Business Information and Modern BI: Balancing OLAP, Analytics, and Performance Management Training

BI program managers; business sponsors and information consumers; business analysts; data architects and systems architects; developers of data marts, data warehouses, OLAP environments, semantic layers, or information products such as reports, dashboards, and scorecards.

• How to classify business requirements across key BI functions: business analytics, OLAP, and performance management
• Where data governance and MDM intersect with your BI program
• The impacts of big data technologies on your information architecture: virtualization, unstructured data, data mining, and visualization
• Dimensional modeling techniques that facilitate business interaction, support high-impact analytics, and synchronize with integrated performance management
• Best practices that ensure your data warehouse is a useful resource for business analytics
• Which parts of your information architecture should be subject to centralized development and control, and which parts can be entrusted to the business
• Multiple ways to enable the combination of enterprise dimensional data with local, external, or unstructured data
• How to match methodologies and technology standards to the unique requirements of each initiative

Module 1 Modern BI

• Modern Concerns
  • Traditional data architecture
  • Impact of new technologies and practices
• Who Has an Interest in Information
• Modern BI Services
  • The Three Pillars of Modern BI
  • Performance Management, OLAP and Analytics
• Modern Information Architecture
• Impact of new technology on each service category
• Information Requirements
  • Organizing information requirements by business process
  • Sorting requirements by BI service

Module 2 Performance Management

• Performance Management
  • Business performance
  • Dashboards and Scorecards
• Business Metrics and Data
  • Aligning metrics with business strategy
  • How to document business metrics
• Performance Management and Other Services
  • Linking dashboards to detail (PM and OLAP)
  • Targeting analytics and measuring ROI (PM and Analytics)
• Dynamic Nature of Performance Management
  • Project vs. operations
  • Adapting and evolving content of performance dashboards
• Visualization
  • Communicating the right message
  • Eliminating chart junk
  • High bandwidth visual elements

Module 3 Business Analytics

• Insight and Impact
  • What is an analytic model
  • Kinds of models
• Enabling Analytics
  • Identifying where analytics can have business impact
  • The makeup of project teams
  • Business analysts and analytic modelers
  • Methodologies for analytics
• Big Data
  • Sorting through the hype
  • Kinds of data structures
• Information Architecture
  • The place of unstructured data in your program
  • Capture & Explore
  • Discard / Deploy / Augment / Extend
• Analytics and Performance Management
  • Targeting metrics and measuring ROI
• Which Data to Use
  • Looking beyond enterprise data
  • The analytic sandbox
• Incorporating Unstructured Data
  • Augmenting the data warehouse
  • Linking dimensional data with machine data, semi structured data and key-value pairs

Module 4 OLAP and Reporting

• Analysis and Detail
  • Supporting ad hoc access, analysis and reporting
  • Integrated record of business performance
• Enterprise Perspective
  • Integrated data vs. stovepipes
  • Conformance
• Business Information Requirements
  • Capturing requirements through the Business Dimensional Model (BDM)
  • Fact groups & definitions
  • Dimensionality and hierarchies
  • Master Data Management
• Dimensional Design
  • Key characteristics of dimensional models
  • Handling history through “slow changes”
  • How to document a dimensional design
  • Quick overview of advanced concepts in dimensional modeling
• Governance and Master Data
  • Data governance
  • Governance roles
  • Master data management
  • Master data and conformed dimensions
• OLAP and Performance Management
  • Synchronizing perspectives on business metrics
• OLAP and Business Analytics
  • Making the data warehouse analytics-friendly
• Virtualization
  • Where virtualization may fit in the information architecture

Module 5 People, Process, and Technology

• Architecture
  • Components of architecture
  • Program management and planned evolution
• Process: One size does not fit all
  • Different nature of projects in OLAP, performance management and analytics
  • Adapting projects to scope and importance of business need
• Development Methods
  • Control vs. autonomy in each BI service category
  • The agile manifesto
  • Collaboration and frequent delivery in BI
• Organization: BI, IT and the Business
  • Evolution of BI and analytic capabilities
  • Cross-functional teams
  • Competency centers and the contract with IT
• Choosing Your Toolset
  • Program control vs. Business Autonomy
  • Policies for centralized and user-maintained toolsets

Module 6 Summary and Conclusions

• Summary of Key Points
• Recommended Reading