Data Cleansing

.Data cleansing process steps that are tobe covered during the performance of the Services are summarized asfollows (in the context of implementation of Oracle fusion HCMcloud solution and moving away from Oracle EBS HR solution):Task # Data Cleansing processSteps Description 1Data Discovery Comprehensive analysis of the standard and customdata in EBS for quality and completeness. Analysis of data outsideEBS but used by HR as part of process (eg email excel trackersetc.) as per Alshaya SMEs inputs. 2Assessment Identify any missing outdated incomplete or inaccuratedata that needs to be enriched. Identify inconsistencies ordiscrepancies in the data or not fit for purpose and mark them assuspense entries. 4 Enrichment Enrich thedata with additional information or attributes required for OracleFusion HCM and rectifyerrors.
Validatethe cleansed data to ensure accuracy and consistency. Proposevarious HCM analytics & reports that can be produced fromthe available data. 3 Standardizing Definedata cleansing strategies and rules to address identified dataquality issues. This may involve standardizing data formatsremoving duplicates filling missing values and resolvinginconsistencies. Develop data cleansing scripts or workflows toautomate the cleansing process where possible.5 Data Mapping Map data from Oracle EBS tocorresponding data structures in Oracle Fusion HCM. Ensure thatdata transformation rules are defined to accommodate differencesand dependencies in data models between the two systems. Validatedata mapping and transformation rules to ensure that data isaccurately mapped and antiquated data is left out.6 Transforming as per Fusion structures Runvarious data transformation scripts using relevant formulas andlookups to change data formats values or structures as perrequirements from Oracle Fusion HCM modules.7 Validating This may involve dataderivation and validation scripts bridging masters data validationchecks reconciliation with source systems and data validation withusers. Analyze impact of change in data with respect to variousrelevant stakeholders. Obtain approval from SMEs on the datachanges. Establish relevance of data to GDPR compliance.8 Socializing Socialize the data andassociated changes with relevant selected stakeholders groups andbring them onboard with new ways of understanding thedata.
Out ofScope 1 Data Content language Only datacontent stored in English will be cleansed data content innonEnglish languages such as Arabic Turkish etc. will be out ofscope. Following activities are out of scopeof the consultant team as these will be performed by the Alshayainternal teams or implementation partner selected by thecompany.
2Data Extraction & data cleansing tool Data extraction fromvarious source systems will be performed by Alshaya IT &Implementation partner. Data cleansing team will use the toolsprovided by Alshaya IT to conduct the exercise provided sufficienttraining to use the same is provided tothem. 3 Data Analytics Analysis of data fromreporting perspective and recommendations for additional datarepresentations (reports dashboards etc) are out of scope and willbe carried out by Alshaya team. 4 Creationof testing data sets Testing data sets covering all business usecases will be out of scope of data cleansing team. Alshaya projectteam will take up this exercise during Design and configurationphase of the project. 5 Data Migration(implementors task) Develop a detailed data migration planoutlining the steps involved in extracting converting transformingand loading data from Oracle EBS to Oracle Fusion Financials.Define roles and responsibilities for data migration activities andestablish timelines for each phase of the migration process.6 Testing and Validation (Implementors task)Conduct thorough testing of the data migration process to validatethe accuracy and completeness of migrated data in Oracle FusionFinancials. Perform data reconciliation between Oracle EBS andOracle Fusion Financials to ensure data integrity and consistency.Get sign off from HCM users on tested data.7 Documentation & Training(Implementors task) Document data quality assurance processincluding data quality rules checklists business rules. Providetraining to relevant stakeholders on data quality processes andbest practices to maintain data quality in Oracle FusionFinancials. 8 Change Management &Governance Create data awareness among wider stakeholders as partof fusion implementation project after the golive. Transfer knowledge about datagovernance practices in order to increase the rate of adoption. #Data Cleansing Stages Resources Engagement for 12 Months 1Discovery Data Cleansers 5 Data Cleansingsupport will be required throughout the project as per Alshaya DataCleansing Strategy to cleanse Master data active data recentrelevant data historical data and relevant transactionaldata
Kindlyread the JD Carefully & then applybutton.
RegardsGurpreetKaur

cleansing,data,testing,validation

---