Framework
FoodDataQuest will develop ground-breaking data-driven solutions based on an integrated methodological framework that explores new types of private and public data sources, data from “unconventional players” and non-competitive data, and leverages data sharing mechanisms in order to provide the EU food chain stakeholders with increased insights and enhance the transition towards sustainable healthy diets. The proposed framework will include guidelines and data collection strategies, to drive the food system transformation towards inclusive, sustainable, healthy diets within the boundaries of legal and policy frameworks. FoodDataQuest will co-create and test advanced data-driven solutions based on AI and ML algorithms, following a multi-actor approach that will serve as a lighthouse that positively impacts a fair, healthy and environmentally friendly food system. Last, FoodDataQuest will engage citizens in industry’s data-driven innovations balancing between data openness and protection of private and sensitive data of multiple stakeholders.
Introduction
The framework will serve as a basis for developing data-sharing strategies, guidelines, and tools that can foster collaboration, boost transparency, reduce waste, and empower consumers to make more sustainable choices — all while honoring stakeholders’ preferences and legal requirements.
Furthermore, this task will connect the policy, legal, and technical dimensions of data sharing and lay the foundation for subsequent tasks in the project.
Methodology
This section introduces the methodology that was used to develop the Framework and the Toolset. To start with, a literature review was carried out on data sharing in agri-food, and some reference models that have been used in this context were summarized. However, the main focus in this chapter is on the definition of the framework and the development of key components. After the framework and tool were set up, the data was collected from the use cases (UCs) by applying the T2.1 tool. Task 2.1 reviews and assesses the data collected in the agri-food system value chain.
Figure 1: Overview of framework development methodology
What Is Actually a Framework?
Generally speaking, a framework defines a set of ideas, conditions, or assumptions that determine how something will be approached, perceived, or understood (Merriam-Webster, n.d.). As such, a framework can range from very basic conceptual structures to specific principles and strategies for technical implementation.
In the FDQ project, the framework plays a central role as the “glue” that brings together the different activities and results into a coherent and well-balanced whole. The second perspective is typically not included in a framework but in a reference model or blueprint. To avoid confusion, we distinguish these two concepts in this project as follows:
“The FDQ Framework is a systematic classification of concepts for analyzing, designing, and implementing data-sharing systems in food value chains.”
Explanatory notes:
A data-sharing system is defined as a digital system to share data between multiple actors of a value chain.
Data sharing is related to a wide range of data for each stage of the value chain, including: structured and unstructured data; public and private data; master and process data; and different functional domains (financial, agricultural field, food quality, compliance, traceability, consumption, etc.).
A value chain comprises a series of interconnected processes in which each step adds value to the product or service for the final customer (Michael E. Porter, 1985).
A consumer-driven value chain focuses on delivering products and services in a way that meets the demands of the final consumer — this means generating and disseminating market intelligence across the chain and ensuring a supply chain-wide ability to respond quickly.
In a food value chain, the final consumers are food consumers, and the added value is related to key parameters such as nutritional content, taste, price, health, and sustainability. The food value chain comprises a network of diverse actors, including agricultural input suppliers, farmers, producer cooperatives, food processors, retailers, packaging companies, certifiers and inspectors, food labs, advisors, traders, and food service companies.
“The FDQ Blueprint is a toolbox of practical building blocks for analyzing, designing, and implementing data-sharing systems in food value chains.”
What is the FDQ Framework?
The FDQ framework focuses on data-sharing between multiple actors, directly or indirectly involved in food systems — from farm to fork — to maximize interoperability. A key complexity in these applications lies in the collaboration of numerous stakeholders, each with different roles in the data value chain. To account for this, the framework draws upon value chain management and data management literature. It combines two main chains, Food Value Chain and Data Chain. It is designed to be both conceptual and actionable, allowing interoperability across diverse systems and stakeholders. Figure 2 illustrates its components and building blocks.
Figure 1: Framework Overview
Food Value Chain
Value chains consist of value-adding processes performed by different organizations within a network. The participating organizations are independent but need aligned strategies, shared governance mechanisms, and a common infrastructure to coordinate their activities.
Data Value Chain
The FDQ framework considers the food value chain within a data ecosystem, where data is a key asset for production, distribution, and utilization of value. A data value chain comprises a sequence of activities — from data capture, storage, transfer, transformation, and analytics to utilization — that add value to the data. It mirrors the structure of the food value chain, with analogous components:
i) Data Strategy and Governance,
ii) Data Chain Processes,
iii) Data Infrastructure.