Data Migration Testing

Infosüsteemide uuendamise ajajärgul puutuvad infosüsteemide arendajad üha enam kokku vajadusega tõsta (migreerida) vana infosüsteemi andmed uue infosüsteemi andmebaasi. Magistritöös selgitatakse migratsiooni testimist ning leitakse võimalusi testide koostamise ja testimise lihtsustamiseks (automatiseerimiseks). Testimise automatiseerimise vajadusest lähtuvalt käsitletakse migratsiooni testi päringute komplektina, kus igasse komplekti kuulub üks isevalideeruv test (päring) ja vähemalt üks vigaseid kirjeid tagastav abipäring. Sellise lähenemisviisiga saab testide käivitamisel kohese ülevaate migreeritud andmete seisust ja vigaste andmete (kirjete) detailvaate saamiseks ei ole vaja kirjutada SQL lauseid. Seega muutub vigade analüüsimine efektiivsemaks, sest väheneb täiendavate päringute koostamise vajadus. Testide koostamise automatiseerimise eelduseks testide sarnasuse alusel testide tüüpidesse jagamine. Töös defineeriti 16 testi tüüpi ja iga testi tüübi jaoks koostati testi päringu mall (template) ja abipäringute mallid (näidispäringud) ning selgitati testi metaandmete vajadust. Lisas toodud testide tüüpide kirjeldused on näidiseks migratsiooni testijatele. Testide kirjelduste alusel saab arendada ka testide päringute koostamise generaatori.In the era of information system upgrades, information system developers are increasingly confronted with the need to upgrade (migrate) the old information system data into the database of the new information system. Master's thesis explains migration testing and finds ways to simplify (automate) test design and testing. Based on the need for automation testing, the migration test is considered as a set of queries where every element include one self-validating test (query) and at least one auxiliary query that returns invalid entries. With this approach, one can instantly view the status of migratory data when one runs the tests, and no need to write new SQL statements to get a detailed view of the incorrect data (records). Thus, the analysis of errors becomes more efficient as the need for new additional queries is reduced.The prerequisite for automation of test preparation is the division of tests into test types based on similarity of tests. In this work, 16 test types were defined, and a template for the test query and auxiliary query templates (pattern queries) were prepared for each type of test and the need for test metadata was explained. The descriptions of the types of tests in the Appendix are an example of migration testing. A test generator can also be developed based on test descriptions

Master datan laadun parantaminen toiminnanohjausjärjestelmän käyttöönoton datamigraatiossa

ERP systems constitute the information system backbone of most organizations across all industries. An ERP implementation is a huge commitment for organizations and too often they are failed or ran over schedule and budget. Poor data is claimed to be the number-one reason for the high failure rate of new computer system implementations. The problem is that the data which is migrated from a legacy system to a target system has poor quality. The aim of this thesis was to study how the master data quality can be improved in the data migration process of SAP ERP implementation. The research was conducted at a general level and specific cases or organizations were not examined. The objective was to compile a list of methods how to improve the master data quality in the data migration process. To achieve the goal the main barriers for the master data quality in the data migration process were also recognized. The research was conducted in two parts: theoretical and empirical. The theoretical section was based on scientific literature about the research title and formed the foundation for the empirical part. The empirical research was conducted as a case study where the qualitative data was collected by interviewing nine SAP consultants. Furthermore, an additional questionnaire was conducted in order to point out the most intrinsic results. The results show that the data migration process includes several data quality barriers which need to be taken into consideration. The barriers were divided into three groups: data, people, and process related barriers. The methods to improve the master data quality in the data migration process were derived from the data quality barriers and they were also divided into the same three groups: data, people, and process relater methods. According to the results the most intrinsic methods to improve the master data quality in the data migration of SAP implementation are taking care of the good engagement with the client, defining and communicating the data related roles and responsibilities unambiguously, analyzing the status of the data in the legacy systems at starting point, arranging and executing the data cleansing carefully already in the legacy system, creating unambiguous data collection templates and carrying out walkthrough for them, and determining SAP rules for data to correspond to the business rules