MGT 6203 Business Analytics & Data
Management
Aggregated data - ANS-individual data points that have been combined into subtotals,
such as counts, sums, or averages
Analytics Mindset - ANS-willingness and ability to specify which business questions
need to be addressed, find and extract pertinent data that might address those
questions, analyze those data, and then report the results to decision-makers.
-Understand the questions that their business and its decision-makers are asking
-Understand the nature and quality of the business's data
ANOVA test - ANS-Data Type: numerical
# of Groups: three or more unrelated groups
Purpose: to determine if significant differences exist among three or more groups
Benford's Law (descriptive) - ANS-the principle that in any large, randomly produced set
of numbers, there is an expected distribution of the first, or leading, digit. A higher
percentage of numbers in a population of numbers with 1 rather than any other digit,
followed by those that begin with 2, then 3, and so on.
Big Data - ANS-data sets that are too large and complex for businesses' centralized
systems to capture, store, manage, and analyze
Characterized by the four V's:
1. Volume: sheer amount of data
2. Variety: different forms of data
3. Velocity: the speed that the data is being generated or the rate that the data is being
analyzed
4. Veracity: the underlying truthfulness, accuracy, and trustworthiness
Business Analyst - ANS-a data specialist who curates and uses data to help an
organization make effective business decisions
Business Process - ANS-a coordinated, standardized set of activities conducted by both
people and equipment to accomplish a specific business task
Business Value - ANS-all items, events, interactions that determine a company's
financial health
Cash Flow analysis: prescriptive analytics - ANS-prescriptive analytics technique that
evaluates future cash flows for potential investments or expenditures, typically using net
present value (NPV) or internal rate of return (IRR)
, Categorical data - ANS-data that are categorized into groups that are represented either
by words or by non-meaningful numerical data
1. Nominal Data: categorical data that cannot be ranked or averaged
To summarize:
-Counting & Grouping
-Proportion
2. Ordinal Data: categorical data with a natural order that allows them to be ranked and
sorted (ex. Letter grades)
To summarize:
-Counting & Grouping
-Proportion
-Ranking (or sorting)
Centralized Relational Databases - ANS-1. Tables: are data organized into a set of
columns and rows
2. Fields (attributes): are the columns that contain descriptive characteristics about the
observations in the table
3. Records: are the rows, with each observation corresponding to a unique instance of
what is being described in the table
4. Primary Key: any field that functions as a unique identifier in a table
5. Foreign Key: creates relationships between two tables so that database users can
look up details of the observation based on the primary key/foreign key relationship
Chi-square test - ANS-Data Type: categorical
# of Groups: two or more groups
Purpose: to compare the distributions of two different data sets and to compare
observed versus expected data
Classification: predictive analytics - ANS-technique for discrete values: separates or
classifies a sample (or population) into two or more groups of classes
-Market segmentation
-Fraud
-Base rate: the probability of an event occurring based on a related historical avg.
(gauges the reasonableness of prediction)
-Base rate fallacy: occurs when a prediction places too little weight on the base rates
and instead favors different or new information as the basis for the prediction
Cluster Sampling - ANS-Random sampling only from SOME specific groupings of the
population
Confidence Interval - ANS-a range of values estimated from sample data that contains
the true population parameter at a certain level of confidence (typically 90% of 95%)
-Point estimate (+ or -) Margin of error
Management
Aggregated data - ANS-individual data points that have been combined into subtotals,
such as counts, sums, or averages
Analytics Mindset - ANS-willingness and ability to specify which business questions
need to be addressed, find and extract pertinent data that might address those
questions, analyze those data, and then report the results to decision-makers.
-Understand the questions that their business and its decision-makers are asking
-Understand the nature and quality of the business's data
ANOVA test - ANS-Data Type: numerical
# of Groups: three or more unrelated groups
Purpose: to determine if significant differences exist among three or more groups
Benford's Law (descriptive) - ANS-the principle that in any large, randomly produced set
of numbers, there is an expected distribution of the first, or leading, digit. A higher
percentage of numbers in a population of numbers with 1 rather than any other digit,
followed by those that begin with 2, then 3, and so on.
Big Data - ANS-data sets that are too large and complex for businesses' centralized
systems to capture, store, manage, and analyze
Characterized by the four V's:
1. Volume: sheer amount of data
2. Variety: different forms of data
3. Velocity: the speed that the data is being generated or the rate that the data is being
analyzed
4. Veracity: the underlying truthfulness, accuracy, and trustworthiness
Business Analyst - ANS-a data specialist who curates and uses data to help an
organization make effective business decisions
Business Process - ANS-a coordinated, standardized set of activities conducted by both
people and equipment to accomplish a specific business task
Business Value - ANS-all items, events, interactions that determine a company's
financial health
Cash Flow analysis: prescriptive analytics - ANS-prescriptive analytics technique that
evaluates future cash flows for potential investments or expenditures, typically using net
present value (NPV) or internal rate of return (IRR)
, Categorical data - ANS-data that are categorized into groups that are represented either
by words or by non-meaningful numerical data
1. Nominal Data: categorical data that cannot be ranked or averaged
To summarize:
-Counting & Grouping
-Proportion
2. Ordinal Data: categorical data with a natural order that allows them to be ranked and
sorted (ex. Letter grades)
To summarize:
-Counting & Grouping
-Proportion
-Ranking (or sorting)
Centralized Relational Databases - ANS-1. Tables: are data organized into a set of
columns and rows
2. Fields (attributes): are the columns that contain descriptive characteristics about the
observations in the table
3. Records: are the rows, with each observation corresponding to a unique instance of
what is being described in the table
4. Primary Key: any field that functions as a unique identifier in a table
5. Foreign Key: creates relationships between two tables so that database users can
look up details of the observation based on the primary key/foreign key relationship
Chi-square test - ANS-Data Type: categorical
# of Groups: two or more groups
Purpose: to compare the distributions of two different data sets and to compare
observed versus expected data
Classification: predictive analytics - ANS-technique for discrete values: separates or
classifies a sample (or population) into two or more groups of classes
-Market segmentation
-Fraud
-Base rate: the probability of an event occurring based on a related historical avg.
(gauges the reasonableness of prediction)
-Base rate fallacy: occurs when a prediction places too little weight on the base rates
and instead favors different or new information as the basis for the prediction
Cluster Sampling - ANS-Random sampling only from SOME specific groupings of the
population
Confidence Interval - ANS-a range of values estimated from sample data that contains
the true population parameter at a certain level of confidence (typically 90% of 95%)
-Point estimate (+ or -) Margin of error
