Basic business analytics using excel BI348Chapter01

Highline Class,
BI 348
Basic Business Analytics using Excel,
Chapter 01
Intro to Business Analytics
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Topics
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Raw Data Into Useful Information
Business Analytics (Textbook):
Descriptive Analytics
Predictive Analytics
Prescriptive Statistics
Big Data
Steps In Making A Decision
Types Of Decisions
Approaches To Decision Making
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Raw Data Into Useful
Information
• Data Analysis (From Busn 216 and Busn 218):
• Converting raw data into useful information for decision makers

• Statistical Analysis (Busn 210):
• Statistics is the art and science of collecting, analyzing,
presenting and interpreting data to help make informed
decisions.

• Analysis (Merriam-Webster dictionary):
• A careful study of something to learn about its parts, what they
do and how they relate to each other
• An explanation of the nature and meaning of something

• Analytics (Merriam -Webster dictionary):
• Information resulting from systematic analysis of data or
statistics

• Business Analytics (textbook):
• Scientific process of transforming data
into insight for better decisions

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Business Analytics
(Textbook):
• Scientific process of transforming data into
insight for better decisions

• Data driven decision making
• Fact-based decision making
• Scientific process such as: Queries, Linear
Regression, and Optimization

• Business Analytics has three parts:
• Descriptive Analytics
• Describing the past

• Predictive Analytics
• Build models that help predict the unknown future

• Prescriptive Analytics
• Build models to help predict the best course of
action

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Descriptive Analytics
• Set of techniques that describe what has happened in
the past.
• Examples:
• Data Queries
• Like a Filter in Excel or an Access Query

• Reports
• Like an Income Statement, a Regional Report or a PivotTable
with multiple Criteria

• Descriptive Statistics
• Examples: Mean, Median Mode, Standard Deviation, Correlation

• Data Visualization
• Examples; Charts, Tables, Conditional Formatting

• What if Excel models
• Like Income Statement Budget with Assumption Table or a Fixed
Variable Cost Analysis

• Data Dashboards
• Collection of items such as tables, charts and descriptive
statistics that will update as new data arrives

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Predictive Analytics
• Set of techniques that use models
constructed from past data to:
• Predict the future
or
Ascertain impact of one variable on another

• Examples:
• Linear Regression
• Models to help predict one variable based on a
one or more other variables)

• Time Series Analysis & Forecasting

• Using data to make forecasts of unknown future

• Data Mining (not covered in this class)
• Methods to reveal patterns and relationships in
data

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Prescriptive Analytics
• Set of techniques to indicate the best course of
action; what decision to make to optimize
outcome.
• Examples:
• Optimization models
• A mathematical model that gives the best decision,
subject to the situations constraints
• We’ll use the Excel feature called “Solver” which can
tell us things like what number of units to produce to
maximize profit.

• Simulation
• Use Native Excel Functions to create a simulation

• Decision Analysis (not covered in this class)

• Advanced Analytics
• Predictive Analytics and Prescriptive Analytics

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Big Data
• A set of data that cannot be managed, processed or analyzed with
commonly available software in a reasonable amount of time.
• Why do we have so much data now:
• Every time you buy something, the scanner beep at the register
records a lot of data such as price, product name, time, date, location,
sales person and more.
• All our personal devices collect vast amounts of data everyday
• Social media
• E-commerce data
• Almost every click on the internet…

• According to Google: Amount of data generated every 48 hours is
equal to all data created from the beginning of civilization to
2003.
• Business Analytical methods are used more often now because of:
• Vast amount of data
• Improved computational approaches and algorithms to handle the
vast amounts of data
• Faster computers and more ability to store vast amounts of data

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Steps In Making A
Decision:
• Identify and define the problem
• Determine criteria that will be used to

evaluate alternative solutions
• Determine the set of alternative solutions
• Evaluate the alternatives with the criteria
• Choose the alternative

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Types Of Decisions:
• Strategic Decisions
• High-level manager decisions concerning the overall
direction, goals and objectives of the organization (3 - 5
year time span)
• Examples:
• Does a local company try and sell out of the state or
internationally?
• Does an online only company try to open brick and mortar
stores?

• Tactical Decisions
• Mid-level manager decisions about how organization can
achieve the goals and objectives of the organization (1 year
or 6 month time span)
• Examples:
• What states or cities or locations to sell in?

• Operational
• Decisions concerning day to day operations such as number
of products to make or order, or how to schedule events.

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Approaches To
Decision Making:
• Tradition
• (Probably based on someone’s past
experience from way back)

• Intuition
• (Probably based on persons unconscious past
experiences)

• Rule of Thumb
• (Probably based on past experiences)

• Data Based Decisions
• (Based on past experiences, but in a more
objective way)
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