Mastering Data Management and Technology

Villanova University

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  • online
  • on demand

Villanova University

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Who should attend

Whether you’re a strategic decision maker, project manager, business analyst, market researcher, or IT professional, this course can provide you with a solid understanding of critical business intelligence topics. With subjects including best practices in data modeling, data architecture and performance management, you’ll gain the skills needed for success in today’s highly competitive global data analytics market.

About the course

Building upon the foundation concepts of Essentials of Business Intelligence, this 100% online certificate course explores more advanced topics including data modeling, securing business intelligence content, reporting and performance management. With case studies and exercises, you’ll gain real-world insight into all facets of business intelligence.

What You’ll Study

Data Architecture and Quality

  • Business Case for Improving Data Quality
  • Data Quality Assessment
  • Process Improvements, Source System Validation
  • Attribute Constraints, Profiling and Precision
  • Historical Data
  • Real Time vs. Right Time
  • Granularity for Analysis

Data Modeling

  • Dimension Types
  • Snowflake vs. Star
  • Fact Types
  • Aggregates
  • Process for Designing

CDM and MDM

  • Master Data Management (MDM)
  • Customer Master Data
  • Product Master Data
  • Other Types of Master Data
  • MDM Best Practices and Benefits
  • MDM Team Creation and Scope

BI Front-End Tool Segments

  • User Interfaces
  • Waterfall Development
  • Agile Methodology
  • The Different Types of Online Analytics Processing
  • Network Architecture
  • Maintaining Your BI Application

Industry Standards

  • Middleware (ODBC, ODBO, XMLA)
  • MDX
  • ETL
  • SQL Introduction
  • Common Warehouse Meta Model (CWM)
  • HTML

Creating a Business Analytics Competency Center (BACC)

  • BACC Review
  • Initialization
  • Define
  • Establish
  • Organize
  • Operate

Technical Architecture

  • Cloud-based BI and MDM
  • Virtualization
  • Load Balancing
  • Performance Tuning
  • On-line Analytical Processing (OLAP)

Success Factors and Constraints

  • Leadership
  • Business/IT Partnership
  • Business Alignment Incentives
  • Company Culture
  • Change Management

Curriculum

Success Factors and Constraints

  • Describe the significance of leadership buy-in
  • Name the three key characteristics of a strong and active sponsor
  • Summarize ways to bridge the gap between Business and IT
  • Discuss how to get people onboard and involved with your BI project, ways to build and maintain momentum, and how to align and design the right incentives
  • Name and explain the five structured phases of change

BI Front End Tool Segments

  • Describe scorecards, dashboards, and key performance indicators (KPI)
  • Review waterfall methodologies as applied to BI programs and compare/contrast waterfall vs. Agile for BI program management.
  • Discuss Agile program management methodologies and how they apply to Business Intelligence initiatives
  • Outline the three types of Online Analytical Processing (OLAP)
  • Discuss modern BI reporting capabilities

Data Architecture and Quality

  • Provide data quality issues and ways data quality can be improved
  • Explain the differences between the top-down approach and the bottom-up approach to data quality
  • Discuss the definitions of data quality assessment and explain how to effectively perform data quality assessments
  • Illustrate how data flows through from sources to reporting
  • Differentiate real-time from right-time data

Data Modeling

  • Illustrate star schema and snowflake schema design basics
  • Identify fact types and dimension types
  • Outline the data modeling process, including defining requirements back-to-front and front-to-back
  • Summarize when to create aggregate tables and why they appear in the data warehouse

Customer Data Management (CDM) and Master Data Management (MDM)

  • Define Master Data Management (MDM), customer master data, and product master data
  • Explain how customer master data and product master data are used, and provide the best practices for integrating each
  • Discuss why an MDM team is necessary as well as their roles and responsibilities
  • Analyze case studies in Master Data Management

Industry Standards

  • Define middleware, identify the three forms, and explain the uses of each
  • Summarize the meaning of ETL, explain how it works, and when to use it
  • Discuss best practices in ETL processing.

Creating a Business Analytics Competency Center (BACC)

  • Define what a BACC is, why it is needed, its role, and how to place it in an organization
  • Identify best practices for creating and managing a BACC
  • Analyze BACC case studies to investigate real-world competency center application.

Technical Architecture

  • Name the three tiers that support technical architecture, and explain each tier’s purpose
  • Discuss best practices in cloud-based BI and MDM
  • Distinguish the differences between virtualization and traditional architecture
  • Explain what scalability is, and how to use load balancing to achieve scalability
  • Summarize when and why performance testing is done

Mastering Data Management and Technology at Villanova University

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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