I have mentioned to my colleagues that this was the best training course I have been to so far (and I mean this sincerely) as I learned a lot as well as confirmed some of my thoughts in the last 3 days.

Ram Swami, Self Insurance Corporation


Three or two (abbreviated outline) full days.

Information is the modern competitive battleground. Don’t miss this unprecedented opportunity to learn Master Data Management (MDM), Data Quality (DQ), Data Governance (DG) and the new technologies for managing the important corporate asset of information.

The world of information management has changed significantly and now there are several new technologies poised to change it even more. Heterogeneity is alive and well. Increasing volumes and the desire for spontaneous response to business events are necessitating a variety of specialised platforms for handling specific workloads while maintaining a sense of consistency across the enterprise architecture. At the core of such a strategy is master data management.

This course provides a practical guide to implementing successful MDM from experience. It covers all the aspects of MDM, from justification to architecture to data management and project management using agile principles. This class gives the common process, organisational and architectural focus for building strategies and implementing master data management programs such as those that have consistently, for many years, improved the productivity and performance for clients including global giants.

Having data quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. In this course, you will receive strategic, expert guidance for building data quality.

Business participation comes in the form of data governance. Pragmatic, explicit advice will be given to forge effective governance that transcends the pitfalls and helps organisations position information as a competitive asset.

Due to increasing data volume and data’s high utility, there has been an explosion of capabilities in the past few years. While stalwarts of our information, like the relational row-based enterprise data warehouse (EDW), are highly popular, it is widely acknowledged that no single solution will satisfy all enterprise data management needs.

This course will explore the major categories of information data stores available in the market and help you make the best choices based on the workloads. This includes columnar databases, data stream processing, data virtualisation and the game changer that is the BigData/NoSQL/Hadoop-family solutions. This new and fast-growing category of data management technologies uses non-relational database architectures that is often better suited to handle the requirements of high-performance, web-scalable systems and big data analysis.

At this unique course, you will gain strategic, expert guidance for initiating and growing implementations and agile programs for information management.

This is the vendor-neutral course with the most ready-to-use content for the information management professional that every organisation needs, regardless of the toolset selected.

Who Should Attend Master Data Management, Data Quality and Data Governance?

The course is designed for Business Analysts, Chief Information Officers, Data Administrators, Data Architects, Data Modelers, Data Stewards, Database Administrators, Database Designers, Enterprise Architects, Information Technology Leadership, Project and Program Managers, Systems Analysts.

Course Outline

Master Data Management

How MDM provides benefits to an organisation and how to justify an MDM project
The various architectural styles of MDM
How to incorporate syndicated data to MDM
What MDM provides for hierarchy management and data governance
How to go to market for an MDM tool
All the roles and responsibilities on an MDM project
How to manage the organisational change that occurs with MDM projects
How to manage an information management project using agile principles

Data Quality

How to Define Data Quality Expectations and profile Against the Defined Expectations
How to Profile Data Against the Defined Expectations
How to Measure Data Quality Impact Across Various Thresholds
How to Improve Quality of Data to Improve the Business
The 11 Types of Data Quality Defects

Data Governance

The contributions of each level of the business to data governance
Aligning governance with business strategy
The role of governance in technology domains of business intelligence and master data management
Improving corporate data quality through data governance
Extending data governance across the enterprise

The New Data Ecosystem

Major Information Management Strategic Trends
The continuing role for Relational Row-Oriented Data Warehouse and Data Marts
What are data streams and when they are an information store
How columnar databases work and the types of analysis they support
When the right architectural answer is not to centralize everything and do data virtualisation
Understanding the rapidly growing functionality of the Big Data marketplace
The impact of big data on existing information strategies and architectures
How to get started in deploying a Big Data environment
What is NoSQL and what are the business drivers
What are Key-Value Stores, Document Stores and Graph Stores