Recap examples of analytics in real-world situations, particularly business scenarios.
Display an understanding of how analytics works in an organization.
Analytics Defined
Analytics involves the extensive use of data, statistical and quantitative analyses, explanatory and predictive modeling, and fact-based management to drive decision-making.
Analytics is about providing business users with better insights, particularly from operational data stored in transactional systems.
The key consumer of these analytics is the business user, whose job isn't directly related to analytics but who uses analytical tools to improve business processes.
Questions Data Analytics Can Answer
What has happened in the past?
Why did it happen?
What could happen in the future?
With what certainty?
What actions can we take to support or prevent certain events from happening?
Data Analytics Process
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.
Rank Me Up!
The DIKW pyramid (Data, Information, Knowledge, Wisdom, Decision).
Data: Raw figures, metadata; nothing known apart from numbers and metadata.
Information: Data with context.
Knowledge: Reveals relationships between entities.
Wisdom: Reveals trends and patterns.
Decision: Reveals ideas, principles, biases; course of action.
Data to Wisdom
Data: What?
Information: Why?
Knowledge: What could happen?
Wisdom: What should we do?
Decision: Implementation, monitoring, correction.
Convergence of Vocabulary
Domain Knowledge: Provides the contextual understanding needed to interpret data accurately and derive meaningful insights.
Computer Science: Provides the technical foundation for processing, analyzing, and interpreting data efficiently.
Statistics: Provides the mathematical foundation for analyzing, interpreting, and deriving insights from data.
Data Analytics represents the convergence of domain knowledge, computer science, and statistics, incorporating research, machine learning, and software development.
Data Science vs. Data Analytics
Data Science
Data Exploration: Slicing and Dicing.
Knowledge Discovery: Data Mining.
Big Data: Un/Structured Data, Social Media, Text Analytics.
Data Analytics
Reporting Visualization: Charting, Dashboards.
Why Study Analytics?
Analytics is used by:
Business Users (BUs): Rely on data insights to make strategic decisions.
Data Analysts (DAs): Focus on processing and interpreting structured data, creating visualizations, and generating reports to support business users.
Data Scientists (DSs): Apply advanced statistical methods, machine learning, and predictive modeling to uncover deeper insights.
Data analysis is performed at many levels in the organization; data analytics is performed and used by individuals who may not have formal training.
Examples of Business Analytics
Retail: Timing or pricing strategies, discounts, up-selling and cross-selling of products.
Manufacturing: Demand forecasting, production planning.
Marketing: Targeted marketing.
Government: Resource allocations, tax compliance.
Utilities: Demand forecasting, management of power supplies.
Investors: Determine which investments are acceptable.
Examples of Data Analytics Applications
Sports
Science
Medicine
Education
Law Enforcement
The Analytics Cycle: A Framework for Data Analytics
Data analytics is the process of gathering, cleaning, storing, and manipulating data to provide insights.
The data analytics process converts raw data to information to knowledge to wisdom and finally a decision.
Enablers of analytics include technology, infrastructure, tools, and techniques.
Analytics Cycle Steps
Identify goals
Gather data
Design Model
Apply Model
Derive Insights
Present Findings
Review Results
Make Decisions
Deploy Strategy
People/Users, Controls and Training are the foundation of the framework.
Benefits of analytics are value/profit, performance, safety, and health/longevity.