Data Analysis

Depending on the agreed service requests and expected outcomes, the Service may include:

• Analysis of data available in operational systems and/or the DataLake, in connection with business needs and with input from architects and ABA stakeholders.
• Administration of physical or virtual data ingestion into the DataLake where required for the agreed deliverables.
• Construction of analytical models appropriate to the business need.
• Direct implementation of dashboards or provision of documented technical and functional guidance for their construction.
• Specific or exploratory ad-hoc analyses using SQL, R, Python and other agreed technologies.
• Use of data analysis techniques in Python and/or Scala and application of relevant statistical methodologies.
• Use, as applicable, of BI tools such as Looker, SAP SAC, Tableau, QlikView or Qlik Sense.
• Use, as required, of GCP capabilities including BigQuery, Airflow, DataProc and AI services, together with Git for version control.
• Preparation and execution of manual functional testing and maintenance of traceability through Jira, Confluence and Xray.
• Delivery in an Agile context and professional collaboration in English at the level required for the Service.
• The Services are delivered as outputs and results, not as individual roles or positions. The Supplier remains fully responsible for organizing its resources, choosing its working methods, ensuring service capacity and applying its internal governance.

Unless added through an agreed change request, the scope excludes operational application development, administration of the Beneficiary infrastructure, final architecture decisions and activities unrelated to the agreed data analysis deliverables. Detailed boundaries are to be completed and contractually validated.

• Data analysis report stating the objective, data sources, assumptions, method, results and limitations.
• Specific or exploratory ad-hoc analysis with conclusions and traceability evidence.
• Notebooks, SQL queries, Python/R/Scala scripts or other analytical artefacts where included in the service request.

• Physical or virtual ingestion specification covering source, destination, transformation rules and controls.
• Configured and validated DataLake ingestion where required.
• Ingestion control report covering errors, exceptions and corrective actions.

• Analytical model aligned with the stated business need.
• Documentation of variables, rules, assumptions and limitations.
• Functional and, where applicable, statistical validation results.

• Implemented dashboard or documented specification and guidance for its construction.
• Definitions of indicators, calculation rules, sources and reconciliation criteria.
  Functional test evidence and validation result.

Documentation and Governance Deliverables

• Technical and functional documentation maintained in Confluence or the agreed repository.
• Traceability records in Jira and/or Xray.
• Periodic Service report on deliverables, blockers, risks, dependencies and corrective actions.

• Manual functional test plan and test evidence.
• Non-conformity register and remediation evidence.
• Handover package for accepted deliverables, including code, documentation and usage instructions where applicable.

Originally posted on Himalayas