Pre-Conference Tutorials: 7 November 2011

World-class companies apply the same quality principles, such as Deming’s Fourteen Points, Kaizen and Quality Function Deployment (QFD) to information as a product of business process. This presentation addresses how these principles and techniques apply directly to information as a product and knowledge workers as information customers.

In this tutorial Mr. English describes the fundamental principles of information quality. He describes how an organization can improve the quality and value of its information resources.

Learning Objectives:

• Define Information Quality and Information Quality Management
• Understand the Business Value Proposition of Total Information Quality Mgt
• Explain why Information Quality is essential to business survival
• Identify roles of Information Customers and Information Producers
• Describe the Processes for Measuring Business Information Quality
• Describe the Process for Information Process Quality Improvement
• Describe the Cultural Requirements and Governance for a Sustainable Information Quality Environment

If you attend this tutorial you will leave more ready and able to tackle enterprise wide DQ across your organisation. This highly interactive workshop will also give you the opportunity to tackle the problems of a fictional (but realistic) company who are experiencing end to end data quality challenges. This will enable you to practise some of the key techniques in a safe, fun environment before trying them out for real in your own organisations.

Run by Nigel Turner of NHT Data Consultancy, it will draw on his experience of initiating and running BT’s Enterprise Wide Data Quality Improvement programme, and of enterprise DQ consultancy undertaken in other large commercial and government organisations. BT’s ten year DQ improvement programme delivered benefits of over £600 million, winning praise from Gartner, Forrester and other industry analysts, so the approach outlined in this session really does work. Part of the day will include a case study of BT’s programme. The tutorial will cover:

• What differentiates enterprise DQ from traditional project based DQ approaches
• How to take the first steps in enterprise DQ
• Making the case for investment
• How to deliver the benefits – people, process & technology
• Real life case study – how BT reaped the rewards
• Practice case study – getting enterprise DQ off the ground in a hotel chain
• Key lessons learned and maxims for success

• Business intelligence and data warehousing trends; why do we need more query power?
• Different ways for classifying analytical database servers
• The advantages and disadvantages of record based, column-based, value-based, and cube-based storage structures
• What is in-database analytics and what is the relationship with Google’s MapReduce?
• The TCO of datawarehouse appliances
• Market overview including products from Aster Data, Exasol, Netezza (IBM), Ingres VectorWise, Kognitio, Microsoft, ParAccel, Sybase, Teradata, and Vertica (HP)

You will learn how to apply the core ten categories that make up the Scorecard to your:

• Operational systems that feed your data warehouse
• Data warehouse
• Data marts (both star schemas and snowflakes)

Dashboard software from Open Source will be used for creating, using and maintaining the Dashboards.

• What is a Dashboard?
• What is a Performance Dashboard?
• Performance Dashboard Implementation Process
• Dos and Don’ts of Dashboards
• Use of Dashboard software available as Open Source for creating, using and maintaining Dashboards

No tutorial takes you starting with raw data to a dashboard through all the steps in between! This is the uniqueness of this presentation! Case Studies will be used throughout to illustrate the various topics.

• What is Operational BI?
• Types of operational BI and business use
• On-demand Vs Event-driven analysis
• Data integration in an operational BI environment requirements
• Integrating BI into operational business processes
• An introduction to complex event processing
• The role of predictive analytics and in-memory data in operational BI
• Key mistakes that can be made and their consequences
• How to avoid these mistakes – best practice approaches to data integration, on-demand intelligence, event processing and analytics when implementing operational BI projects

Half Day
Tutorial
09:30-12:45

Through presentations and exercises this workshop will cover:

• The roles of a data model.
• Data modelling concepts.
• The development of data models.

We will examine:

• Pros and cons of deploying on the cloud
• Managing multiple databases in the cloud vs. managing a multi-tenant database
• Managing data privacy in a hybrid cloud computing environment
• Data quality on the cloud
• Building information services
• What works and what doesn’t

Key objectives of the session:

• Learn data management techniques that lead to a more extensible and supportable outcome
• Understand the motives and incentives of developers and business sponsors
• Leverage the enterprise data model, abstraction, and the grain matrix

This method covers:

• A recommended place to start (and why) - and it isn't with a data model;
• How to decide what to do next;
• Data Management and Architecture artifacts as services and tools, not deliverables.
  

Attendees will learn:

• The difference between enterprise architecture and data architecture
• A review of architectural principles.
• Approaches for developing, and governing enterprise architecture and data architecture.
• Architectural governance.