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Data Models

Modeling Complex Enterprise Data

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

Constructs and Strategies for Developing Analytic Insights

Great analytics start with great data that is clean, consistent, fresh, validated, and well-modeled and enhanced with comparisons, features, and names. Great data analytics enable enterprises to make effective decisions quickly that are based on evidence and to have everyone agree on a single version of the truth. With the proliferation of data and data tools, there is more division and disagreement around “the numbers” than there ever has been. Large enterprises are wasting millions of dollars and driving coordination costs even higher when proven frameworks and methodologies exist.

This course covers the frameworks, best practices, and techniques for collecting, organizing, and modeling complex enterprise data. Most corporations and large organizations are sitting on a mountain of incredibly valuable internal data. This data is unique to their organization and holds the keys to increased performance and future development, but they find it very hard to actually use their data. Here we discuss practical, real-world solutions for getting enterprise data in a state where it can easily support decision making and be used for developing analytic insights. We cover traditional topics like star schema design, aggregation and partitioning strategies, data replication, conformed dimensions and transactional data transformations, as well as more modern topics like modeling data for machine learning processing, working with streamed data, and leveraging data fabric/orchestration/virtualization technologies. We address concerns like security, auditability, validation and quality, and master data management. You’ll also learn about data monetization and see data equity frameworks that help provide a strong understanding and justification for the valuation of data within an enterprise.

At the conclusion of this course, you’ll be ready to develop a coherent, realistic plan for modeling your most important enterprise data using a mixture of frameworks and methods based on real-world examples developed in Dan and Tim’s consulting practice.

What you’ll learn and how you can apply it

  • Learn methods for the identification of your most valuable data.
  • Learn how to organize complex enterprise-scaled data assets around analytics use cases rather than data source and legacy data stores.
  • Understand how to develop appropriate levels of aggregation that facilitate decision making across the enterprise.
  • Get practical, real-world insights from experts who work with enterprise data every day.
  • Learn when and how to leverage new technologies and capabilities and how to integrate them into your existing systems, whether they be cloud-based, on-premise, or a hybrid.
  • Understand how to blend traditional hierarchical modeling practices with new techniques from the world of network science like property graphs.
  • Modeling from the front end for navigation and drill paths
  • Modeling for machine learning, predictive analytics, and forecasting

This live event is for you because...

  • You understand the importance of data strategy and want to how to gain broader support within your organization for strategic data analytics projects.
  • You sit at the intersection of data and business and want to estimate the value of data within your firm.
  • You want to move up to a more strategic role within your firm and know that analytic architecture and data strategy development are the keys to your success.

Prerequisites

  • You should have basic knowledge of data processing and data strategy.
  • You should have a basic knowledge of enterprise systems and decision-making dynamics.

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Enterprise Data Modeling Overview (15 minutes)

  • Star Schema foundations
  • Dimensions, attributes, facts, and derived measures
  • The curse of fast data

Segment 2: Identifying Your Most Important Data (30 minutes)

  • Frameworks for identifying the importance and use cases for data assets
  • Paradox of increasing returns with use
  • Challenges of data asset classifications

Segment 3: Data Modeling for Analytics Use Cases (45 minutes)

  • Star schemas and property graphs
  • Aggregation strategies
  • Building hierarchical dimensions
  • Data storage, processing, and streaming and ACID tradeoffs
  • Data creation: feature engineering, machine learning, time series analysis, and forecasting
  • Promoting self-service data sets and analyses to governed enterprise level

Break (10 minutes)

Segment 4: Data Modeling Requirements for Different Types of Data (40 minutes)

  • Machine generated data
  • Business process data and records of human activities
  • OLAP cubes, analytic views, and highly structured data
  • Data quality, consistency, and metadata management

Segment 5: Real-world Applications (25 minutes)

  • Rubrics and methodology for identifying and valuing internal data
  • Data selection and exclusion from real world perspective
  • Recommended data model development sequence and method for a first initiative
  • Data Modeling and design implications for security and access
  • Data Modeling and design implications for Machine learning and predictive analytics
  • Data Modeling and design implications for dashboards and interfaces
  • Data Modeling and design implications for scalability and production systems

Segment 6: Questions and Answers (15 minutes)

  • Course wrap-up and next steps

Your Instructors

  • Dan Vlamis

    Dan Vlamis is President and founder of Vlamis Software Solutions, a boutique consultancy which has led more than 200 Business Analytics implementations for more than 25 years at many of the world’s leading organizations. Recognized by Oracle as an Oracle ACE Director, he consults with Oracle Product Management regularly. Dan covers Oracle BI and related products through his popular blog at www.vlamis.com/blog. Dan was a co-author on the Oracle Press books Data Visualization for Oracle BI 11g and Oracle Essbase and Oracle OLAP - The Guide to Oracle's Multidimensional Solution. Dan Vlamis holds a degree in Computer Science from Brown University. Dan has been a popular speaker at major Oracle conferences such as Oracle OpenWorld, Collaborate, and ODTUG Kscope for two decades and is known for his live demos of Oracle software. Dan has led the Analytics and Data Summit conference by the Analytics and Data Oracle User Community for many years.

  • Tim Vlamis

    Tim Vlamis is an expert in the visualization of data and the design of business intelligence dashboards, Tim combines a strong background in the application of business intelligence (BI), analytics, and machine learning with extensive experience in business modeling and valuation analysis. Tim has worked with dozens of America’s largest corporations and leading government and science organizations in the design of their business intelligence dashboards and data visualization programs. Tim has assisted several high-tech startups, led partnership formations and dissolutions in Europe, Australia, Hong Kong, Canada, and India, and has negotiated acquisitions in Mexico and Canada. He earned his Professional Certified Marketer (PCM) designation from the American Marketing Association and is an active speaker on business analytics and data visualization topics as well as machine learning, predictive analytics, and analytic warehousing. In addition to a life-long study of business processes, systems, and theories, Tim is a passionate student of complexity theory, the history of mathematics, and the principles of design. Tim earned an MBA from Northwestern University’s Kellogg School of Management and a BA in Economics from Yale University. Tim is a named contributor to multiple Oracle University courses on predictive analytics and machine learning and often serves as an expert instructor for them.