Overview

A 3-day course exploring the Business & Human aspects of Data Modelling.  It also explores  the uses of Data Modelling beyond the “traditional” covering models in Data Strategy, Data Quality and Master Data Management.  Whilst business focused, we will also briefly look at why Data Modelling IS needed in the Big Data world.

Objectives

For Business focused Data models, a different set of skills and techniques are needed to those we see for creating “Database” focused models.  The “Business data modelling” component of this course describes the techniques for Top down, Bottom Up & Middle out capture of requirements and how to elicit these from the business community.  The human centric aspects of business data model development will be explored together with examining “how far is enough”?   The course leverages experiences gained in over 40 years of Data modelling from the tutor, who authored “Data Modelling for the Business”,  “A handbook for aligning business with IT using high level data models”.

The use of Data models in all the disciplines of Data Management will be examined, and we’ll see how they can be leveraged in areas including Data Strategy, Data Governance, Master Data Management and Data Quality Management.

Additionally, the course addresses the thorny issue of “do we still need to data model in the big data era”?

What you will learn

At the end of the course, delegates would have gained the following:

Level set understanding & terminology:

• • Understand the differences in Data model levels.
  • Discover techniques for eliciting business data requirements to produce meaningful business data models.
  • Learn about the need for and application of Data Models in Big Data and NoSQL environments
  • See the areas where Data modelling adds value to Data Management activities beyond Relational Database design
  • Understand the critical role of Data models in other Data Management disciplines particularly Data Strategy, Data Quality, Master Data Management and Data Governance.

Pragmatic Learning

• • Understand how to create data models that can be easily read by humans
  • Recognise the difference between Enterprise, Conceptual, Logical, Physical and Dimensional Data models
  • Through practical examples, learn how to capture Business data model requirements and how to apply different Data modelling techniques
  • Learn why Data models for Big Data are still required.

Course Outline:

• Data modelling recap: Data modelling basics, major constructs, identifying entities, model levels and the linkage between them.
• Requirements capture for Conceptual models:
  • Top down requirements capture: 
    When is it appropriate, what are the limitations.
  • Bottom up requirements synthesis: 
    When this works, where is it appropriate.  How do we cope with existing DBMS’s and systems.
  • Consolidated approach:
    The fusion of both top-down and bottom up.
• How to capture requirements for both Data and Process needs.
  • Practical approaches for capturing Data and process requirements.
  • Mid the gap – what have we missed.
• Data Models & Data Governance:  The essential role that Data Models play in Data Governance.  The use of rich metadata in our data models.  Using a data modelling repository as the basis of Business Glossary.
• Using Data Models for Data Integration & Lineage:  How to exploit data models for design of data integration approaches and in data lineage.
• Data Strategy:  The essential role of a Business Data model in Data strategy & steps for creating an actionable strategy.
• Data Quality Management and data models:  Yes, they do mix, we’ll see how a data model is a vital component in any DQ approach.
• Checking the Data vs, the Metadata; why does it matter?
• Use of standard data model constructs, and pattern models: Several standard model patterns are essential for data modellers to master, including:
  • Understanding the Bill of materials (BOM) construct.  Where can it be applied, why it’s one of the most powerful modelling constructs.
  • Party; Role; Relationship:  Why mastering this construct can provide phenomenal flexibility.
  • Mastering Hierarchies:  Different approaches for modelling hierarchies.
• Alternative Data Modelling Notations and tooling
• Normalisation:  Progressing beyond 3NF.  4NF, 5NF Boyce-Codd, and why, and when to use them.
• Data Modelling – Back to the Future?
  • Data Modelling didn’t start with relational! This may be a surprise to many people, but the first uses of data models were well before Relational data bases became the norm.  The techniques are applicable to many of the modern non-relational formats we see today.
  • Data Modelling for Big Data & NoSQL: What must change when we are developing data models for a Hadoop or other Big Data environments?
  • Do modelling tools support Big Data technologies, what are the restrictions and considerations?
• Modelling for hierarchic systems & XML:  What must change when developing data models for XML & Hierarchic systems?
• Services Oriented Architecture (SOA):  Why data models are essential for success.
• Dimensional Data Models:
  • How (and why) do we create a dimensional model?
  • Converting an ER model to a Dimensional model.
  • Slowly changing dimensions, what types and when are they applicable.    
  • Beyond the basics with aggregates, conformed dimensions, bridges, junk dimensions & fact less facts.
• Application Packages & Data Models:  Do we need to develop data models when implementing a COTS package?  Uses and benefits.

Audience

Practitioners who will need to read, consume or create data models.  Users who wish to gain a better understanding of data during Information Management initiatives including:

• • Business Intelligence & Data Warehouse developers & architects
  • Data Modellers
  • Developers
  • Data Architects
  • Data Analysts
  • Enterprise Architects
  • Solution Architects
  • Application Architects
  • Information Architects
  • Business Analysts
  • Database Administrators
  • Project / Programme Managers
  • IT Consultants
  • Data Governance Managers
  • Data Quality Managers
  • Information Quality Practitioners