Let's make data systems more inclusive.

Data is not for donors. 
Data is not for headquarters.
Data is not for evaluators. 

From paper to Excel to cloud-based systems such as Salesforce, Dev Results, and dhis2, there are currently various tools and systems social impact organizations are using to monitor and collect data to meet their programming and reporting needs. No matter which platform organizations use, a major dilemma with current monitoring systems or software is unequal representation throughout the implementation process, particularly due to an increased focus on the outputs of the tool being implemented. Under these conditions, the process fails to adequately represent an organization's data flow, which begins and ends with the everyday users themselves. More often than not, folks who actually enter data are not part of the decision-making process, design, or adaptation process of new tools. They are simply involved at the end, during training. Not only is this a consulting and implementation dilemma, but also one for designers and developers, who receive feedback from an ailing process. What’s currently dead wrong about the thinking and monitoring systems available today are that they are marketed to, and sometimes built for, the wishes of donors and decision-makers. This leads to systems and implementers flaunting eye-catching graphs, mobile apps, time savings or increased productivity. These selling points are never as valuable as having clean data or strong user adoption, which cannot be achieved without equal and consistent input from an organization's entire user base. Without clean data and strong user adoption, the international development field is a junkyard of failed, drawn out, abandoned or over budget tool implementations. In order for new tools and systems to be more effective, the missing requirement is a more inclusive process in how these tools are marketed, developed, and implemented. 

The day in and day out programmatic level is the most critical part of your organizations data flow. In order to truly get the most out of any monitoring tool or system, data collection needs to be optimized for your organizations daily data needs, specifically for the roles of enumerators, secretaries, and database administrators. These roles should be at the table during the selection and decision-making process for a new  tool or system, and must be involved throughout the implementation. Once tools and processes are optimized from this perspective, results can flow upstream, providing a stronger data chain with more proficient users. Aside from saving time and money spent on fixing and updating systems, this will lead to stronger user adoption, which must be a top priority for any implementation. 

Furthermore, not only is the decision-making and implementation process often flawed, but the thinking that goes behind implementing a new monitoring tool or system also does not usually flow downstream. This has to do with the questions folks want and need answers to across different levels within the organization. The thinking and questions that a monitoring and evaluation manager, CIO, or country director wish to answer should flow downstream towards end users, regardless of the tool, system or software being used. As new tools are implemented, the thinking behind their purpose typically gets diluted into simple training for use down the road. Throughout an implementation, this can be improved by giving all users the necessary agency to adapt tools and the ability to provide feedback at any time during the process. Listen to this feedback and address it. Enumerators, secretaries, and database administrators should feel that the better the data they can collect and manage to answer the questions they have, get the insights they need, and generate the reports they want, the more headquarters, donors, and the international development sector as a whole can work towards better results. This awareness and proficiency with new tools will allow an organization to take full advantage of healthy monitoring and evaluation systems. 

In order to effectively use any platform, we must work to make data processes, data thinking, and data implementations more inclusive. This begins and ends with the everyday user