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What data is collected in Foodpairing's knowledge graph?

A vast collection of data on flavors, markets, trends, and consumers, offering deep insights to uncover opportunities and drive precise food innovation.

What Data Is Collected Within the Knowledge Graph?

The Headspace knowledge graph is the world’s largest food-focused data structure, designed to support innovation by capturing a wide range of interconnected insights. This comprehensive database collects and organizes diverse datasets to empower precise, data-driven product development.

 

Types of Data Collected

  1. Product Data

    • Proprietary analytical data from ingredient and product analyses executed in the Foodpairing lab including aroma, taste, trigeminal, texture. Each day, 40-100 new products are analyzed in the lab, ensuring the knowledge graph and digital twins remain current and robust.
    • Details on the chemical composition of foods to uncover unique flavor pairings.
    • Proprietary algorithmic generated sensory data retrieved from algorithms linking chemical analysis to sensory profile.
    • Information on ingredients, flavor profiles, nutritional content, and usage in various cuisines.

  2. Market Data

    • Trends and consumer preferences across global regions and markets.
    • Insights into seasonal demands, emerging food categories, and market-specific innovations.
    • New products launched on the market with all their meta data like ingredients, claim, price, location, launch data.

  3. Consumer Data

    • Proprietary survey results capturing preferences, habits, mood, lifestyle and buying behaviors.
    • Data segmented by demographics, regions, and psychographics to tailor insights.

  4. Trend Data

    • Current and emerging trends in food, beverages, and flavor combinations.
    • Industry shifts such as plant-based eating, functional foods, and sustainability.
  5. Clinical Trial Data collected from scientific publications

  6. Cultural Data

    • Regional cuisines and traditional flavor pairings.
    • Culinary practices and preferences tied to specific cultures and holidays.

 

Ensuring Data Accuracy and Relevance

To keep the knowledge graph reliable, data is updated daily through automated pipelines and validated using robust systems. This ensures that every piece of information remains relevant and aligned with the latest market and consumer trends.

 

A Foundation for Food Innovation

By integrating and connecting product, market, and consumer data, the knowledge graph enables you to innovate with precision. Its comprehensive, dynamic nature ensures your concepts are always aligned with current insights, making it a powerful tool for successful product development.