Introduction

Data migration projects are critical to strategic initiatives yet the success rate across the industry is extremely low. A recent industry survey by Bloor Research discovered that only 16% of data migration projects are typically delivered on time and on budget. Severe delays or failure during the data migration project can have a major impact on strategic objectives. New business systems sit idle as they wait to be populated with data. Competitive advantage is lost and costs begin to soar.

One principle cause of this endemic failure within the data migration industry is the inability to adequately manage the quality of the data assets that require migration. The same Bloor Research report also found that only 10% of organizations surveyed admitted to using data quality tools during their data migration.

This white paper provides practical advice that will help the reader understand the pivotal role data quality technology must play in a data migration. Five distinct implementations of data quality technology are described in detail. Each one provides clear evidence and benefits for the need to adopt the right data quality approach in data migration projects.

Scope, Cost and Timeline Forecasting

The Risk

At the inception of any data migration there is a great deal of uncertainty.

• How many systems do we need to migrate?
• How many business objects will be in scope?
• What type of skilled resources is required?
• How many data quality issues will we face?
• How much time will the business grant to load the data?
• Will the target system be available as planned?

One of the first risks to impact most data migration projects is the failure to gather sufficient information to enable accurate forecasting for the scope, cost and duration of the project.

Without accurate forecasting the following conditions may arise:

• Funding expiring before the project completes because cost estimates are insufficient
• Delivery date not being met because the timelines are unrealistic and the scope ‘creeps’ during the project
• Lack of sufficient personnel at key phases of project because the scoping and resource estimates are poorly understood