Business Intelligence and Analytics Principles and Practices: Charting the Course to BI and Analytic Success Training
Learn via: Classroom / Virtual Classroom / Online
Duration: 1 Day
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Upcoming Training
10 May 2021
1 Day
Business analytics and intelligence architecture is a set of frameworks to organize the data, management and technical components used to build BI systems and analytical capabilities. Architecture plays an important role in business analytics and intelligence programs and projects, ensuring that the development efforts of multiple projects and teams fit together to form a cohesive and valuable whole. Comprehensive architecture addresses business purposes, organizational structure and roles/responsibilities, integrated information resources, various types of key processes and a wide array of technology components.
There is no prerequisites for this course.
Business, data, integration and technology architects involved in business analytics and intelligence programs
Business Analytics and Intelligence program and project managers that need a basic understanding of the fundamental components of successful analytics and BI programs
Business Analytics and Intelligence team members that need a better understanding of the big picture of the systems they work with
The full scope of architectural objectives – structural integrity, standardization, reusability, environmental fit, esthetics, and sustainability
A framework to ensure overall architectural completeness and success - business purpose, organization, integrated information, process and technology platforms
A framework to organize business components - performance, stakeholders, processes, rules and information
A framework to organize the organization - people, purpose, process and structure
A framework to organize information components and capabilities- collection, storage, operational data integration, data warehousing, big data integration, distribution/access/applications and data modeling/metadata management
A framework to organize the processes associated with business analytics and intelligence - methdologies, data governance, data modeling/metadata management, data flow, business processes and operations/support
A framework to organize technology platform components - servers, data sourcing, databases, storage, data integration, business analytics and data management
Module 1 - BI and Analytics—Then and Now
Definitions
Data Warehousing
Business Intelligence
Business Analytics
Evolution
Achieving Business goals
Emphasis
Components
People and Applications
Systems and Processes
Data and Technology
Perspectives
Points of View
Program Orientation
BI and Analytics Lifecycle
Evolving Environment
Parallel Paths
Assessment
Purpose and Approach
Maturity Models
Roadmap
Continuous Planning
Mistakes to Avoid
When Validating Direction
When Delivering Business-Driven BI
Summary and Discussion
Key Points
Discussion
Module 2 - Supporting the Organization
BI and Analytics Stages
Purpose
Descriptive BI and Analytics
Definitions and Benefits
Query Services
Data Feeds and Downloads
On-Demand Reporting
Scheduled Reporting
Basic Data Visualization
Diagnostic BI and Analytics
Definition and Benefits
Online Analytical Processing
Dimensional Data Marts and Star Schema
The OLAP Cube
Performance Management: Definitions and Concepts
Performance Management
Scorecards and Dashboards
Continuum
Casual Analysis
Analysis Types
Discovery BI and Analytics
Definitions and Benefits
Analytic Modeling
Advanced Data Visualization
Data Mining
Geospatial Analytics
Text Analytics
Storytelling
Predictive BI and Analytics
Definition and Benefits
Forecasting and Prediction
Prescriptive BI and Analytics
Definitions and Benefits
Simulation and Optimization
Decision Management
Roadmap
Expanding Your Roadmap
Mistakes to Avoid
In Predictive Analytics Efforts
In Data Storytelling
Summary and Discussion
Key Points
Discussion
Module 3 - Architecture and Methodology
Data Integration Architecture
Integration Strategy
The Purpose of Architecture
Architecture and Data
Components and Structures
Integration Techniques and Technologies
Data Types
Data Properties
Data Characteristics
Data Structure
Big Data Defined
Big Data Sources
Big Data Characteristics
Physical Storage
Ecosystem
Building Blocks
Traditional Architecture
Data Lakes
Analytics Sandboxes
Expanded Architecture
Data Warehouse Implementation
Agile Development
Data Warehouse Automation
What Can You Automate?
Data Warehouse Operation
Components
Evolution
Project Selection
Roadmap
Expanding Your Roadmap
Mistakes to Avoid
When Using Data Federation
In Your Big Data Implementation
Summary and Discussion
Key Points
Discussion
Module 4 - Data Management
Data Governance
Data Governance Concepts
Data Governance Roles and Responsibilities
Data Stewardship
Data Quality
Data Quality Concepts
Data Quality Assessment
Data Quality Improvement
Data Profiling
Purpose and Processes
Profiling Techniques
Analyzing Data Profiles
Tools and Technology
Application
Roadmap
Continuous Planning
Mistakes to Avoid
When Creating Your Data Strategy
When Building a Data Quality Program
Summary and Discussion
Key Points
Discussion
Module 5 - BI Technology
The Technology Stack
Technology Layers
Functions and Services
Technology Architecture
The Right Technology-Present and Future
Technology Management
Reliable Platforms
Roadmap
Continuous Planning
Mistakes to Avoid
When Adopting New Technologies in BI
In Hadoop Implementations
Summary and Discussion
Key Points
Discussion
Module 6 – Summary
Summary
Key Points
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