Drawing on more than 15 years personal experience and the shared experience of Datanomic, Steve will provide real world examples of organisations who are profiting from improved data quality and tell you how they have achieved it.

The Carphone Warehouse is Europe's leading independent retailer of mobile phones and services, with over 2,000 stores in 10 countries. Over the past 5 years, The Carphone Warehouse has built up a significant Telecoms business, which already contributes half of the Group's revenue and is set to be a major driver of future profitable growth.

During this presentation Bhavesh Chavda, Head of Enterprise Data Warehousing at The Carphone Warehouse will take us through the Information Management vision of Carphone Warehouse describing the key stages in their road map. The audience will hear how through effective data warehousing and data quality initiatives The Carphone Warehouse have been able to support key business challenges such as single view of customer and increasing customer loyalty and service levels, whilst operating in a fast moving and ever changing environment.

The audience will learn how:

• Enterprise Data Warehousing supports key business challenges and initiatives
• The roadmap to success- lessons learnt
• Why data management and data quality is intrinsic to the business- the business benefits gained
• How an Integration Competency framework has helped to meet strategic platform challenges

Attend this session to learn how you can gain control of your metadata. Find out how to consolidate, integrate, and audit metadata from disparate tools and data sources – including reporting and business intelligence, data integration (ETL), data modelling, and applications – to deliver trusted data for compliance requirements, internal controls, and critical business decisions.

Plus, learn what’s ahead in metadata management including impact analysis and lineage from both a data and user perspective, as well as data quality and operational metadata analysis.

The MDM journey is likely to be a painful process for many organisations. Faced with a challenge of process, technology and organisational alignment, how do you determine what information assets should become a part of your MDM solution - what data is strategic, what is not? What is the quality of your operational systems and what integration or cleansing needs to happen in order to support your MDM requirements? Where do you start?

This session will cover the following topics:

• Gaining a better understanding of source data for a MDM initiative
• Analysing the risk inherent in source systems prior to starting an MDM initiative
• Understanding what's truly in your data, for better MDM project preparation
• Cleaning your source data for efficient MDM implementations
• Automating data improvement processes to keep your master data accurate over time

Data Integration is a difficult challenge – and getting tougher all the time with new architectures, more systems, shorter timelines and smaller budgets. What are the main issues with today’s approach to Data Integration – and how can we most improve our chances for success? Karl Glenn of ETL Solutions explores this topic in an eye-opening perspective session that should be well worth your time.

This session will show how IBM is addressing these issues and evolving its warehousing solutions towards “Dynamic Warehousing”, a key part of its all-encompassing vision for Information On Demand.

Key Bullet points:

• Introduction & Objectives
• Brief History: DB2 & Warehousing To Date
• IBM’s Vision for Dynamic Warehousing
• Recent IBM Warehousing Announcements
• Conclusion & Summary

Customer Data Quality is not only a local issue anymore. The need to tackle global name, address and identification challenges is becoming increasingly important and organizations have to think across their borders. Current global solutions lack local knowledge to provide the intended party data quality. Human Inference provides a solution for this paradox by combining local knowledge with intelligent interpretation methods.

This session is designed to show you:

• challenges of global data
• the need for knowledge and specific local/regional knowledge
• solve this paradox by using local knowledge in your global customer data