Introduction

When I start my work day at Celium, I outline a plan of what I’m going to tackle and then share it in our daily tasks chat. Then, the inevitable happens – something comes up that needs addressing, and my plans change. This tension between expectations and the reality of what happened mirrors a recurring challenge in our work with our clients' data: designing a system that effectively bridges the gap between theoretical plans and real-world outcomes.

What is Data Context? 

Recently, I've started thinking about this interplay between plans and outcomes as 'data context.' In our work, planning data often represents the initial sketch of a pasture: intended practices, grazing plans, and other strategies designed for regenerative growth. Actualized data reflects the living reality—measurable outcomes like soil samples and practice implementations after heavy rains or drought. This contrast between plans and outcomes is central to designing data systems that allow our clients to effectively deploy their data towards their goals. 

On our team, we’ve been referring to these different types of data as "data context." While a thoughtfully designed approach to data context can streamline reporting and project accountability, its true importance lies in building feedback loops that enhance our understanding and decision-making over time. In this post, I'll share some lessons and strategies to avoid common pitfalls when designing with schemas that incorporate data context.

Aligning Visions and Reality

Duplication Creep 

When temporal data (pasture improvements and boundaries) and static data (addresses and names) are stored together in a table, data inevitably proliferates in inconsistent ways. For example, when planning pasture improvements, you enter both the static pasture details and planned improvement data. Later, when recording the actual improvements, you must re-enter those same static pasture details alongside the actualized data. This redundancy undermines confidence ("Which numbers should we trust?"), increases the burden of manual data entry, and obscures relationships between entities. To avoid this, data schema designs should always attempt to store static and temporal data independently from one another. This separation allows us to link temporal datasets—both planned and actualized—to consistent reference points, reducing redundancy and facilitating stronger data analytics and modeling. 

Fractured Timelines

Agricultural operations follow natural rhythms: calves graze seasonally, and pastures regenerate over multiple years. These biological timelines rarely align with project schedules—such as annual grant cycles or quarterly reports. Managing these parallel timeframes complicates the comparison of planned versus actualized data. This issue can be alleviated by establishing consistent time units across the system, whether based on reporting cycles, contracts, or seasons. This standardization simplifies comparisons between data contexts, allowing us to compare apples-to-apples. 

Misaligned Contexts

When data collection methods vary across contexts, analysis becomes more difficult. For example, grazing periods might be planned as the number of days (“12 days”) but recorded as range of actual grazing days (“05.01.2024 to 05.15.2024”), making direct comparisons needlessly convoluted. Whenever possible, maintain consistency in how metrics are captured across contexts. Use the same units, formats, and collection methods for both planned and actualized data. When certain fields are unique to either planning or implementation (weather conditions for example), store them separately from the data meant for cross-context comparison. This approach prevents tables from becoming cluttered with null values and maintains clarity about which metrics can be meaningfully compared.

Conclusion

While these principles serve as guidelines, each project's unique requirements should inform their application. The goal is to strike a balance: avoid over-engineering that might limit adaptability while maintaining enough structure to ensure data reliability. 

Designing data systems for regenerative agriculture requires us to embrace a complicated reality—the distance between our carefully drawn plans and the living unpredictability of the land. This gap isn't a flaw to be eliminated, but rather a space where critical learning and creative innovation can thrive.

Through thoughtful data modeling—separating static from temporal data, aligning timelines, and allowing metrics to evolve—we can create systems that don't just document differences between plans and outcomes, but help us understand why those differences occur. 

Are these problems resonating with you? Want to talk about it more? Drop us a line at info@celiumgroup.com and let’s chat!