Fakultät für Informatik Technische Universität München

Testing & Quality Assurance in Data Migration ProjectsWilliamsburg, 26th of September 2011

Klaus Haller2

Florian Matthes1

Christian Neubert1

Christopher Schulz1

1Lehrstuhl I19 (sebis), Fakultät für Informatik, TU München, Garching, Germany2Swisscom IT Services Finance, Testing & Quality Assurance, Zurich, Switzerland

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The author team

Software Engineering for Business Information Systems (sebis)Prof. Dr. Florian Matthes is holder of the chair Software Engineering for Business Information Systems (sebis) at the TU München, GermanyResearch areas in Enterprise Architecture Management & Social Software

Swisscom IT Services FinanceDesign, implementation, and operations of IT systems (customer-specific and standard software) and BPO services for ~190 banking & insurance institutionsThe Testing & QA group offers management and technical consulting, test automation, and testing as a services

PhD student at sebis, primary research area: Web 2.0 Tools, Hybrid Wikis, Model driven developmentProfessional working experience as software engineer in the area of logistics

Dr. Klaus Haller (Swisscom IT Services Finance) Prof. Florian Matthes (TU München)Christopher Schulz (TU München)

Christian Neubert Authors

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Mastering data migration projects is a challenging task

„83% of data migrations fail outright or exceed their allotted budgets and implementation schedules.“

[Gartner Group, 2005]

”..current success rate for the data migration portion of projects (that is those that were delivered on time and on budget) is just 16%.”

[Bloor research, 2007]

“Few companies have the necessary skills to manage, build and implement a successful data migration.”

[Endava, 2007]

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Definition, drivers, and characteristics of data migration projects

Data migrationTool supported one-time process which aims at migrating formatted data from a source structure to a target data structure whereas both structures differ on a conceptual and/or technical level

DriversCorporate events like mergers and acquisitions or carve-outsImplementation of novel business-models and processesTechnological progress and upgradesNew statutory and regulatory requirements

CharacteristicsRe-occurring replacement or consolidation of existing business applicationsEverlasting although infrequently performed disciplineConstantly underestimated in size and complexity

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Research focus

How does a comprehensive process model for migrating data to relational databases looks like?

What are risks frequently occurring in context of data migration projects and is there an appropriate classification scheme helping to structure them?

Which dedicated testing and risk mitigation techniques cope with these issues from a technical and organizational point of view?

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Migration programs rest on an architecture

Source staging databaseCopy of source database to uncouple both databases

Transformation databaseStores intermediate results of the data migration programs

Target staging databaseStores the result of the transfor-mation ready for the upload

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Data migration programTransforms and moves the data & its representation from source to target databaseComprises the subprograms extract & pre-filter, transform, and upload

Orchestration componentEnsures the correct starting order of the programs using a timetable-like mechanism

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Migrating data in a stepwise & iterative stylePractice-proven process model consists of 4 main stages which are subdivided into 14 distinct phases

1. Initialization, prepares the necessary infrastructure and organization

2. Development, implements the actual data migration programs

3. Testing, validates the correctness, stability, and execution time of both, data and migration programs

4. Cut-Over, finally switches to the target application by executing the migration programs

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A risk model helps to turn vague migration fears into concrete risks

Shaped like a house, the model is subdivided into • business risks often articulated by the customer,• IT management risks with a technical focus, and• data migration risks covering issues associated with migration programs

Business and IT management risks are abstract but map on data migration risks

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Different testing techniques mitigate the risk often emerging in data migration projects

Concrete testing techniques, their explicit mapping on risks, as well as dedicated testing phases assure the quality of data migration projects

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Systematize the testing-based quality assurance techniquesData validation

Combination of automated and manual comparisons to validate completeness, semantical correctness, and consistency on the structure & data level

Completeness and type correspondence testsAutomated comparison of all data to identify new or missing business objects

Appearance testsManual comparison of a selection of business objects on GUI level

Integration testsSemi-automated tests dedicated to the proper functioning of the target application with the migrated data in context of its interlinked applications

Processability testTest focusing on coordinated interplay of target business application and new data

Partial/Full Migration run testSemi-automated validation of the data migration programs in part or entirety

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Each data migration is risk is covered by a different set of testing techniques

Risk Testing techniqueStability Partial/full migration run test

Corruption Appearance test, processability test, integration testSemantics Appearance test, processability test, integration test

Completeness completeness & type correspondence Execution risk Full migration run test

Orchestration risk Partial/full migration run testDimensioning Partial/full migration run testInterference Operational risk, no testing

Parameterization Appearance test, processability test, integration test, completeness & type correspondence test

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Project management-based quality assurance

Involve an external data migration teamExperienced specialists bring in methodologies, tool support, and know-how

Reduce IT management risks of extended delays and overspends

Exercise due while perform project scopingCareful scoping in strategy phase applying source-push or target-pull principles

Eliminate risk of data and transformation loss

Apply a data migration platformScalable and reusable platform ensures independence from source & target database while providing increased migration leeway for testing measures

Mitigate risk of corruption and instabilityPrevent budget and time overruns Reduce risk of interference between the migration teamsReduce parameterization risk

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Project management-based quality assurance

Thoroughly analyze and cleanse dataIn-depth analysis helps to understand the data’s semantics & structure and to seize migration project’s characteristics more accurately

Prevent project delays and budget overrunsMitigate the risks of corruptionReduce performance and stability risk for target application Bring down the risk of unstable data migration programs

Migrate in an incremental and iterative mannerEarly and regular generation of migration results ensures a high project traceability and the possibility for frequent adjustments

Reduce risk of project failure

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Summary and outlook

To deliver a data migration project in time and on budget, a stringent approach, proactive risk mitigation techniques, and distinct test activities are required

This contribution…outlines a practice-proven process model describing how to proceeded when shifting data from a source to a target databaseintroduces and classifies dedicated risk mitigation techniques and project management practices helping to assure the quality in data migration projects

Future directionsEmpirically evaluate process model, risk mitigation, and project management techniques in practiceExamine the case where several source databases have to be consolidated resulting in data migration seriesEnhance process model with additional data harmonization activitiesIdentify alternative versions of the model and techniques for NoSQL databases

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Thank you for your attention!

ContactChristian.Neubert@in.tum.deChristopher.Schulz@in.tum.deKlaus.Haller@swisscom.com

Further informationhttp://wwwmatthes.in.tum.de/wikis/sebis/mergers-and-acquisitionshttp://finance.swisscom.com/

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Any Questions?

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