
Step	3:	Proving/Disproving	the	Hypothesis
pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Traini...
www.firstsanfranciscopartners.com
Types	of	Data	Analysis
Types	of	Data	Analysis
pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Predictive PrescriptiveD...
§ Though	the	most	simple	type,	it	is	used	most	
often.
§ Two	types	of	descriptive	analysis:
1. Measures	of	central	tendenc...
www.firstsanfranciscopartners.com
AnalysisPredictive
§ Some	mistake	predictive	analysis	to	have	exclusive	relevance	to	predicting	
future events.	
− However,	in	cases	such	as	...
Forecasting
§ Forecasting:
− Moving	average	technique:	use	the	
mean	of	prior	periods	to	predict	the	
next
§ The	mean	of	p...
Simulation
§ Simulation
− Queuing	models:	used	to	predict	wait	time	and	queue	length
§ Results	can	be	used	to	create	staff...
Queuing	Model	Example
pg 17© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Scenario	1 Scenario	2
Pre...
Monte	Carlo	Simulation	Example
pg 18© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Predictive
Regression
§ Regression	− generally	speaking,	used
to	understand	the	correlation	of	
independent	and	dependent	variables
p...
Classification	&	Clustering
§ Classification:	used	to	assign	objects	to	
one	of	several	categories
− Sentiment	analysis	of...
www.firstsanfranciscopartners.com
AnalysisPrescriptive
§ Decisions	can	be	formulated	from	descriptive	and	predictive	analysis
− If	I	need	to	cut	a	product	and	I	know	that	produc...
Linear	Programming	Example
pg 23© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Product	A Product	B ...
Comparing	the	Three	Types	of	Data	Analytics
§ Descriptive	analysis	is	most	common.
− Best	practice	to	perform	descriptive	...
Key	Takeaways	and	Suggested	Resources
§ Gaining	meaningful	insights	from	data	requires	planning,	technical	awareness	and	c...
Closing	Q&A
pg 26© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Descriptive
Predictive
Prescriptive...
pg 27
Thank	you!
See	you	Thursday,	April	6		for	our	next	DIA	webinar,
Building	a	Flexible	and	Scalable	Analytics	Architect...
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DI&A Slides: Descriptive, Prescriptive, and Predictive Analytics

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Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.

This webinar will compare and contrast these different data analysis activities and cover:

- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each

Published in: Business
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DI&A Slides: Descriptive, Prescriptive, and Predictive Analytics

  1. 1. The First Step in Information Management www.firstsanfranciscopartners.com Produced by: MONTHLY SERIES Brought to you in partnership with: March 2, 2017 Descriptive, Prescriptive and Predictive Analytics
  2. 2. Polling Questions § What type of statistical analyses do you use or plan to use (can choose multiple answers)? − Descriptive − Predictive − Prescriptive − I don’t use any of these − I don’t know the difference between these pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  3. 3. Polling Questions § What type of statistical analyses do you use or plan to use (can choose multiple answers)? − Descriptive − Predictive − Prescriptive − I don’t use any of these − I don’t know the difference between these § How frequently do you use statistical analyses in your work? − I don’t currently do any type of statistical analysis − Less than once a week − Once or a few times a week − At least once a day pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  4. 4. Topics For Today’s Webinar pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com § Overview of statistical analysis process − Forming a hypothesis − Identifying appropriate sources − Proving/Disproving the hypothesis § Types of data analysis − Descriptive data analytics − Predictive data analytics − Prescriptive data analytics § How these types compare within the analytic environment § Key takeaways and suggested resources Combine? Descriptive Predictive Prescriptive
  5. 5. The Process of Statistical Analysis pg 5© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Form Hypotheses • Null: Nothing special • Alternative: Something unique, an actionable finding, etc. Identify Data Source • Don’t go overboard! • Collect your own, OR • Use secondary data Prove/Disprove Hypothesis • Is Type I or Type II error worse? • Choose confidence level • Reject/not reject null When we have resource constraints, Statistical Analysis enables us to make quantitative inferences based on an amount of information we can analyze (a sample).
  6. 6. Step 1: Forming a Hypothesis § In statistical analysis, we have two hypotheses: − Null hypothesis: Claims that any irregularities in the sample are due to chance − Alternative hypothesis: Claims that irregularities in the sample are due to non-random causes (and would therefore reflect the population) § What are you really looking to discover/prove? − Experiment 1: § Null: There is no difference in the amount sold when comparing salespeople who did and did not receive training. § Alternative: There is a difference in the amount sold when comparing salespeople who did and did not receive training. − Experiment 2: § Null: The salespeople who received training do not sell more on average than the salespeople who did not receive training. § Alternative: Salespeople who received the training sell more on average than those who did not receive the training. pg 6© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Step 1
  7. 7. Step 2: Identifying Appropriate Sources § Remember, you don’t need Big Data for every decision! § Sometimes, knowing what data you don’t need is just as important as knowing what you do need. Keep your end decision in mind. § Potential sources of data: − Primary data − collect new data § Who to include: Random sample, stratified random sample, etc. § How many to include: Sample size calculators online (free) § Determine the level of measurement needed for your desired analysis: categorical, ordinal, interval, rational § As necessary, design a control group − Secondary data − utilize existing data § Census records, syndicated data, government data, etc. § Consider your data needs, data cleanliness, cost, etc., when determining appropriate sources. pg 7© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Step 2
  8. 8. Step 3: Proving/Disproving the Hypothesis § Establish a confidence level prior to analysis. § Confidence levels: 1. Determine how significant a difference/irregularity must be for you to prove/disprove your alternative hypothesis. 2. Determine how confident you can be in your decision. § Even with a high confidence level, you aren’t always right: − Type I error: You reject the null hypothesis but shouldn’t have. − Type II error: You do not reject the null hypothesis but should have. − How to decrease the likelihood of these errors: change the confidence level, increase sample size (be aware of effect size), etc. § Determine which type of error is more detrimental to your investigation and set up your study accordingly. pg 8© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Step 3
  9. 9. Step 3: Proving/Disproving the Hypothesis pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Training N Mean Std. Deviation Std. Error Mean No training 74 102.643 9.95482 1.15722 Training 74 106.3889 9.83445 1.14323 QPctQ3 Sig. (2- tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference 95% Confidence Interval of the Difference Lower Upper 0.029 0.865 -2.303 146 0.023 -3.74595 1.6267 -6.96086 -0.53103 -2.303 145.978 0.023 -3.74595 1.6267 -6.96087 -0.53102 Levene's Test for Equality of Variances t-test for Equality of Means F Sig. § Confidence level = 95% § Alpha = 0.05 100 102 104 106 108 No training Training Percent of 3rd Quarter Quota Sold by Trained vs. Untrained Salespeople
  10. 10. www.firstsanfranciscopartners.com Types of Data Analysis
  11. 11. Types of Data Analysis pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Predictive PrescriptiveDescriptive • Aims to help uncover valuable insight from the data being analyzed • Answers the question “What happened?” • Helps forecast behavior of people and markets • Answers the question “What could happen?” • Suggests conclusions or actions that may be taken based on the analysis • Answers the question “What should be done?”
  12. 12. § Though the most simple type, it is used most often. § Two types of descriptive analysis: 1. Measures of central tendency (tells us about the middle) § Mean − the average § Median − the midpoint of the responses § Mode − the response with the highest frequency 2. Measures of dispersion § Range − the min, the max and the distance between the two § Variance − the average degree to which each of the points differ from the mean § Standard Deviation − the most common/standard way of expressing the spread of data pg 12© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Customer_ID Items Purchased Amount Spent 29304 1 1.09$ 28308 3 44.43$ 19962 21 218.58$ 30281 1 73.02$ 6.5 2 1 0 1 2 3 4 5 6 7 Mean Median Mode Mean, Median and Mode Amounts of Items Purchased Descriptive Data Analytics
  13. 13. www.firstsanfranciscopartners.com AnalysisPredictive
  14. 14. § Some mistake predictive analysis to have exclusive relevance to predicting future events. − However, in cases such as sentiment analysis, existing data (e.g., the text of a tweet) is used to predict non-existent data (whether the tweet is positive or negative). § Several of the models that can be used for predictive analysis are: − Forecasting − Simulation − Regression − Classification − Clustering pg 14© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Predictive Data Analytics
  15. 15. Forecasting § Forecasting: − Moving average technique: use the mean of prior periods to predict the next § The mean of periods 1−4 = period 5 § The mean of periods 2−5 = period 6 − Exponential smoothing technique: similar, but more recent data points are weighted more heavily due to relevance − Regression techniques § Use caution in forecasting – The larger the forecasted time period, the less accuracy there is in the projections. pg 15© 2017 First San Francisco Partners www.firstsanfranciscopartners.com $- $5,000.00 $10,000.00 $15,000.00 $20,000.00 $25,000.00 2006 2008 2010 2012 2014 2016 2018 2020 2022 Net Income of Store C Projected 2017-2020 Predictive
  16. 16. Simulation § Simulation − Queuing models: used to predict wait time and queue length § Results can be used to create staff schedules in a way that reduces inefficiencies, etc. − Discrete event model: used in special situations when queuing cannot be used § Results can be used to identify bottlenecks, etc. − Monte Carlo simulations: used to identify probable outcomes of a scenario based on many possible outcomes (uses random number generation and many iterations of the scenario). § Results can be used to predict the likelihood of profitability within the first two years, etc. pg 16© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Predictive
  17. 17. Queuing Model Example pg 17© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Scenario 1 Scenario 2 Predictive
  18. 18. Monte Carlo Simulation Example pg 18© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Predictive
  19. 19. Regression § Regression − generally speaking, used to understand the correlation of independent and dependent variables pg 19© 2017 First San Francisco Partners www.firstsanfranciscopartners.com § Types of regression models: − Logistic: used for categorical variables (i.e., will customers shop at your store or a competitor?) − Linear: used to identify a linear relationship between the dependent variable and at least one independent variables (i.e., daily store revenue predicted by the number of customers entering the store) − Step-wise: used to identify a relationship between dependent/independent variables. This is done by adding/removing variables based on how those variables impact the overall strength of the model. Predictive
  20. 20. Classification & Clustering § Classification: used to assign objects to one of several categories − Sentiment analysis of social media postings § Clustering: another method of forming groups − Intragroup differences are minimized − Intergroup differences are maximized − Commonly used to create and better understand customer groups pg 20© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Predictive
  21. 21. www.firstsanfranciscopartners.com AnalysisPrescriptive
  22. 22. § Decisions can be formulated from descriptive and predictive analysis − If I need to cut a product and I know that product C is least preferred and least profitable, I will cut product C. § However, prescriptive analytics explicitly tell you the decisions that should be made. This can be done using a variety of techniques: − Linear programming − Integer programming − Mixed integer programming − Nonlinear programming pg 22© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Prescriptive Data Analytics
  23. 23. Linear Programming Example pg 23© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Product A Product B Product C Product D Product E Quantity to Order Profit per Unit 5$ 3$ 20$ 50$ 200$ Total Profit -$ Product A Product B Product C Product D Product E Used Available Storage Space 0.05 0.5 1 5 10 1000 Selling Effort 0.25 5 0.5 2 7 500 Minimum Order 100 15 20 60 5 Product A Product B Product C Product D Product E Quantity to Order 100 15 490 60 5 Profit per Unit 5$ 3$ 20$ 50$ 200$ Total Profit 14,345.00$ Product A Product B Product C Product D Product E Used Available Storage Space 0.05 0.5 1 5 10 852.5 1000 Selling Effort 0.25 5 0.5 2 7 500 500 Minimum Order 100 15 20 60 5 Solution: Prescriptive
  24. 24. Comparing the Three Types of Data Analytics § Descriptive analysis is most common. − Best practice to perform descriptive analyses prior to prescriptive/predictive § Understand that distribution, variance, skew, etc., may exclude certain models § How to know which type of analysis to pursue: − How much time do you have? − What resources are available to you? pg 24© 2017 First San Francisco Partners www.firstsanfranciscopartners.com − How accurate is your data? How accurate do you need the model/analysis to be? − How popular/accepted is the model you are considering? § Don’t subscribe to “that’s how we’ve always done it,” but remember to use a model that stakeholders will accept.
  25. 25. Key Takeaways and Suggested Resources § Gaining meaningful insights from data requires planning, technical awareness and consistency. § Statistical analysis isn’t a replacement for your own logic (don’t go on statistical autopilot). § Utilize available resources (blogs, podcasts, articles, webinars and online courses) to learn more. − Look for APPLIED statistics topics § Big data is not always required. § Basic understanding of the statistical analysis process goes a long way! pg 25© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Podcast: Not So Standard Deviations https://soundcloud.com/nssd-podcast Guide: When Predictive Models Fail searchdatamanagement.techtarget.com/ ezine/Business-Information/When- predictive-analytics-models-produce- false-outcomes Book: Statistics in Plain English Timothy C. Urdan
  26. 26. Closing Q&A pg 26© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Descriptive Predictive Prescriptive ?
  27. 27. pg 27 Thank you! See you Thursday, April 6 for our next DIA webinar, Building a Flexible and Scalable Analytics Architecture Catch our webinar recap next week here: firstsanfranciscopartners.com/blog John Ladley @jladley john@firstsanfranciscopartners.com Kelle O’Neal @kellezoneal kelle@firstsanfranciscopartners.com © 2016 First San Francisco Partners www.firstsanfranciscopartners.com

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