The project aims to analyse and improve the data quality of Kenya's National Social Security Fund (NSSF), including applying as many immediate remedial measures as possible, and to propose ongoing processes for data quality governance and management to ensure continuous data quality.
Objectives
The objective of the assignment is to analyse and improve the data quality of the National Social Security Fund (NSSF), including applying as immediate remedial measures as possible and proposing ongoing processes for data quality governance and management to ensure continuous data quality. The project will result in a data quality assessment, an improvement plan, and a digital transformation and data governance strategy and roadmap.
Activities
A1. Review and map data landscape, data catalogues, formats, sources, forms, and means of data access and exchange across the organization, and with external parties.
A1.1. Review data and records management policies (data collection, storage systems).
A1.2. Integrate data into one data system for analysis.
A1.3. Define core information and data needs and compare with the existing data sets for validity and fit for purpose. Define a revised master data dictionary (inventory and profile of core business non- transactional data) and key transaction data dictionary.
A1.4. Specify a data quality measurement model (dimensions1 and metrics, see below)
A1.5. Measure data quality against defined data quality dimensions Document the results and analyse, especially the issues and pain points, the impacts, and the root causes of problems.
A2. Specify data quality goals and indicators for specific categories of master data.
A2.1. List and schedule actions to address the root causes of data quality issues.
A2.2. Improvement actions address key dimensions of data quality.
A2.3. Define and write a data quality improvement plan.
A3. Implement concrete and actionable data cleansing operations to improve data quality.
A3.1. Write a report on the measures implemented.
A4. Define and document accountability, roles and responsibilities for data quality
management.
A4.1. Specify data quality business rules to be implemented including routines for measuring and correcting data quality issues
Improvement actions address key dimensions of data quality .
A4.2. Define actions over a data cycle: data collection, storage, retrieval, and analysis for management information systems and business Intelligence.
A4.3. Develop a data quality risk assessment including preventive measures to address acceptable levels of risks.
A4.4. Define procedures for the management of incidents relating to the quality of the master data.
A4.5. Support the formation of data quality team, and train/coach senior management and quality data support team on structures, procedures, and routines for managing data quality issues.
A4.6. Evaluate and propose appropriate industry standard trainings that would help the Fund implement the data strategy both in the short and long term.
Outputs
D1. Presentation on international experiences on digital transformation of social security
D2. Presentation on International experiences on IT governance in public/social security institutions including interoperability dimension
D3. Four technical documents presenting inputs/peer review of the Digital Strategy and Digital transformation road map and its final document
