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 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.
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 |