Career Opportunity /  17 March 2023

M&E organizational data management consultant needed

WeForest is looking for a consultant(s) to identify the best tool(s) and/or program(s) to standardize M&E data collection and management across all our Forest Landscape Restoration projects, globally. Will you perform a market evaluation to identify all potential solutions regarding M&E data collection and management that addresses as many of the identified ideal features? See full details in Terms of Reference. 

Deadline: 17 March 2023 - the deadline has passed.
Remotely - Belgium, Brazil, Ethiopia, Malawi, Senegal, Tanzania, Zambia

Terms of Reference

M&E data collection and management tools

  1. Overview of WeForest

Mission: To conserve and restore the ecological integrity of forests and landscapes, engaging communities to implement and deliver lasting solutions for climate, nature and people.

Programmes: WeForest is a Belgium-based non-profit organization, which works with communities and local partners to develop scalable Forest Landscape Restoration (FLR) projects mostly in tropical regions to sustain nature's diversity, benefit our climate, and support human well-being. In recent years we have been growing fast: while we were involved in just 7 projects focussing mainly of forest restoration in 2019, we are currently working across 17, mainly more holistic forest landscape restoration projects; with several more new projects in the pipeline for coming years.

  1. Monitoring & Evaluation of our FLR projects

For a summary on the details of WeForest’s FLR approach see Annex 1. WeForest aims to have comprehensive monitoring & evaluation (M&E) protocols in place for each of our FLR projects to:

  • Keep track of project progress & success within WeForest, by comparing measured data with predefined targets
  • Implement quality assurance processes to reinforce the credibility of our projects 
  • Identify needs for adaptive management when targets and measured data do not align
  • Support lessons learning and sharing across projects within WeForest
  • Support transparent and informative communication of project progress to all stakeholders, including funders

To guide each project’s M&E activities we construct a logframe that is hierarchically structured following the expected theory of change for each specific project, where activities lead to short-term outputs, longer-term outcomes and the eventual project goal, with SMART (specific, measurable, achievable, realistic & time-specific) indicators with pre-identified, mostly annual, targets placed at each hierarchical level. Although each logframe is project-specific, they all adopt the same basic outcome structure, based on out 3 main intervention focus areas: (forest) governance (outcome 1), trees & biodiversity (outcome 2) and people (livelihoods) (outcome 3). Details on indicator methodology, timing and responsibilities is captured in a comprehensive, detailed M&E plan that accompanies each logframe. 

In the field this translates in the need for extensive quantitative and qualitative data collection covering governance structures and document status, forest and biodiversity status monitoring and socio-economic assessments with local households. Properly collecting and managing all this data can present complex logistical challenges for project staff. Currently projects use a wide range of tools (paper-based and digital collection tool-based (KOBO, GPS, GISCloud, SMART) and programs (QGIS, Excel, Word, SPSS) for this purpose. A schematic general overview of the different data types that need to be collected across most of our projects is provided in figure 1. This figure also shows the wide range of tools & programs that we are currently using to collect all of this information. Lines in the diagram furthermore highlight how data is usually interlinked, with green and red lines indicating instances where these links are automatically embedded and absent in our data collection/management tools, respectively. The use of multiple tools within each project has also hindered automatic integration of all project data in one central database system.



Figure 1. Schematic overview (‘mindmap’) of WeForest data entities, collection methods & data dependencies.

  1. Identified gaps & desired features

Based on our experience in the field and the M&E mapping exercise presented in figure 1, we have identified the following gaps & challenges regarding our M&E data collection and management:

  • no consistency in the tools used across projects, leading to inconsistencies in the type of data collected
  • Frequent errors when tools are inappropriate (e.g. paper)
  • Significant amounts of time needed for collecting, checking, summarising & analysing data
  • Operational decisions not always based on project M&E data due to delays or unavailability 
  • Lack of updated contextual and project data, leading to delays in adaptive management 
  • Missed opportunities due to unavailable or delayed livelihoods data
  • High risk of double counting and other errors in interpretation due to decentralised data collection & management tools
  • Data collection often resulting in data format that is not conducive to efficient analysis

Moving forward, WeForest would like to identify the best tool(s) and/or program(s) to standardize M&E data collection and management across all our FLR projects, that incorporates the following features to overcomes the identified weaknesses of our current system:

Must have: 

  • A limited number of digital tools and programs achieve all our 1) data collection & 2) data management (database) needs, including data matrices for vegetation & household data, geospatial point, line & polygon data, questionnaire data, among others (cf. fig 1)
  • The system is based on a relational database, that allows data to be automatically linked across different data entities (fig. 1)
  • Related to the previous point, the tool allows the intuitive assignment of clear identified codes for data points in certain entities, most crucially for farmers or households, to allow clear tracking of involvement of an individual household across multiple livelihood interventions
  • The data collection tool can be used on any smartphone, works offline, does not drain battery power and is intuitive and easy to use
  • The tool/program has capacity to hold significant amounts of data points for each individual project. For example: the number of households that need to be tracked can reach up to a few 1000 to even 13000, individual intervention polygons up to 1600, and number of livelihood interventions up to 10 for a given project
  • The tool(s)/program(s) come at a realistic investment and management cost for a mid-sized NGO
  • The tool is guaranteed to be continuous/ stable in the long-term and/or allows extraction and transfer of our data to another system in case WF would decide to shift

Should have:

  • The tool(s) allow(s) to tailor access to individual data entities and collection forms to specific users of the tool. This would allow community facilitation of data collection with the tool in a limited functionality mode, thus increasing ease of use
  • The tool/database allows historical data to be retained as memory. For example, if a specific farmer drops out of the project, his previous involvement in the project is still retrievable, even though he is no longer part of the active farmer database
  • The tool provides dashboards to summarise changes in key indicators in the database (cf. Business Intelligence)
  • The tool has the capacity to integrate evolving needs in the future (e.g. increasing # projects, # data units per entity, # entities)
  • clear metadata generation and/or management functionality built into the tool
  • Tool is also suitable to facilitate data collection and/or management needed specifically for specific projects we aim to have carbon certified (MRV data needs). 

Could have: 

  • Subsets of the database can intuitively be quarried and summarized by the tool/program
  • Standard functionality to automize, or facilitate manual, initial data quality evaluation
  • The tool has the option to include ‘transactional’ data entities. For example to track when specific inputs such as beehives are distributed to specific farmers
  • Clearly defined capacity to store all database data and metadata, either cloud-based or server-based
  • The tool can be co-hosted or directly managed by WeForest


  1. Objective of the study

Perform a market evaluation to identify all potential solutions regarding M&E data collection and management that addresses as many of the identified ideal features listed in the ‘general objective’. Note that identified solutions can consist of a single tool (example: smallholdr app) or a combination of tools (example: ODK-based data collection tool integration with Microsoft ACCESS and PowerBI). 

Expected results:

  • A schematic overview of up to five identified solutions, indicating which of the identified ideal features it addresses as well as the comparative advantages and disadvantages of each proposed solution. 
  • As short description of each presented solutions workflow, level of expertise required for implementation and expected implementation and (annual) management costs.


  1. Deliverables and timeframe

Key findings will be compiled, analysed and clearly presented in a final, structured report, which will include the consultant’s key findings, results and recommendations. All deliverables will be submitted to WeForest in English in soft copy, modifiable versions.

The consultancy will ideally start on 1st of April 2023 at the latest. The final version of the report will need to be submitted at the latest by 25th of May 2023. The consultancy should ideally not last more than 10 days of effective work. 


1First draft of report submitted to WeForest for comments28th of April 2023 at the latest
2Final, validated version submitted25th of May 2023 at the latest

  1. Consultant profile specifications

Must have: 

  • Demonstrable relevant experience in data analysis/ database management.
  • Experience with multiple data collection & management tools/programs.
  • familiarity and understanding of multisectorial and multidisciplinary programme data needs.

Should have:

  • Experience with collection and manipulation of (FLR-related) biophysical, socio-economic and geospatial data. 
  • Academic background in relevant topics eg. data science, statistics, GIS, ecology, sustainable development, etc.

Could have:

  • Good understanding of forest landscape restoration projects and associated M&E data needs. 


  1. Consultancy proposals

Applicants are invited to submit their offers by 17st of March 2023 at the latest at the following address:  [email protected], indicating the reference “M&E data collection and management” in the email subject. Any clarifications or additional technical details needed for offer right-up can be requested through the same contact address (recruitment@weforestOrg). Offers must include: 

  • A technical offer (no more than 3 pages)
  • A financial offer including daily rates
  • Consultant’s CV demonstrating their ability to fulfil the study 
  • At least two professional references
  • Potentially: One study or report produced and written by the consultant for similar tasks