Unifying Data Access for Self-Service Analytics in Agricultural R&D

Context & Problem

Our partner faced growing complexity in managing and accessing data across its R&D landscape, where analysts and scientists relied heavily on development teams to create dedicated views for reporting and data preparation. This limited speed, scalability, and data-driven decision-making.

We designed and implemented an automated solution to centralise information from several sources and provide insights without the need for constant coordination and manual searches.

The result is faster access to insights, reduced dependency on development teams, and a scalable foundation for advanced analytics and AI-driven innovation in agricultural research. Reports that could take weeks to generate due to constant back-and-forth now take hours.

Facts & Figures

Operational efficiency increased as dependency on development teams decreased, allowing technical resources to focus on core development activities rather than ad hoc data requests.

The introduction of automated data pipelines and self-service capabilities reduced the time analysts spent preparing datasets from several days to a few hours. Reporting requests that previously took up to a week can now be fulfilled within one to two days, improving responsiveness across business and research teams.

The platform provides a unified, governed data layer, enabling consistent access control, built-in data lineage, and a single source of truth for multiple data domains, including environmental, phenotypic, and machine data.

Business Challenge & Why Accesa

As data volumes and complexity increased across research and development activities, the company faced challenges in making data accessible and usable at scale. Analysts and domain experts depended on development teams to prepare datasets, even for minor adjustments such as adding new fields. This created delays, limited flexibility, and slowed decision-making.

At the same time, critical data required for breeding and seed evaluation, ranging from satellite imagery and weather data to machine and field data, was fragmented across multiple sources. Extracting and combining this data required manual effort, slowing down research workflows and limiting the ability to generate timely insights.

Accesa was selected to support the design and implementation of a modern data platform that could address these challenges holistically. The focus was on enabling self-service data access, standardising data management practices, and creating a scalable foundation for advanced analytics and AI use cases across R&D and beyond.

Solution & Impact

The initiative introduced a machine learning–based approach to detect anomalies in machine behaviour ahead of potential failures, shifting maintenance from reactive response to early intervention.

Machine log and state data were processed and structured into a format suitable for analysis, enabling the identification of patterns associated with abnormal behaviour. These signals were then correlated with CRM service tickets to validate whether detected anomalies corresponded to real-world maintenance events.

This validation framework ensured that the solution remained grounded in operational reality. By clearly defining true positives, false positives, and missed events within predictive intervals, the team established a transparent method for measuring performance and refining the model.

As a result, maintenance teams gain earlier visibility into potential issues, allowing for more targeted and prioritised interventions. This reduces reliance on emergency call-outs and supports more efficient allocation of service resources. At the same time, the alignment between machine data and CRM systems strengthens the connection between technical insights and business processes.

Importantly, as a proof of concept, the solution demonstrates what is achievable while also defining clear boundaries. It validates detection capability but does not yet represent a fully optimised or production‑scale predictive maintenance implementation. Instead, it provides a tested foundation and proves the potential for further iteration and improvement.

Applicability & Current Status

The data platform is actively being implemented, with multiple high-impact use cases already defined and in progress to drive adoption across the organisation. The approach is designed to scale beyond R&D into broader business functions, supporting enterprise-wide data integration and analytics.

Our future focus is on expanding data-driven use cases, increasing adoption of self-service capabilities, and enabling more advanced analytics and AI scenarios on top of the unified data foundation.

This model is applicable to organisations with fragmented data landscapes and a high reliance on technical teams for data access. It is particularly relevant in industries where combining multiple data sources is essential for decision-making, such as agriculture, manufacturing, and life sciences.

By shifting from fragmented data access to a centralised, self-service model, our partner has improved efficiency, accelerated decision-making, and laid the groundwork for advanced analytics in agricultural research.

If you are looking to unlock the value of your data and enable scalable, unified, self-service analytics across your organisation, let’s explore how our data and automation specialists can do for your organisation.