CHALLENGES
MONITORING AND EVALUATION - COMMON PITFALLS
Many tools are readily available to manage monitoring and evaluation, data collection, data analysis and impact reporting. These tools do offer some distinct advantages to small-scale organizations but does not scale well when the data is collected from different sources for multiple programs. As most organizations need a common data warehouse to store all of their information so seamless continuous data learning and reporting to happen.
While many nonprofits and social enterprises collect data, most organizations trip up to derive insights from the collected data, which gets worse when data is collected continuously. There is no well-established process to turn the data from raw data to insightful information.
When evaluating the pricing of a solution, it is important to think about the different factors that affect the cost.
- Cost for the tool
- Cost to train people to use the tools
- Hiring people with special skills on specific products you may be using
- The time it takes to set up the entire process - for continuous learning and improvement.
- Customization to suit specific needs
The perfect tool selection integrates with a well-designed data strategy and can effectively improve your data's accountability and insights. This makes continuous learning easier and drastically simplifying the process of reporting direct impact specific to each funder with whom you engage.
Managing end-to-end monitoring and evaluation or social impact measurement requires four major steps.
Managing end-to-end monitoring and evaluation or social impact measurement requires four major steps.
Organizations commonly use different tools for monitoring and evaluation or impact measurement. Using different tools can be effective in smaller-scale operations but it becomes quite cumbersome for an organization running multiple projects in multiple locations. So, juggling with these different tools(such as online AND offline data collection, excel for aggregation, and Tableau for reporting) can be extremely challenging to establish a robust learning and improvement process. In most cases, these tools are way more sophisticated than one may need, resulting in unnecessary complexity and learning curves when applying them to your specific use case.
Now we can move on to Impact Strategy. Often most organizations work with impact practitioners to define the theory of change or logic model. The theory of change or logical framework is not just diagrams that they can agree upon. Still, it needs to be a living and breathing process throughout the impact management lifecycle - from strategy, data collection, data analysis, and reporting. This is a process that they use for continuous learning and improvement. Having identified measurable impact metrics is critical, organizations can start with activities and output with a clear goal to align them with measuring core outcomes.
These metrics are merely measurement elements, but a well-defined strategy that first identifies WHO, i.e., the kind of community or the stakeholder the organization is reaching out to, WHAT impact or outcome will they focus on, HOW much outcome, i.e., depth and scale of impact, and what CONTRIBUTION of organizations and impact RISK if intervention is not carried forward.
Identifying measurable impact metrics is just one of the steps. Organizations, especially with a larger scale of operations, need to develop a systematic process to make the strategy actionable through systematic data collection and impact data analytics.
Next up, Mobile Data Collection: This may sound obvious, but organizations that serve people in parts of the world with choppy internet connections need to use mobile offline data collection tools. It is quite surprising to see well-established organizations using paper-based data collection practices resulting in a massive waste of time and resources. This can be solved very easily using tools such as KoboToolBox.
Offline mobile data collection reduces data entry pain and improves accuracy; however, organizations should have well-established processes to learn from the collected data after the data collection. In absence of such processes, monitoring and evaluation staff painstakingly go through complex data aggregation for different programs, locations, and sources, limiting their ability to learn and improve their stakeholders' programs.
Processes that involve multiple tools by engaging in manual processing of data are erroneous, time-consuming, and inefficient. This is where an easy-to-use configuration-driven platform can make a big difference in improving its data governance.
Lastly, let's talk about Data analytics and visualizations.
Alternative: A large number of organizations use bespoke visualization applications to learn from their collected data. These tools are great at what they do. However, to effectively establish monitoring and evaluation practice, one needs a tightly integrated analytics application that can derive insights from the collected data. This ensures that the data flow is seamless from stakeholders to the visualizations that make their way into a dashboard or reports. This improves operational efficiency and helps the organization focus on what they need to focus on: serving the stakeholders!
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