USAID’s Performance Management system envisions a seamless process for collecting data on the front lines, ranging from improvements in time required to process exports and the level of agricultural export production to changes natural resource management practices and children’s school completion rates, entering that data into mission information systems, storing it, forwarding it to Washington for onward reporting foreign assistance results and Presidential initiatives thorough the FACTS and other Washington-based systems, and, at the Mission level, accessing and analyzing results data, conducting Portfolio Reviews, and sharing information on progress with USAID partners for learning and improvement purposes.
USAID Performance Information Management Path
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Historically, USAID Missions have devised their own systems for uploading data from projects and other sources and transmitting that data to USAID/Washington as well as making it available to USAID staff locally. Work is now beginning on a process for harmonizing these systems under USAID’s new Standardization Project, including further development of AIDtracker, a Mission level performance information system that is already being used by a number of Missions.
At the Mission level, a primary focus will continue to be on the quality of the data going into a Mission’s performance information system – and on useful ways of accessing data once it has entered the Mission’s database to analyze and share for learning purposes. USAID staff can play an important role in ensuring that implementing partners and external evaluators who develop baseline studies for impact evaluations understand the data entry mechanisms into which data generated from the interview schedules and observation templates they develop will flow. Moving left to right on the performance information management path diagram above, arrows identify quality control opportunities. They also suggest why it is important for USAID staff, implementing partners and evaluation teams to understand the larger picture into which their particular roles in performance data collection fit.
- Moving from Performance Planning to Performance Indicator Reference Sheets – this point in the performance information management process is critical for ensuring that measures selected are actually valid indicators for the results to which they are linked. USAID’s limit of a combination of three standard and custom indictors for each DO and IR displayed in a CDCS level Results Framework makes the selection of appropriate indicators vital. For Results Frameworks, as well as in Project Logical Frameworks and activities, it is also important to indicate which performance indicators are being included because they track key results for a Presidential Initiative such as Feed the Future (FtF). In addition, for trade projects that involve exports where data from different parts of the country or even countries within a region might be aggregated and compared to country or regional trends, the identification of relevant Harmonized System product codes for specific products in Performance Indicator Reference Sheets is an important aspect of the data disaggregation section of that template.
- Moving from Indicator Reference Sheets to Data Collection Protocols – this quality control point includes checking on whether data collection instruments have been pre-tested with intended respondents; whether they have been translated into local languages, if needed, and then retranslated into English as a cross-check; whether instruments are designed for data uploading or other relatively error free interfaces with the USAID database they will enter; and whether instructions for every instrument are clear and complete. For baseline studies and evaluations that will be carried out by third parties, USAID would be wise to include requirements, such as the explicit linking of data collected to specific performance indicators, or its collection based on sex disaggregated data, or harmonized system product codes, in Statements of Work (SOWS) for these activities.
- Moving from Data Collection Protocols to Data Collection – check on whether data collectors have been trained and had an opportunity for practice and critique; whether, how and how frequently data collection is supervised, e.g., spot checks on data collector performance. Even before data are collected, USAID needs to review the types of data to be gathered to determine whether Standard Indicators on for which a Data Quality Assessment is needed has already taken place or will need to occur before data are entered into the Mission’s information system.
- Moving from Data Collection to Data Entry and Storage – check on how data is recorded, transcribed and entered into USAID’s data base. In a Project MEL Plan, Missions will normally include a sample Project Performance Reporting Template for documenting planned and actual performance on performance indicators for which data will be needed to support reporting against a program level PMP or for other management purposes. These formats may also be included in PMPs and in implementing partner Activity MEL Plans. During implementation, it is important to make sure they are utilized and that adequate data editing and cleaning, removal of duplicates and typographical errors, and other repairs are made.
- Moving from Entry and Storage to Analysis, Retrieval and Presentation – this step include the implementation of whatever data analysis plan a Performance Indicator Reference sheet outlined. This may require a special review to check on whether patterns and averages have been determined, and whether and how outliers have been explored. Data Visualization, which can help USAID staff and partners understand the implications of performance reports, may involve the creation of graphs and charts for a web-based performance displays. This is also the point at which data on performance would be most likely to be integrated on maps that may also show where USAID and other donors are working, or include other types of data for which the geographic location or origin is known. These data can be particularly helpful in developing dash boards and other mechanisms for Inclusive Performance Reporting and information sharing, as discussed later in this kit.