LSUS - MBA 741 - Amin Saleh - Data-Driven Decision-
Making
Using facts, metrics, and data to guide strategic business decisions that align with
your goals, objectives, and initiatives. Asking the right questions.
M1: Define data-driven decision making
(analytics)
It is the science of applying a structured method to solve a business problem using
data and analysis to drive impact.
A collection of facts used to identify patterns, draw conclusions, make predictions,
M1: Define data
and make decisions.
M1: Distinguish between data science and Toward Insight:
decision science: DATA SCIENCE This is the technical track, designed to derive insights from data.
Toward Impact:
M1: Distinguish between data science and This is the business track, designed to align stakeholders so that the valuable
decision science: DECISION SCIENCE insights produced using the data science track can be inserted into the
decision-making process and converted into action.
Data analysts focus on business analytics and perform tasks such as:
M1: Describe the tasks an analyst may need
-Accessing, Transforming, and Manipulating (MySQL, Microsoft Excel)
to perform and the software they might use
-Statistical Analyses (R, Python)
-Visualizing (Tableau, Power BI Desktop)
, Quantitative—continuous values
– Mean, Median, Variance, Standard Deviation):
Interval Scale (Common Arithmetic Operations -- Numerical ranking for how service
was today, % supervisors assign to good performers %0 bad, 100% good, Low
temperature = Bad attitude and high temperature = Good attitude)
M1: Describe and give examples of metric
-Ratio Scale (All Arithmetic Operations -- Amount purchased, Salesperson Sales
volume, Likelihood of performing some act: 0% = No Likelihood to 100% = Certainty,
Number of stores visited, Time spent viewing a particular web page, Number of
web pages viewed)
Qualitative—discrete values:
Ordinal – ranking scale with counting and ordering (Frequency, Mode, Median,
Range)
M1: Describe and give examples of non-
EX: Dissatisfied to Delighted or HS Diploma up to Graduate Degree)
metric data
Nominal Scale (absolute value) with only counting (Frequency, Mode
EX: Yes-No, Female-Male, Buy-Did Not Buy, Postal Code )
M1: Identify the three characteristics of big Volume, Variety, Velocity
data
M1: Identify the characteristics of valuable Relevance, Completeness, Accuracy, Timeliness
data
M1: Describe the components of a Both financial and nonfinancial metrics matter. Looking forward, backward, internally,
balanced scorecard and externally
Profit
Net Present Value (NPV)
M1: Identify Financial Metrics
Internal Rate of Return (IRR)
Payback
Brand Awareness
Product Trials
Churn
M1: Identify Non-Financial Metrics
Customer Satisfaction (CSAT)
Customer Lifetime Value (CLTV)
Conversions
Customer Behavior -- (Frequency of Firm Desired Behavior, Strength of Firm Desired
Behavior, Behavioral Intentions) and
M1: Identify Customer Metrics
Customer Evaluations -- (of Service Provider, of Service Experience, of Goods, of
Firm, of Self)
1) Business question
2) Analysis Plan
M2: Name the steps in the BADIR process 3)Data Collection
4) Insights
5)Recommendation
1) Reduction of iterations
2)Contributions with actionable recommendations
M2: Identify the advantages of taking time 3)Recognition as a valued partner
to establish the Business Question 4) Solutions originate from discussion not data
5)Quality of decision is proportional to the time invested in fully exploring what
the problem is
M2: Name Information Seeking Questions Who? What? When? Where? Why? How?
Making
Using facts, metrics, and data to guide strategic business decisions that align with
your goals, objectives, and initiatives. Asking the right questions.
M1: Define data-driven decision making
(analytics)
It is the science of applying a structured method to solve a business problem using
data and analysis to drive impact.
A collection of facts used to identify patterns, draw conclusions, make predictions,
M1: Define data
and make decisions.
M1: Distinguish between data science and Toward Insight:
decision science: DATA SCIENCE This is the technical track, designed to derive insights from data.
Toward Impact:
M1: Distinguish between data science and This is the business track, designed to align stakeholders so that the valuable
decision science: DECISION SCIENCE insights produced using the data science track can be inserted into the
decision-making process and converted into action.
Data analysts focus on business analytics and perform tasks such as:
M1: Describe the tasks an analyst may need
-Accessing, Transforming, and Manipulating (MySQL, Microsoft Excel)
to perform and the software they might use
-Statistical Analyses (R, Python)
-Visualizing (Tableau, Power BI Desktop)
, Quantitative—continuous values
– Mean, Median, Variance, Standard Deviation):
Interval Scale (Common Arithmetic Operations -- Numerical ranking for how service
was today, % supervisors assign to good performers %0 bad, 100% good, Low
temperature = Bad attitude and high temperature = Good attitude)
M1: Describe and give examples of metric
-Ratio Scale (All Arithmetic Operations -- Amount purchased, Salesperson Sales
volume, Likelihood of performing some act: 0% = No Likelihood to 100% = Certainty,
Number of stores visited, Time spent viewing a particular web page, Number of
web pages viewed)
Qualitative—discrete values:
Ordinal – ranking scale with counting and ordering (Frequency, Mode, Median,
Range)
M1: Describe and give examples of non-
EX: Dissatisfied to Delighted or HS Diploma up to Graduate Degree)
metric data
Nominal Scale (absolute value) with only counting (Frequency, Mode
EX: Yes-No, Female-Male, Buy-Did Not Buy, Postal Code )
M1: Identify the three characteristics of big Volume, Variety, Velocity
data
M1: Identify the characteristics of valuable Relevance, Completeness, Accuracy, Timeliness
data
M1: Describe the components of a Both financial and nonfinancial metrics matter. Looking forward, backward, internally,
balanced scorecard and externally
Profit
Net Present Value (NPV)
M1: Identify Financial Metrics
Internal Rate of Return (IRR)
Payback
Brand Awareness
Product Trials
Churn
M1: Identify Non-Financial Metrics
Customer Satisfaction (CSAT)
Customer Lifetime Value (CLTV)
Conversions
Customer Behavior -- (Frequency of Firm Desired Behavior, Strength of Firm Desired
Behavior, Behavioral Intentions) and
M1: Identify Customer Metrics
Customer Evaluations -- (of Service Provider, of Service Experience, of Goods, of
Firm, of Self)
1) Business question
2) Analysis Plan
M2: Name the steps in the BADIR process 3)Data Collection
4) Insights
5)Recommendation
1) Reduction of iterations
2)Contributions with actionable recommendations
M2: Identify the advantages of taking time 3)Recognition as a valued partner
to establish the Business Question 4) Solutions originate from discussion not data
5)Quality of decision is proportional to the time invested in fully exploring what
the problem is
M2: Name Information Seeking Questions Who? What? When? Where? Why? How?
