A recommended methodology for reducing the risks involved in data migration.

1. Migrating data: How
to reduce risk
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2. Data migration: A risk!

3. A real-life example of the risks involved
We were called in to rescue a project in which tools had
not been used:
• The vendor hand-wrote the migration
to target system
• Rewritten three times due to:
• Structural limitations
• Performance (twice)
• Bugs in code delayed the migration
• Bugs in code caused incorrect data,
making it difficult to test actual migration
logic

4. Recommended best practice
 Business ownership: transparency,
control, engagement
 Use of toolsets
 Use of a project management
methodology
 A team with skills and experience in
data migration
 Use standards wherever applicable
 Use of a data migration methodology

5. Recommended team leader objectives
 Identify and mitigate risks
 Ensure confidence in the project
team
 Appropriate resourcing
 Keep the project on track:
 Within budget
 On time
 Reduce the overall cost and effort
needed

6. Overview of our recommended data migration process
Business
engagement
Scoping Core Legacy
migration decommissioning
Assessments

7. Embed best practice within the process
• Migration methodology: an in-depth process
for each stage of the data migration, bringing
in specialists at key times and working to a
structured plan and documents
• Project management methodology: We use
PRINCE2 to ensure that the migration is well
managed to a common standard
• ISO standards: We use 15288,12207,and
8000 to ensure the methodologies have a
core standard structure
• Migration software: The core migration steps
should be carried out using a well established
tool
• Migration specialists: Specialists should be
available for the duration of the project

8. Data migration process: Assessments
Staff competency assessment Project assessment
• Helps to identify gaps in the • A strategic review of the
existing skill base proposed project, examining:
• Shows where training may be • Plans
required • Processes
• Reduces reliance on external • Workflows
experts • Data targets
• Grows the confidence of the • Systems
project team • Identifies and mitigates risks
• Provides individuals with greater and issues before they occur
clarity about their roles
• Our proprietary Data Migration
Competency Framework is
designed specifically to support
data migration teams

9. Data migration process: Scoping
Project scoping Technical scoping
• A detailed, tactical examination • A detailed examination of the
of the proposed project, project’s technical structure:
including: • Available models
• Stakeholders and deliverables • Available software
expected • Data volume and quality
• Budgets
• Identifies potential technical
• Deadlines issues before they occur
• Communication plans
• Available experts: business
domain, systems, migration
experts
• Helps with project planning to
mitigate risk
• Provides business leaders with
a clear view of the project plan

10. Data migration methodology
Iterative agile development is used throughout
Project scoping
Core migration
Configuration
Landscape analysis Data assurance Migration design Migration development
Requirements analysis Data discovery Data review
Testing design Testing development
Data modelling Data cleansing
Execution
Review
Legacy decommissioning

11. Landscape analysis in more detail
• Landscape analysis encompasses the
systematic process of identifying all
source and target systems that may be
involved in the data migration
• Gain an overview of the source and
target systems
• Key tasks:
• Understand how each system works
• Understand how the data within each system
is structured
• Model the systems
• Model links and interactions between systems
• Model data structures

12. Data assurance in more detail
• Data assurance puts measures
in place to ensure that all Data assurance questions:
information used within the • What data migration and
data migration is handled profiling tools are available?
accurately • Are there key areas of
• Data quality is a key element, weakness in data?
along with data cleansing • Are rules for the data quality
where required (attribute and relational) within
• Planning is required for the the source already
documented?
retirement of data, for deletion
• Which governing rules have to
or for storage due to industry
be applied to the data?
regulations
• Will all data be migrated?
• Key tasks:
• Data review
• Data cleansing

13. The key data assurance tasks
Data review Data cleansing
• Profiling is carried out to identify • Define the cleansing rules which
areas of the data that may not will be carried out manually and
be of sufficient quality to meet those which will be automated:
business requirements • The manual cleansing will be
• Data quality definitions define typically be carried out prior to
the quality that must be attained migration
for elements, attributes and • The automated cleansing will be
relationships within the source carried out as a first step of the
migration or indeed may also be
system; these definitions will be
able to be carried out prior to
used during the profiling to migration
verify if the data adheres to the
• Data verification is focused on the
rules defined
checking of data being available,
• The system retirement plan accessible, in the correct format
defines which data will be and complete
moved from the old system to
• Data impact analysis is carried
the new and what is no longer
out to evaluate the effect on other
required
elements and systems

14. What happens when data assurance is omitted?
• Multinational 3-way multi-company venture
• US vendor migrating data
• UK project management
• No migration management
• Lack of resource
• No verification or quality rules
• No profiling carried out
• Ad-hoc testing
• Security delays of 6 months
• Testing time increased by 3 months
• 1 month delay for invalid characters
• Additional dry-run required due to issues encountered

15. Core migration in more detail
• Use a structured approach with methodologies to guide the
process
• Establish clear structures and boundaries: a methodology will
help with this
• Ensure tool-based execution: we use our own commercially
available software, Transformation Manager
• Carry out tool-based testing
• Create an organisation-focused go live: scoping the project at
an early stage can assist in disseminating the information to
stakeholders around timings and resource required to achieve
the goals

16. A core migration design

17. Our offerings for data management
Transformation Data
Manager data migration
movement packaged
software services
Support,
training and
mentoring
services

18. Why Transformation Manager?
For the user:  Everything under one roof
 Greater control and
transparency
 Identify and test against errors
iteratively
 Greater understanding of the
transformation requirement
 Automatically document
 Re-use and change
management
 Uses domain specific
terminology in the mapping

19. Why Transformation Manager?
For the business:  Reduces cost and effort
 Reduces risk in the project
 Delivers higher quality and
reduces error
 Increases control and
transparency in the
development
 Single product
 Reduces time to market

20. Contact us for more information:
Karl Glenn, Business Development Director
kg@etlsolutions.com
+44 (0) 1912 894040
Read more on our website:
www.etlsolutions.com
Raising data
management
standards
www.etlsolutions.com
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