TERMS OF REFERENCE

CONSULTANCY FOR THE REQUIREMENT GATHERING AND DESIGN OF LIVING GOODS DATA & ANALYTICS SOLUTION

Living Goods is hiring an external consultancy firm

December 2022

Living Goods Overview

Since 2007, Living Goods (LG) has been saving and improving lives in resource-constrained communities by facilitating access to cost-effective and quality health education, diagnoses, treatments, and referrals for basic health challenges -- facilitated by cutting-edge technology and a cadre of motivated and supervised community health workers.

Working in partnership with governments every step of the way, we recruit, train, mobilize, and manage the performance of Community Health Workers (CHWs) who live in the communities that they serve. We ensure that the CHWs in our network are trained and regularly stocked with a quality and affordable supply of life-saving medications and other health-related products, which support our focus on ameliorating the biggest drivers of maternal and child mortality.

Living Goods currently operates in three countries; Uganda, Kenya, and Burkina Faso, and while our approach has been proven to deliver lasting impact, we look to save even more lives at scale. We have set ourselves the goal of reaching 18 million people in 5 developing countries by 2026. Success in scaling our impact will require us to partner with the government and in some cases other local and international implementers. The technical assistance we aim to deliver to governments and partners is based on the Living Goods approach codified as DESC, through which CHWs are: Digitally enabled, equipped with skills and supplies, Supervised, and Compensated.  DESC is designed to improve the performance of CHWs, ensuring they are incentivized and able to deliver high-quality health care in an efficient manner. We have found that having a working supply chain is essential to saving lives. Where Living Goods is supporting the public sector to strengthen their community health system, it will be necessary to address the challenges in the supply system to the last mile to ensure that essential medicines are in the hands of community health workers and are delivered successfully to those in need.

Contextual Background:

Living Goods currently has multiple instances of working applications dedicated to each organizational need, which include:

• The Community Health (CHT) Toolkit is used by CHWs for data collection and service delivery at the community level in their operations in Kenya and Uganda and.
• The ERP for organizational finance and operations and some level of commodity inventory management etc.

LG uses existing dashboards that provide static indicators for routine program monitoring to mostly CHW and program stakeholders that manage the day-to-day functioning of the program. Remaining dynamic data inquiries from other stakeholders are directed to an internal team of data engineers and analysts.

Problem Statement

We believe that most of the dynamic data inquiries from within and beyond the organization can be answered quickly if data was optimized for analytics and data-driven innovations while also empowering users with more robust access channels that include self-service menus that can be configured by the users directly. Currently, our data engineers and analysts carry out a repetitive, time-consuming process to generate on-demand and dynamic insights. This creates long backlogs in requests for data and data analysis across the organization.

Scope of Work

As part of the continued evolution and desired data-driven enhancement and innovations for Community Health programming, the organizational vision is to transform from the current data set up to a data stack that can be accessed by a diverse group of internal users,  and which provides a holistic view of our program operations, hence enabling the organization in making better data-driven decisions.

Living Goods is seeking proposals from qualified firms to assess gaps and opportunities in the current data infrastructure and analytics solutions and design a fit-for-purpose data warehousing solution that would support LG create innovations that would have the biggest impact. We are looking to design and build data systems that enable data-driven decision-making (analytic systems) and drive data-powered products, including machine learning (operational systems). The updated data analytics solution should ensure improvements in LG’s data pipeline, data storage solutions, SQL engines that analyze data, and dashboards that make data easy to understand – from data science and machine learning libraries to automated data pipelines, to data catalogs, and beyond.

Key Deliverables & Timelines

We expect 4 deliverables to be completed by the consultancy firm, divided into two phases.

All deliverables need to be completed by Feb 28, 2023.

Phase 1: Discovery, Design, and Requirements

1. Drive a discovery process with internal stakeholders. This process will map and consolidate data sources and indicators required by internal stakeholders to be accessed in one place for dynamic inquiries/queries as well as identify key needs, and challenges faced by stakeholders.
2. Develop requirements for either a customized analytics solution or existing open-source and/or commercial off-the-shelf analytics solutions to carry out data inquiries. For example, Solutions like Snowflake could be considered to build an MVP of the desired data stack. The desired solution should be co-designed with stakeholders across different departments at LG.

Phase 2: Solution Recommendations & Analysis

1. Evaluation of several solutions proposed by the consultants to inform our decision in the choice of platform. The exploration of viable open-source solutions that can handle growing data needs and new use cases should be broad and detailed. Evaluation of different choices should focus on the desirability, viability, and sustainability-driven by suggested questions included in the ToR.
2. Implementation roadmap of the highly recommended solutions that prioritize progressively building the internal capability of the data engineering teams and future external services/consultants to maintain and modify the system.

Key questions the consultancy will answer in Phase 1: Design, Discovery, and Requirements are described below:

System Requirements for improved data architecture and analytics engine:

• What are the current and future needs including clarity on immediate problem space from across multiple organizational stakeholders?

Design, prototype, and build:

• What is the most viable proposed solution from the available options?
• Based on the current LG infrastructure and data stack what would the implementation work plan look like including costing and timelines?
• How do we continuously validate assumptions and rapidly prototype with iterative inputs and feedback from all stakeholders and users?
• How do we build and test for scale and diverse use/integration cases while also meeting security and data protection compliance requirements?

Key questions the consultancy will answer in Phase 2: Solution Recommendations and Analysis are described below:

Fitness for purpose - current and future-ready:

• How well does the proposed solution work for Living Goods’ immediate and future needs and other implementation partners, donors, and governments? (i.e., desirability by stakeholders).
• What design elements do we have to ensure the solution will be easily scaled and customized for diverse futuristic use cases?
• Value risk evaluation and mitigation - How do we ensure that all users will choose to use it?
• Usability risk evaluation and mitigation – How do we ensure users can figure out how to use it?
• Technology value protection, feasibility risk evaluation, and implementation plan– How do we ensure engineers can build up on what we already have in-house with the time, skills, and technology stack we have?
• Organizational strategic viability risk – How do we ensure that the products will work for all LG organizational units and will be a great fit for other partners and governments?

Sustainability:

• What would be the roadmap for traditional support and building capability for internal long-term support?
• What does LG need (people, processes, and technology) and how much will it cost to build and maintain for the long term?

Adoption guide for use by partners and governments:

• What would a costed tech-agonistic template and implementation roadmap for adoption by other partners and governments look like?
• How do we ensure flexibility in the template to guide design and implementation for different maturity of data collection tools, data stack, and infrastructure?

Your Support Structure

You will receive support and guidance from primarily LG’s Global Software Engineering (GSE) team. You will be reporting to the Director of Software Engineering who is the workstream lead and provide weekly / periodic updates and communications. Other stakeholders with who you will be required to engage and interact include the Programs and Country Teams to assess the core data functional requirements.

Minimum Qualification & Experience for Firms

• Demonstrated experience in ICT life cycle management for NGOs, undertaking at least 1 similar project for open-source tools and platforms being leveraged in the digital health space, such as but not limited to Global Goods.
• Demonstrated experience building highly scalable and secured solutions, data modeling, and design patterns
• Demonstrated experience designing and building similar solutions for at least 3 organizations using BI tools including open-source tools such as (but not limited to) Superset and Klipfolio, logical and physical data modeling as well as relational databases.
• Prior experience in similar assignments with at least 3 healthcare organizations or organizations in the healthcare sector with evidence of a successful deployment and adoption
• Organizations should have prior experience working with data standards such as HL7 etc.
• The proposed team submitted by the organization must have the following qualifications:
  • Postgraduate degree in computer science, information technology, software engineering, or related fields
  • Minimum 7 years of experience implementing enterprise architecture framework, preferably in the health sector
  • Proficiency in Python, Javascript, and R.
  • Proficiency with MSSQL, Postgres, Couchdb, and MySQL is desirable.