Data Analytics Chapter 1

1.1 | Decision Making:

Vocabulary:
- $Strategic decisions:$ A decision that involves higher-level issues and that is concerned with the overall direction of the organization, defining the overall goals and aspirations for the organization’s future.
- $Tactical decisions:$ A decision concerned with how the organization should achieve the goals and objectives set by its strategy.
- $Operational decisions:$ A decision concerned with how the organization is run from day to day.
The Process:

Identify and define the problem.
Determine the criteria that will be used to evaluate alternative solutions.
Determine the set of alternative solutions.
Evaluate the alternatives.
Choose an alternative.

Approaches to making decisions:
- Follow Tradition
- Intuition // Gut Feeling
- Rule of Thumb

1.2 | Business Analytics Defined:

Vocabulary:
- $Business analytics:$ The scientific process of transforming data into insight for making better decisions.
What makes decision-making difficult or challenging?
- Uncertainty
- A company doesn’t know how things will work out in the future
- Evaluation of alternatives
- There are an overwhelming number of alternatives to review
Business Analytics can help aid decision-making by:
- creating insights from data
- improving our ability to more accurately forecast ~~for planning~~
- helping us quantify risk
- yielding better alternatives through analysis and optimization

1.3 | A Categorization of Analytical Methods and Models

Analytics is generally thought to comprise three broad categories of techniques:
- ^^Descriptive analytics:^^ Analytical tools that describe what has happened.
- ^^Predictive analytics:^^ Techniques that use models constructed from past data to predict the future or to ascertain the impact of one variable on another.
- ^^Prescriptive analytics:^^ Techniques that analyze input data and yield the best course of action.
[Descriptive analytics:]()
- Vocabulary:
- $Data Query:$ A request for information with certain characteristics from a database.
- $Data Dashboards:$ A collection of tables, charts, and maps to help management monitor selected aspects of the company’s performance.
- $Data mining:$ The use of analytical techniques for better understanding patterns and relationships that exist in large data sets.
- Data queries are a request for information with certain characteristics from a database.
- Ex. A query about shipments might yield descriptive information about these shipments, the number of shipments, how much was included in each shipment, the date each shipment was sent, and so on.
- Data Dashboards are collections of tables/charts/maps and summary statistics that are updated as new data become available.
- Dashboards are used to help management monitor specific aspects of the company’s performance related to their decision-making responsibilities.
- Data Mining is the use of analytical techniques to better understand patterns and relationships that exist in large data sets.
- Ex. Company analyzes text from customer reviews/complaints to learn about what customers think of the brand or product
[Predictive Analysis:]()
- Vocabulary:
- $Simulation:$ The use of probability and statistics to construct a computer model to study the impact of uncertainty on the decision at hand.
- Predictive Analysis consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another.
- Ex: Past data on sales can help predict future sales
- Might also look at survey data and past purchase behavior
- Data Mining is often used in predictive analysis
- Ex: Grocery stores might develop a targeted marketing campaign → Predicting what promos customers will respond to best // Giving customers personalized coupons
[Prescriptive Analysis:]()
- Vocabulary:
- $Rule-based models:$ A prescriptive model that is based on a rule or set of rules.
- $Optimization models:$ A mathematical model that gives the best decision, subject to the situation’s constraints.
- $Simulation optimization:$ The use of probability and statistics to model uncertainty, combined with optimization techniques, to find good decisions in highly complex and highly uncertain settings.
- $Decision analysis:$ A technique used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events.
- $Utility theory:$ The study of the total worth or relative desirability of a particular outcome that reflects the decision maker’s attitude toward a collection of factors such as profit, loss, and risk.
- A Rule-Based Model is a combination of a prediction and a rule
- Ex. If a person is more than 60% likely to default on a loan → Don’t let them take out a loan
- Optimization models are models that give the best decision subject to the constraints of the situation.
- Ex. Portfolio models look at historical investment data to determine the best yield on investment return

1.4 | Big Data

Vocabulary:
- $Big data:$ Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software.
Big Data is generally defined as any amount of data that is too large to be processed by a standard-data processing software
The Four V’s of Big Data (According to IBM):
- Volume → Data at rest
- Because data are collected electronically, we are able to collect more of it.
- Velocity → Data in motion
- Real-time capture and analysis of data present unique challenges both in how data are stored, and the speed with which those data can be analyzed for decision-making.
- For example, the New York Stock Exchange collects 1 terabyte of data in a single trading session and having current data and real-time rules for trades and predictive modeling are important for managing stock portfolios.
- Variety → Data in many forms
- In addition to the sheer volume and speed with which companies now collect data, more complicated types of data are now available and are proving to be of great value to businesses.
- Analyzing information generated by these nontraditional sources is more complicated in part because of the processing required to transform the data into a numerical form that can be analyzed.
- Veracity (Accuracy) → Data in doubt
- Vocabulary
  - $Hadoop:$ An open-source programming environment that supports big data processing through distributed storage and distributed processing on clusters of computers.
  - $MapReduce:$ Programming model used within Hadoop that performs the two major steps for which it is named: the map step and the reduce step. The map step divides the data into manageable subsets and distributes it to the computers in the cluster for storing and processing. The reduce step collects answers from the nodes and combines them into an answer to the original problem.
  - $Data security:$ Protecting stored data from destructive forces or unauthorized users.
  - $Data scientists:$ Analysts trained in both computer science and statistics who know how to effectively process and analyze massive amounts of data.
  - $Internet of Things (IoT):$ The technology that allows data collected from sensors in all types of machines to be sent over the Internet to repositories where it can be stored and analyzed.
- Veracity has to do with how much uncertainty is in the data.
  - But businesses have realized that understanding big data can lead to a competitive advantage.
- Models used to analyze data:
  - Hadoop: provides a divide-and-conquer approach to handling massive amounts of data, dividing the storage and processing over multiple computers.
  - MapReduce: a programming model used within Hadoop that performs the two major steps
  - Map Step: divides the data into manageable subsets and distributes it to the computers in the cluster (often termed nodes) for storing and processing
  - Reduce Step: collects answers from the nodes and combine them into an answer to the original problem.
- Dealing with Data
  - Data security: protection of stored data from destructive forces or unauthorized users, is of critical importance to companies.
  - Data scientists know how to effectively process and analyze massive amounts of data because they are well-trained in both computer science and statistics
- The Internet of Things (IoT)
  - IoT is the technology that allows data, collected from sensors in all types of machines, to be sent over the Internet to repositories where it can be stored and analyzed
  - This ability to collect data from products has enabled the companies that produce and sell those products to better serve their customers and offer new services based on analytics.

1.5 | Business Analytics in practice

Vocabulary
- $Advanced analytics:$ Predictive and prescriptive analytics

→ The spectrum of Business Analytics

Financial Analysis
Human Resource (HR) Analytics
Marketing Analytics
Health Care Analytics
Supply Chain Analytics
Analytics for Government and Nonprofits
Sports Analytics
Web Analytics