Data Cleansing
Data Cleansing
Posted on :
07-09-2024
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1 Vacancy
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Job Description
.Data cleansing process steps that are to be covered during the performance of the Services are summarized as follows (in the context of implementation of Oracle fusion HCM cloud solution and moving away from Oracle EBS HR solution):
Task # Data Cleansing process
Steps Description
1 Data Discovery Comprehensive analysis of the standard and custom data in EBS for quality and completeness. Analysis of data outside EBS but used by HR as part of process (eg email excel trackers etc.) as per Alshaya SMEs inputs.
2 Assessment Identify any missing outdated incomplete or inaccurate data that needs to be enriched. Identify inconsistencies or discrepancies in the data or not fit for purpose and mark them as suspense entries.
4 Enrichment Enrich the data with additional information or attributes required for Oracle Fusion HCM and rectify errors.
Validate the cleansed data to ensure accuracy and consistency. Propose various HCM analytics & reports that can be produced from the available data.
3 Standardizing Define data cleansing strategies and rules to address identified data quality issues. This may involve standardizing data formats removing duplicates filling missing values and resolving inconsistencies. Develop data cleansing scripts or workflows to automate the cleansing process where possible.
5 Data Mapping Map data from Oracle EBS to corresponding data structures in Oracle Fusion HCM. Ensure that data transformation rules are defined to accommodate differences and dependencies in data models between the two systems. Validate data mapping and transformation rules to ensure that data is accurately mapped and antiquated data is left out.
6 Transforming as per Fusion structures Run various data transformation scripts using relevant formulas and lookups to change data formats values or structures as per requirements from Oracle Fusion HCM modules.
7 Validating This may involve data derivation and validation scripts bridging masters data validation checks reconciliation with source systems and data validation with users. Analyze impact of change in data with respect to various relevant stakeholders. Obtain approval from SMEs on the data changes. Establish relevance of data to GDPR compliance.
8 Socializing Socialize the data and associated changes with relevant selected stakeholders groups and bring them onboard with new ways of understanding the data.
Out of Scope
1 Data Content language Only data content stored in English will be cleansed data content in nonEnglish languages such as Arabic Turkish etc. will be out of scope.
Following activities are out of scope of the consultant team as these will be performed by the Alshaya internal teams or implementation partner selected by the company.
2 Data Extraction & data cleansing tool Data extraction from various source systems will be performed by Alshaya IT & Implementation partner. Data cleansing team will use the tools provided by Alshaya IT to conduct the exercise provided sufficient training to use the same is provided to them.
3 Data Analytics Analysis of data from reporting perspective and recommendations for additional data representations (reports dashboards etc) are out of scope and will be carried out by Alshaya team.
4 Creation of testing data sets Testing data sets covering all business use cases will be out of scope of data cleansing team. Alshaya project team will take up this exercise during Design and configuration phase of the project.
5 Data Migration (implementors task) Develop a detailed data migration plan outlining the steps involved in extracting converting transforming and loading data from Oracle EBS to Oracle Fusion Financials. Define roles and responsibilities for data migration activities and establish timelines for each phase of the migration process.
6 Testing and Validation (Implementors task) Conduct thorough testing of the data migration process to validate the accuracy and completeness of migrated data in Oracle Fusion Financials. Perform data reconciliation between Oracle EBS and Oracle 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 process including data quality rules checklists business rules. Provide training to relevant stakeholders on data quality processes and best practices to maintain data quality in Oracle Fusion Financials.
8 Change Management & Governance Create data awareness among wider stakeholders as part of fusion implementation project after the go live.
Transfer knowledge about data governance practices in order to increase the rate of adoption. # Data Cleansing Stages Resources Engagement for 12 Months 1 Discovery Data Cleansers
5 Data Cleansing support will be required throughout the project as per Alshaya Data Cleansing Strategy to cleanse Master data active data recent relevant data historical data and relevant transactional data
Kindly read the JD Carefully & then apply button.
Regards
Gurpreet Kaur
cleansing,data,testing,validation
Employment Type
Full Time
Key Skills
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