DATA DRIVEN DECISION MAKING (C207)
EXAM NOTES LATEST UPDATED Western
Governors University
,C207 Cohort 1:
• I want to buy a new car/ house: In business – where do I find pertinent information
o We do analytics BEFORE then measure our results
o Things that can be measured between vehicles to make best decision: comparable
▪ FACT BASED information for decision to build TRUST
• Makes value of your decisions higher (employees, customers, suppliers)
• Make accurate predictions – to reduce RISK
• Types of Analytics: Ask yourself 2 questions: Am I predicting? Am I optimizing?
o Descriptive:
▪ Past Data Only: “car prices up 2% in past year”
▪ NO prediction: NO optimizing
o Predictive:
▪ Past to predict future: “based on past 10 years, car prices expected to raise 2% next
year”
▪ YES Prediction: NO optimizing
o Prescriptive:
▪ Past to predict future and Optimizing: “By increasing electric charging stations by 7%,
electric car sales are expected to make a 5% increase next year”
▪ YES predicting: YES Optimizing
• Data Quality: Errors in data
o Omission: (find easily by sorting by columns in Excel)
o Out of Range: (find easily by sorting by columns in Excel)
o Outlier is NOT AN ERROR
• Think about what you’re left with. Is it gone?
o Systematic Errors: Error will not fix itself (skew the data)
o Random Error: Error will fix itself (with lots of data)
• Reliable vs. Valid
o Reliable: Consistent and repeatable / a measure of the instrument
▪ Instrument: thermometer – bad reading, do it again
▪ Using reliable instrument = valid data results
o Valid: Measures what is intended to me measured / does test score represent ability?
• BIAS:
o Measurement Bias:
▪ Representative sample: every member of group (population) has equal opportunity to
be selected
• At least 30?
▪ Randomly selected (to eliminate bias)
o Information Bias: ignoring the purpose of the information collected
▪ Questions / information not relevant to the goal of the survey
▪ Record everything, weed out irrelevance later (don’t decide up front)
▪ Non-truthful answers: try to get the bias out of the way up front
, • Anonymous makes more truthful
• BIG DATA:
o WHAT? Both structured and unstructured data in such large volume that it’s difficult to process
using traditional database and software techniques.
▪ Structured: grocery store checkout
▪ Unstructured: don’t fit in rows, columns (social media, email, photos, file notes)
o Where? Servers in a large data warehouse. (3rd party)
o Why? Used to encourage buying behavior. (increase customer base and BUY)
▪ Data-mining used to discover patterns in large data sets.
• 1.04 Rise of Analytics (turning information into insight and developing conclusive fact-based strategies
to gain a competitive edge) An analysis is the key to unlocking the value of big data
o Statistics: interpretation of numerical facts or data through theories of probability
o Analytics: analysis of meaningful patterns in data
▪ Descriptive (diagnostic): depict and describe what is studied- what has already
happened. (used extensively in business)
• Graphical analysis: charts
• Discovery of patterns: numbers
▪ Predictive: Data from past predicts future or the impact of one variable on another
• Models: trend analysis, regression
• Simulations
▪ Prescriptive: includes experimental design to suggest course of action
• Optimization Models
• Simulation
• Decision Analysis: decision trees. Several alternatives with uncertain future
events.
▪ Management Science: the study of model building, optimization, and decision making
EXAM NOTES LATEST UPDATED Western
Governors University
,C207 Cohort 1:
• I want to buy a new car/ house: In business – where do I find pertinent information
o We do analytics BEFORE then measure our results
o Things that can be measured between vehicles to make best decision: comparable
▪ FACT BASED information for decision to build TRUST
• Makes value of your decisions higher (employees, customers, suppliers)
• Make accurate predictions – to reduce RISK
• Types of Analytics: Ask yourself 2 questions: Am I predicting? Am I optimizing?
o Descriptive:
▪ Past Data Only: “car prices up 2% in past year”
▪ NO prediction: NO optimizing
o Predictive:
▪ Past to predict future: “based on past 10 years, car prices expected to raise 2% next
year”
▪ YES Prediction: NO optimizing
o Prescriptive:
▪ Past to predict future and Optimizing: “By increasing electric charging stations by 7%,
electric car sales are expected to make a 5% increase next year”
▪ YES predicting: YES Optimizing
• Data Quality: Errors in data
o Omission: (find easily by sorting by columns in Excel)
o Out of Range: (find easily by sorting by columns in Excel)
o Outlier is NOT AN ERROR
• Think about what you’re left with. Is it gone?
o Systematic Errors: Error will not fix itself (skew the data)
o Random Error: Error will fix itself (with lots of data)
• Reliable vs. Valid
o Reliable: Consistent and repeatable / a measure of the instrument
▪ Instrument: thermometer – bad reading, do it again
▪ Using reliable instrument = valid data results
o Valid: Measures what is intended to me measured / does test score represent ability?
• BIAS:
o Measurement Bias:
▪ Representative sample: every member of group (population) has equal opportunity to
be selected
• At least 30?
▪ Randomly selected (to eliminate bias)
o Information Bias: ignoring the purpose of the information collected
▪ Questions / information not relevant to the goal of the survey
▪ Record everything, weed out irrelevance later (don’t decide up front)
▪ Non-truthful answers: try to get the bias out of the way up front
, • Anonymous makes more truthful
• BIG DATA:
o WHAT? Both structured and unstructured data in such large volume that it’s difficult to process
using traditional database and software techniques.
▪ Structured: grocery store checkout
▪ Unstructured: don’t fit in rows, columns (social media, email, photos, file notes)
o Where? Servers in a large data warehouse. (3rd party)
o Why? Used to encourage buying behavior. (increase customer base and BUY)
▪ Data-mining used to discover patterns in large data sets.
• 1.04 Rise of Analytics (turning information into insight and developing conclusive fact-based strategies
to gain a competitive edge) An analysis is the key to unlocking the value of big data
o Statistics: interpretation of numerical facts or data through theories of probability
o Analytics: analysis of meaningful patterns in data
▪ Descriptive (diagnostic): depict and describe what is studied- what has already
happened. (used extensively in business)
• Graphical analysis: charts
• Discovery of patterns: numbers
▪ Predictive: Data from past predicts future or the impact of one variable on another
• Models: trend analysis, regression
• Simulations
▪ Prescriptive: includes experimental design to suggest course of action
• Optimization Models
• Simulation
• Decision Analysis: decision trees. Several alternatives with uncertain future
events.
▪ Management Science: the study of model building, optimization, and decision making
