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GOAL DIRECTED LEARNING
Master DW

Data and Analytics Tutorial

Data and Analytics Overview
Under Construction

Data and Analytics Success

Data and Analytics Strategy
Project Management
Data Analytics Methodology
Quick Wins
Data Science Methodology

Requirements

BI Requirements Workshop

Architecture and Design

Architecture Patterns
Technical Architecture
Data Attributes
Data Modeling Basics
Dimensional Data Models

Enterprise Information Management

Data Governance
Metadata
Data Quality

Data Stores and Structures

Data Sources
Database Choices
Big Data
Atomic Warehouse
Dimensional Warehouse
Logical Data Warehouse
Data Lake
Operational Datastore (ODS)
Data Vault
Data Science Sandbox
Flat Files Data
Graph Databases
Time Series Data

Data Integration

Data Pipeline
Change Data Capture
Extract Transform Load
ETL Tool Selection
Data Warehoouse Automation
Data Wrangling
Data Science Workflow

BI and Data Visualization

BI - Business Intelligence
Data Viaulization

Data Science

Statistics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics

Test and Deploy

Testing
Security Architecture
Desaster Recovery
Rollout
Sustaining DW/BI

Data Analytics Success

Data and Analytics are hot! This approach has been labeled the "new oil". Data and analytics can impact enterprise results by: reducing costs, increasing revenues and addressing risks.

Costs may be reduced by:

  • Negotiating improvements in supply
  • Dropping unprofitable products and customers
  • Reducing waste due to low quality

Risks may be addressed by:

  • Avoiding problems such as poor credit risks

Revenues may be increased by:

  • Understanding and better serving customers
  • Focusing on the most profitable products and customers
  • Cross selling to customers
  • Capitalizing on trends
  • Growing marketing opportunities

This sounds wonderful - but how can these benefits be realized? Successful Data and Analytics efforts require the right mix of:

  • People: mix of the right people in the right roles.
  • Process: sound project management coupled with the best practice methodologies.
  • Plans: effective strategies, roadmaps, business cases and project plans.
  • Technology: appropriate tools to architect, design, develop and sustain Data and Analytics.
  • Data: clean and appropriately structured data are the raw materials.
  • Algorithms: models that describe, predict and prescribe.

You are invited to use the resources provided in this Infogoal Data and Analytics Tutorial to maximize your return from Data and Analytics.


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