BI and Analytics Principles and Practices: Charting the Course to BI -Analytic Success Training

Business Analytics and Business Intelligence architecture is a set of frameworks used to organize the data, management, and technical components required to build BI systems and analytical capabilities. Architecture plays a critical role in BI and analytics programs by ensuring that the development efforts of multiple projects and teams align to create a cohesive and valuable whole.

A comprehensive architecture addresses business objectives, organizational structures, roles and responsibilities, integrated information resources, key business processes, and a broad range of technology components.

There are no prerequisites for this course.

• Business, data, integration, and technology architects involved in BI and analytics programs

• BI and analytics program and project managers who need a foundational understanding of successful analytics initiatives

• BI and analytics team members who want a better understanding of the overall system landscape

In this training, participants will learn:

• The full scope of architectural objectives, including structural integrity, standardization, reusability, environmental fit, aesthetics, and sustainability

• A framework to ensure overall architectural completeness and success covering business purpose, organization, integrated information, processes, and technology platforms

• A framework to organize business components such as performance, stakeholders, processes, rules, and information

• A framework to organize the enterprise in terms of people, purpose, process, and structure

• A framework to organize information components and capabilities including data collection, storage, operational data integration, data warehousing, big data integration, data distribution and access, data modeling, and metadata management

• A framework to organize BI and analytics–related processes such as methodologies, data governance, data flow, business processes, and operations/support

• A framework to organize technology platform components including servers, data sourcing, databases, storage, data integration, analytics, and data management

Module 1 – BI and Analytics: Then and Now

• Definitions: Data Warehousing, Business Intelligence, Business Analytics

• Evolution and alignment with business goals

• Core components: People, applications, systems, processes, data, and technology

• Perspectives and points of view

• BI and Analytics lifecycle

• Maturity models and assessment approaches

• Roadmapping and continuous planning

• Common mistakes in validation and business-driven BI delivery

• Summary and discussion

Module 2 – Supporting the Organization

• BI and Analytics stages

• Descriptive BI and Analytics

• Diagnostic BI and Analytics

• Discovery BI and Analytics

• Predictive BI and Analytics

• Prescriptive BI and Analytics

• Roadmap expansion

• Common mistakes in predictive analytics and data storytelling

Module 3 – Architecture and Methodology

• Data integration architecture and strategies

• Big Data concepts, sources, and characteristics

• Data lakes and analytics sandboxes

• Data warehouse implementation approaches

• Agile development and automation

• Roadmap development and project selection

• Common pitfalls in data federation and big data implementations

Module 4 – Data Management

• Data governance concepts, roles, and responsibilities

• Data stewardship

• Data quality concepts, assessment, and improvement

• Data profiling techniques and tools

• Building and executing a data strategy

• Common mistakes in data quality programs

Module 5 – BI Technology

• BI technology stack and architecture layers

• Functions and services

• Selecting current and future BI technologies

• Technology management and platform reliability

• Common mistakes when adopting new BI technologies and Hadoop implementations

Module 6 – Summary

• Overall summary

• Key takeaways