DATA MANAGEMENT

In line with best practices for Data management, UMB is implementing a robust comprehensive Data management strategy that consist of; (1) Data managements plans (DMPs) that describe how each Implementing Partner achieves Data collection / generation, Data management, documentation and curation, Data security, Data sharing and access; (2) Standardized Data Quality Assessments that will be used by all Implementing partners to validate reported achievements in line with PEPFARS accountability framework; and (3) Standardized Data Quality Improvement (DQI) approach that guides decisions centered on data quality goals to determine the data processes to improve, solutions to implement, and people to engage.

Data Management Plan

Data management plan is a document that describes how data will be collected, documented, described, shared, and preserved over the lifespan of a project. Through series of stakeholder require gathering meetings with guidance from CDC Nigeria, UMB developed a Data Management Plan that seeks to first collect information around existing Data Management Standards among all Implementing Partner, second harmonize and evaluate all response to CDC Nigeria’s expectation and third provide feedback on Data Management Plans to be adopted and maintain by all Implementing partners.

In year 1 (2016 – 2017), the Data Management plan developed to achieve the first step consist of 8 sections

Section		Brief description of section
1	Project profile	Implementing partner and project details
2	Program objective	Project aims and objectives
3	M & E systems	People, process, equipment, environment
4	Data processes	Data and report types
5	Quality Assurance	Data verification types
6	Data storage	Storage, access and sharing
7	Data Copyright	Intellectual Property Copyright and Ownership
8	Data retention	Post Project Data Retention Sharing and Destruction

Benefits of a DMP

A comprehensive documentation of all data related activities, process and procedures that will occur during the targeted project period
Serves as a guide that demonstrates reproducibility and accountability
Demonstrates the intrinsic value of data collected over time

Data Quality Assessment (DQA)

Data Quality Assessment is a targeted process that evaluates data to determine if the required quality standard is achieved. Data Quality is largely dependent on various dimensions (validity, reliability, timeliness, precision and integrity).

UMB has develop a DQA strategy that follows the PEPFAR MER reporting process to ensure that all reported data is validated in harmonized manner that results in comparism and Benchmarking among Implementing partners, health facilities and indicators. The strategy involves the triangulation of data captured onsite at Service Delivery points to monthly program reports, quarterly reports submitted on DATIM, and the RADET.

A tool is made available as a downloadable excel format which can easily uploaded when completed for determine results.

Data Quality Improvement (DQI)

The Data Quality improvement strategy will combine best practices in quality improvement that will guide the implementation and monitoring of sustainable solutions to identified data quality issues.

UMB’s DQI strategy is guided by the, four approaches and the four steps of QI while incorporating applicable QI tools within each step. As Data quality issues can be remediated at any level, UMB has create DQI tools that can be used by Health Facilities, Implementation partners to start, guide and monitor improvements

Approach	Steps
	1	2	3	4
	IDENTIFY – determine what to improve	ANALYZE – understand the problem	DEVELOP – hypothesize changes to improve problem	TEST/IMPLEMENT (PDSA) – test hypothesized solution; take action
Individual Problem Solving	Individual decision making for a small problem that is not interdependent on others	Relies on individual analysis, using existing data, observation, and intuition	The change is usually minor and not interdependent on others	“Trial and error” approach to testing
Rapid Team Problem Solving	An ad hoc team identifies an intuited or obvious problem based on intuition, observation, and existing data	Generally requires minimal analysis using mainly existing data and group intuition	A series of small changes	Many small to medium tests in similar systems
Systematic Team Problem Solving	An ad hoc team addresses a complex, recurring problem	The team examines the problem to try to identify its root causes; existing data and/or data collection is used	Generally a large change that addresses the root cause of the problem	Generally requires extensive testing before implementation
Process Improvement	A permanent team addresses a core process or issue in a large process or system	Requires detailed process knowledge from on-going data collection and monitoring	A change in a key process	Depends on the approach used and the magnitude of the change; permanent teams continue to monitor and improve the process