Introduction

BUS 320 Business Data Analysis and Interpretation

Elizabeth Stanny

What is business analytics?

is the iterative process of learning from data, transforming data into insights for making better decisions

Speaker notes go here.

Business analytics includes

• Audit Analytics
  • Delivering value with audit data analytics
• Financial Analytics
  • Three-minute guide
• Human Resource Analytics
  • How Analytics is Reinventing Human Resources

• Marketing Analytics
  • A Day in the Life of a Marketing Analytics Professional

• Supply Chain Analytics
  • How does supply chain analytics work?
• Analytics for Government and Nonprofits
  • Data-Smart City Solutions
• Sport Analytics
  • Swimming in Data

• Tax Analytics
  • Discover the value in your tax data
• Web Analytics
  • Part of Internet marketing

What is analytics?

• extracting value from data
• limited in the past by software (e.g., Excel) and hardware
• today can use powerful tools (e.g. R) to more efficiently and effectively analyze a broader range of data and types of data than with (Excel)
• allow for enhanced insights from data to address business issues and transform the way decisions are made

The human element

Despite the increased sophistication of the data and the analytics, the most important aspect continues to be the human element, which includes:

critical thinking and judgment!

Learning objectives

• Determine question want to answer with data analysis
• Extract, transform and load relevant data (ETL process)
• Apply appropriate data analytic methods
• Interpret and present the results

1. Determine question want to answer with data

• Who are the relevant stakeholders?
• What are their objectives?

2. ETL process (data wrangling)

• Extract (determine data will answer questions)
• Load (import/read)
• Transform (tidy)

3. Apply appropriate analytic method

TYpes of data analysis question

Question	Type	Example
What are summarized characteristics of a data set without interpretation? (i.e., report a fact)	Descriptive	Have monthly profit margins changed in the last 2 years?
Are there patterns, trends, or relationships within a single data set? (Often use dot propose hypotheses for future study)	Exploratory	Are monthly profit margins related to underlying economic factors?
Are there patterns, trends, or relationships in a data set AND how applicable are findings to a wider population?	Inferential	Are monthly profit margins for all companies related to economic factors?
Can measurements (features) predict for individuals (people or things) predict outcomes, but not what causes the outcome?	Predictive	Can economic factors predict a company's future profit margins?

Descriptive Analysis

• Descriptive statistics includes statistics such as mean, standard deviation, and count.

• These statistics help describe the overall characteristics of a data set.

• Provides insights into what has already happened.

• Doesn’t involve making any predictions or inferences about the data.

  • Example: A retailer uses descriptive analysis to understand the average sales per day, the most popular items sold, and the typical customer demographics.

Exploratory Analysis

• Exploratory analysis is used to understand the data, get a feel for it, formulate hypotheses, and create visualizations.

• The step that comes before formal, hypothesis testing and modeling.

• Uses visual methods to understand data patterns, relationships, or anomalies, and extracts important variables.

  • Example A retailer uses exploratory analysis to understand the correlation between promotions and sales revenue.

Inferential Analysis

• Inferential analysis is used to make predictions or inferences about a population based on data from a sample of that population.

• Involves statistical tests to determine whether we can generalize findings from a sample to a larger population.

  • Example: A medical researcher might collect data from a small group of people (a sample) to determine if a new drug works. Using inferential statistics, the researcher can then infer if the same results would occur in the larger population.

Predictive Analysis

• Predictive analysis is used to make predictions about future events based on current and historical data.

  • Examples:
    • A weather forecaster uses predictive analysis to predict future weather based on past weather patterns.
    • A financial analyst uses predictive analysis to forecast future stock prices based on historical data.

Your turn

Determine the type of analysis for each of these:

• Company wants to forecast future trends based on historical data, which type of analysis should they use?

• What type of analysis would a business use to investigate potential relationships between variables in a new dataset?

• What type of analysis is used to draw conclusions from a sample of data and make inferences about the population from which it was drawn?

• If a company wants to present a summary of their past sales data, which type of analysis should they use?

4. Interpret and present results

Interpret

• What do you see?

• Do you see what you expected to see?

• Do your results make sense to you?

• Is any further analysis required to meet your objective?

Present

• How will your audience best receive the information?

• How much time you will have to present?

• Whether the presentation will be in person or virtual?

• What the best format of the presentation will be?

• Whether you have any additional recommendations for further analysis?

Learning objectives for this class (again)

• Determine question want to answer with data
• Extract, transform and load relevant data (ETL process)
• Apply appropriate data analytic techniques
• Interpret and share the results

R will be our main tool

Create reproducible results

Data analysts who don’t organize their transformation pipelines often end up not being able to repeat their analyses, so the advice I would give to myself is the same advice often given to traditional scientists: make your experiments repeatable!

Course Overview

Hompage

bus320-quarto-netlify.app

• Links to all course materials

Questions