Industry - Testing & Quality Assurance in Data Migration Projects


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Paper: Testing & Quality Assurance in Data Migration Projects

Authors: Klaus Haller, Florian Matthes, Christopher Schulz

Session: Industry Track Session 3: Evolution and migration

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Industry - Testing & Quality Assurance in Data Migration Projects

  1. 1. Fakultät für InformatikTechnische Universität MünchenTesting & Quality Assurance inData Migration ProjectsWilliamsburg, 26th of September 2011Klaus Haller2Florian Matthes1Christian Neubert1Christopher Schulz11Lehrstuhl I19 (sebis), Fakultät für Informatik, TU München, Garching, Germany2Swisscom IT Services Finance, Testing & Quality Assurance, Zurich, Switzerland110816_CS_ICSM 2011 1
  2. 2. The author teamSoftware 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, Germany Research areas in Enterprise Architecture Management & Social Software Swisscom IT Services Finance Design, implementation, and operations of IT systems (customer-specific and standard software) and BPO services for ~190 banking & insurance institutions The Testing & QA group offers management and technical consulting, test automation, and testing as a services Christian Neubert Authors PhD student at sebis, Dr. Klaus Haller (Swisscom IT primary research area: Web Services Finance) 2.0 Tools, Hybrid Wikis, Prof. Florian Matthes (TU München) Model driven development Christopher Schulz (TU München) Professional working experience as software engineer in the area of logistics110816_CS_ICSM 2011 2
  3. 3. Mastering data migration projects is achallenging 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]110816_CS_ICSM 2011 3
  4. 4. Definition, drivers, and characteristics of datamigration projects Data migration Tool 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 Drivers Corporate events like mergers and acquisitions or carve-outs Implementation of novel business-models and processes Technological progress and upgrades New statutory and regulatory requirements Characteristics Re-occurring replacement or consolidation of existing business applications Everlasting although infrequently performed discipline Constantly underestimated in size and complexity110816_CS_ICSM 2011 4
  5. 5. 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?110816_CS_ICSM 2011 5
  6. 6. Migration programs rest on an architecture Source staging database Copy of source database to uncouple both databases Transformation database Stores intermediate results of the data migration programs Target staging database Stores the result of the transfor- mation ready for the upload Data migration program Transforms and moves the data & its representation from source to target database Comprises the subprograms extract & pre-filter, transform, and upload Orchestration component Ensures the correct starting order of the programs using a timetable-like mechanism110816_CS_ICSM 2011 6
  7. 7. Migrating data in a stepwise & iterative style Practice-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 programs110816_CS_ICSM 2011 7
  8. 8. A risk model helps to turn vague migrationfears 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 risks110816_CS_ICSM 2011 8
  9. 9. Different testing techniques mitigate the riskoften 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 projects110816_CS_ICSM 2011 9
  10. 10. Systematize the testing-based qualityassurance techniques Data validation Combination of automated and manual comparisons to validate completeness, semantical correctness, and consistency on the structure & data level Completeness and type correspondence tests Automated comparison of all data to identify new or missing business objects Appearance tests Manual comparison of a selection of business objects on GUI level Integration tests Semi-automated tests dedicated to the proper functioning of the target application with the migrated data in context of its interlinked applications Processability test Test focusing on coordinated interplay of target business application and new data Partial/Full Migration run test Semi-automated validation of the data migration programs in part or entirety110816_CS_ICSM 2011 10
  11. 11. Each data migration is risk is covered by adifferent set of testing techniques Risk Testing technique Stability Partial/full migration run test Corruption Appearance test, processability test, integration test Semantics Appearance test, processability test, integration test Completeness completeness & type correspondence Execution risk Full migration run test Orchestration risk Partial/full migration run test Dimensioning Partial/full migration run test Interference Operational risk, no testing Parameterization Appearance test, processability test, integration test, completeness & type correspondence test110816_CS_ICSM 2011 11
  12. 12. Project management-based quality assurance Involve an external data migration team Experienced specialists bring in methodologies, tool support, and know-how Reduce IT management risks of extended delays and overspends Exercise due while perform project scoping Careful scoping in strategy phase applying source-push or target-pull principles Eliminate risk of data and transformation loss Apply a data migration platform Scalable and reusable platform ensures independence from source & target database while providing increased migration leeway for testing measures Mitigate risk of corruption and instability Prevent budget and time overruns Reduce risk of interference between the migration teams Reduce parameterization risk110816_CS_ICSM 2011 12
  13. 13. Project management-based quality assurance Thoroughly analyze and cleanse data In-depth analysis helps to understand the data’s semantics & structure and to seize migration project’s characteristics more accurately Prevent project delays and budget overruns Mitigate the risks of corruption Reduce performance and stability risk for target application Bring down the risk of unstable data migration programs Migrate in an incremental and iterative manner Early and regular generation of migration results ensures a high project traceability and the possibility for frequent adjustments Reduce risk of project failure110816_CS_ICSM 2011 13
  14. 14. 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 database introduces and classifies dedicated risk mitigation techniques and project management practices helping to assure the quality in data migration projects Future directions Empirically evaluate process model, risk mitigation, and project management techniques in practice Examine the case where several source databases have to be consolidated resulting in data migration series Enhance process model with additional data harmonization activities Identify alternative versions of the model and techniques for NoSQL databases110816_CS_ICSM 2011 14
  15. 15. Thank you for your attention! Any Questions?ContactChristian.Neubert@in.tum.deChristopher.Schulz@in.tum.deKlaus.Haller@swisscom.comFurther information 2011 15