Reliable data is the foundation and engine of good programming as it leads to informed decisions and trustworthy results. Increasingly, development programming is emphasizing the importance of data utility and validation — from understanding the operating context and clients’ needs to accurately documenting progress, lessons, and achievements. In this blog, we will discuss the importance of data validation and how the GROW2 Project incorporates validation exercises to ensure effective development outcomes and reporting.
Data validation is the systematic process of monitoring and verifying data to ensure that it meets specific quality criteria such as accuracy, completeness, and consistency. It entails reviewing and verifying data in the context of its intended use and determining whether it is suitable for the desired purpose depending on the project or organizational needs. By formalizing the requirements for data validation, project teams communicate these criteria clearly, ensuring that everyone engaged understands the quality standards that must be followed. This practice also ensures the automation of decision-making processes and maintains data quality over time. However, not all components of data validation can be automated. Manual examination and intervention are required in some circumstances to ensure that the data is accurate.
At MEDA, we understand the importance of data quality in project management. In this regard, our Impact and Knowledge Management (IKM) System includes a robust process of checking data accuracy, consistency, and completeness using established structures that satisfy quality standards and requirements. In the proceeding sections, we’ll explore the importance of good and verified data, the different types of data validation techniques and tools GROW2 employs, and the best practices we’ve adopted for implementing data validation in our work.
Why Validate Data?
Implementing an effective data validation strategy is critical for maintaining data integrity and fostering trust within the donor community and among industry stakeholders. MEDA donors like Bill and Melinda Gates Foundation, GAC and USAID prioritize Data Quality Assessments (DQA); and data validation is an integral part of DQA processes. By ensuring data accuracy, reliability, and consistency, organizations uphold the highest standard of data quality and reporting thereby enhancing their credibility.
Data validation serves as a safeguard
Against the occurrence of inconsistencies and errors during data gathering or management processes. It also minimizes risks, when these errors or inconsistencies are identified early on, enabling corrective actions to be taken in a timely manner.
Data validation enables accuracy and reliability
In forecasting and making informed decisions regarding resource allocation. The practice ensures that data is correctly formatted, stored; and complete, i.e., the dataset includes values for all the required fields; and the data is unique, i.e., it is free from duplicates and dummy entries.
Data validation increases project management efficiency and effectiveness
Complete, aligned, and accurate data lead to efficiencies, which positively impact the project and organization. The practice also allows management to assess the appropriateness of the project strategy, identify potential risks, set realistic targets, and ensure project outputs are on track. When data quality is poor, it leads to inefficient processes, increased costs, and poor project outcomes. By validating data, project managers can identify areas for improvement and take corrective action to improve data quality –leading to more accurate forecasting and improved resource allocation.
As the volume and complexity of data increases it becomes even more important to implement robust data validation techniques and systems to plug and rectify errors and inconsistencies in the dataset. Employing solely traditional manual approaches for data validation may be impractical and time-consuming. This is where automated data validation proves valuable, allowing for efficient handling of large-scale data by leveraging data validation frameworks, tools, or programming languages designed especially for large-scale data processes.
Data validation improves the quality of reporting
It ensures that the data aligns with desired indicators and targets, which will contribute to improved reporting. When data is not aligned, it can result in incorrect information being shared and used to make important decisions, which can lead to reputational risks.
The donor community places great importance on data quality, as it has a direct correlation with their confidence in an organization’s ability to achieve intended outcomes. Prioritizing data validation demonstrates an organization’s commitment to delivering reliable and evidence-based results. This, in turn, strengthens relationships with donors, enhances transparency, and increases the likelihood of continued funding support.
Moreover, participants in the industry including other INGOs, LNGOs, agencies of government, and research institutions, rely on accurate and trustworthy data for collaboration, benchmarking, and informed policy-making. By implementing robust data validation practices, organizations can establish themselves as reliable partners and contribute to a more credible and effective sector-wide data ecosystem.
GROW2 Data Validation Techniques and Process
At GROW 2, we understand that data validation is an important and ongoing process. To ensure that our data is accurate and reliable, we use a two-pronged approach that includes both Ex-Ante and Ex-Post procedures. The Ex-Ante strategy involves building logical strings and branching/condition clauses into our programmed e-data collection tool. This allows us to validate the appropriateness of various aspects including, the data collection tools, research design, sampling approach. The data flow and queries are generated for resolution as well as logistical adequacy including human resources.
GROW2 Data Validation Process
1. Data Collection Tool Design
- Research design
- Sampling approach
- Building logical strings and branching/condition clauses into our program data flow
- Tool pre-testing in communities outside the zone of influence with similar characteristics to the zone of influence
2. Enumerators Readiness
Recruitment of enumerators. Training includes:
- Intensive hands-on training
- Role-play with practical scenarios
- Exam and selecting enumerators
- Pre-testing and debriefing
3. Data Collection
- Identify, schedule and interview clients
- Off-site monitoring
- On-site monitoring including re-interviewing and observing interviews
4. Data Management
- Data cleaning
- Data analysis
- Data visualization
5. Validation Workshop
- Data validation framework
- Data review and assessment
- Data validation exercise
- Collaborative discussion
- Action plan
At GROW2, we employ two stages of pre-testing to ensure the data collected will be valid. First, before training and deployment of field officers, we conduct pre-tests in communities outside our zones of influence, which have similar characteristics to our target communities. During this stage, we test the appropriateness of our questionnaire items with the objective of diluting culturally sensitive questions, determining the time taken to complete an interview, identifying resources requirement, and effective community engagement approach. The second pretest is conducted during the training of field officers/enumerators and serves to confirm solutions derived from the initial pre-test. By conducting the second pre-test, we ensure that the adjustments are effective and aligned with the project and research objectives.
At GROW2, the field officers play a significant role in executing this strategy. We prioritize their training as the first quality control measure. Ensuring that field officers are well-prepared and better equipped for their role as data collectors, they are taken through a week-long intensive in-house training with role play and practical scenarios to improve interaction with clients and strengthen their skills to identify and address data quality issues in real time. One key feature of our training is the inclusion of exams to assess the understanding and knowledge retention of field officers, enabling us to address any gaps in knowledge. Our trainings are concluded with a debriefing session. In the field, these officers are equipped with tablets that have internet connectivity, enabling them to capture data digitally. In addition, they are provided with motorbikes to facilitate their movement within their assigned districts of operations1. On average, each field officer monitors 1,500 clients in 18 communities. This extensive coverage allows field officers to effectively carry out their responsibilities across a wide geographic area.
The conduct of on and off-site monitoring of data collection is also integral. This allows us to make corrections and adjustments promptly, preventing the accumulation of errors or inconsistencies that may impact the quality of the final data. We hold discussions across the teams via WhatsApp and field visits, focusing on the issues that emerge from the first two days of fieldwork as a first step. We work together to proactively identify solutions and communicate them in real time, ensuring a seamless and continuous process of checks and corrections throughout the data collection period.
We conduct monitoring visits to observe the data collectors as they conduct interviews, paying particular attention to their questioning and probing skills and the overall interactivity of the conversation. Additionally, we sample clients and conduct re-interviews to confirm the responses captured by the data collectors.
Off-site monitoring leverages WhatsApp platforms, for receiving and addressing emerging issues from the field in real-time. Regular checks of synched data via a dedicated server help us identify and address errors or inconsistencies in our data and maintain high standards of quality.
The Ex-Post strategy
This is another critical component of our data quality control. This approach involves reviewing and verifying data after it has been collected to ensure its accuracy and consistency.
Our data cleaning principle is anchored on two fundamental considerations: the imperative of transparency and accountability to our stakeholders and donors. To uphold these principles, we meticulously implement rigorous quality assurance checks throughout the data cleaning and processing stages. Our data cleaning process commences with a comprehensive assessment of the raw data, where we identify and address various issues that may compromise its quality and integrity. This includes missing values, outliers, duplicate submissions, as well as inconsistencies in responses or data types.
To ensure thorough data cleaning, we use Excel and STATA. Excel enables us to identify and resolve spelling errors and ensure the correctness of qualitative responses. while STATA is employed to identify and rectify missing data, outliers, and inconsistencies in responses. In cases where unresolved issues are encountered, we engage our field officers to resolve identified issues before concluding data collection in their respective communities. These practices streamline the identification and resolution of issues, thereby enhancing the accuracy and reliability of the data.
Stakeholders in the data validation process
GROW2’s data validation process also involves engaging various stakeholders to gather feedback and insights. We begin by reviewing the data with subject matter experts and end-users to gain a better understanding of the context and any potential issues. We involve stakeholders to ensure that the data reflects the perspectives and experiences of all involved. This exercise allows us to revisit initial assumptions and update them based on emerging trends or social, political, and economic changes.
First, we facilitate a debriefing workshop with the data collectors and their supervisors before data analysis commences. The purpose of this workshop is to identify any issues that may have arisen during the data collection process. We specifically focus on documenting any issues that were not previously documented, such as difficulties in obtaining certain types of data or unexpected challenges encountered in the field the digital tool could not record.
Next, to ensure transparency and open communication, we involve the Project Technical Advisory Committee (PTAC) and the Project Steering Committee (PSC) in the validation process. By involving these committees, we aim to maintain a high level of transparency and gather valuable input from stakeholders throughout the validation process. The Committees have similar objectives, namely to:
- Establish and foster a networking platform to effectively communicate project goals, key activities, and accomplishments.
- Provide technical support, ensuring the optimization of available resources.
- Serve as a validation mechanism for the project’s methods, solutions, and overall performance.
Our data validation process goes beyond just checking for accuracy and reliability. We also strive to ensure that the data is up-to-date and relevant. This is accomplished by regularly reviewing and updating our data sources and engaging with stakeholders to gather feedback on the usefulness of the data.