Chapter 1: Data Analytics Overview
Data Staging- Analytics Technologies - publishing
1. Exploration and Reporting
a. slicing/dicing
b. Multidimensional
c. Reporting
2. Knowledge Discovery
a. Unsupervised machine learning
b. Forecasting
c. Predictive machine learning
3. Visualization
a. Dashboard
b. Charts
What is Data Analytics?
● §Data Analytics can answer these and other questions:
○ §dWhat has happened in the past?
○ §Why did it happen?
○ §What could happen in the future? With what certainty?
○ §What actions can we take now to support or prevent certain events from
happening in the future?
○ §Can some of the actions resulting from our discoveries be automated?
○ §Can the analytics process by automated?
- §It is a process that involves
○ §Gathering data that are sometimes not in a usable form
○ §Cleaning up the data to make them usable
○ §Loading the data into storage models
○ §Manipulating them to discover the information
Data Analytics Takes Us From Data to Decision
Data
- Raw figures
- Metadata
- Nothing known apart from numbers and metadata
Information
- With context data become information
- Reveals relationships between entities
- What?
,Knowledge
- With added meaning
- Reveals trends and patterns
- Why?
Wisdom
- Reveals ideas, principles, biases
- Insights, over time
- What could happen?
Decision
- Course of action
- Implementation
- Monitoring
- Correction
- What should we do?
The convergence of Vocabulary
Statistics is used in data analytics
§Computer science improves capabilities to perform data analytics
,§Domain knowledge in every area has its unique vocabulary and analytical applications
● §Examples:
○ §Medicine and each area within medicine
○ §Public services
○ §Sports and Entertainment
○ §Business
■ §By line of business
■ §By functional area within a business
■ §By geographical location, and so on…
Data Science
- Analytics
- Data exploration
- Slicing and dicing (data manipulation)
- Visualization
- Charting
- Dashboards
- Reporting
- Knowledge discovery
- Data mining
- “Big Data”
- Forecasting
- Predicting
Why Study Analytics?
● §Demand for employees who understand and can analyze data
● §Huge growth in the amount of data available
● §Analytics can provide strategic advantages to an organization
Who Uses Analytics?
, ● §Data analysis is performed at many levels in the organization
● §Analytics is performed and used by individuals who may not have formal
training
Examples of Business Analytics
● §Retail – pricing, timing or pricing strategies, discounts, product placement,
up-selling and cross-selling of products
● §Manufacturing – demand forecasting, production planning
● §Marketing – targeted marketing
● §Supply chain – vendor selection, optimizing distribution costs
● §Customer service/help desk – customized service
● §Forecasting and budgeting
● §Audit and analysis of internal controls – risk assessment
● §Governments – resource allocations, tax compliance
● §Utilities – demand forecasting, management of power supplies
● §Investors – determine which investments are acceptable
Analytics Methodology within a Frame
Data Staging- Analytics Technologies - publishing
1. Exploration and Reporting
a. slicing/dicing
b. Multidimensional
c. Reporting
2. Knowledge Discovery
a. Unsupervised machine learning
b. Forecasting
c. Predictive machine learning
3. Visualization
a. Dashboard
b. Charts
What is Data Analytics?
● §Data Analytics can answer these and other questions:
○ §dWhat has happened in the past?
○ §Why did it happen?
○ §What could happen in the future? With what certainty?
○ §What actions can we take now to support or prevent certain events from
happening in the future?
○ §Can some of the actions resulting from our discoveries be automated?
○ §Can the analytics process by automated?
- §It is a process that involves
○ §Gathering data that are sometimes not in a usable form
○ §Cleaning up the data to make them usable
○ §Loading the data into storage models
○ §Manipulating them to discover the information
Data Analytics Takes Us From Data to Decision
Data
- Raw figures
- Metadata
- Nothing known apart from numbers and metadata
Information
- With context data become information
- Reveals relationships between entities
- What?
,Knowledge
- With added meaning
- Reveals trends and patterns
- Why?
Wisdom
- Reveals ideas, principles, biases
- Insights, over time
- What could happen?
Decision
- Course of action
- Implementation
- Monitoring
- Correction
- What should we do?
The convergence of Vocabulary
Statistics is used in data analytics
§Computer science improves capabilities to perform data analytics
,§Domain knowledge in every area has its unique vocabulary and analytical applications
● §Examples:
○ §Medicine and each area within medicine
○ §Public services
○ §Sports and Entertainment
○ §Business
■ §By line of business
■ §By functional area within a business
■ §By geographical location, and so on…
Data Science
- Analytics
- Data exploration
- Slicing and dicing (data manipulation)
- Visualization
- Charting
- Dashboards
- Reporting
- Knowledge discovery
- Data mining
- “Big Data”
- Forecasting
- Predicting
Why Study Analytics?
● §Demand for employees who understand and can analyze data
● §Huge growth in the amount of data available
● §Analytics can provide strategic advantages to an organization
Who Uses Analytics?
, ● §Data analysis is performed at many levels in the organization
● §Analytics is performed and used by individuals who may not have formal
training
Examples of Business Analytics
● §Retail – pricing, timing or pricing strategies, discounts, product placement,
up-selling and cross-selling of products
● §Manufacturing – demand forecasting, production planning
● §Marketing – targeted marketing
● §Supply chain – vendor selection, optimizing distribution costs
● §Customer service/help desk – customized service
● §Forecasting and budgeting
● §Audit and analysis of internal controls – risk assessment
● §Governments – resource allocations, tax compliance
● §Utilities – demand forecasting, management of power supplies
● §Investors – determine which investments are acceptable
Analytics Methodology within a Frame
