Agriculture Data Analytics and Solutions

Business Challenge

The goal of developing high-yield varieties in agriculture requires consistent improvement to meet / exceed the needs of client customers.

With the explosion of innovation in camera and drone development, it is now possible to readily capture and analyze these new sources of data. Enterprises seek access to these new data sources for a variety uses: predictive modeling of plant stressors, growth model alignment, the testing and efficacy of chemical application, variety performance, disease identification, prevention and treatment, and to perform new, large-scale data analytics.

The size and scale of these new data sources produce a tremendous amount of data for analysis. This wealth of data is necessary in many cases to provide statistically viable data sets on which to base business decisions.

The costs to capture, store and analyze this data often limits the scale of study, potentially inhibiting results.

Evaluating the cost-benefit analysis of how the data is stored, retained and analyzed is a key business driver for data intensive industries. The desired output for many organizations is to effectively minimize data storage and compute costs, while enabling evidence-based decisions to drive innovation.

Lifescale Analytics Approach

Lifescale Analytics addresses these issues by developing solutions that store data in a high-performance, low-cost cloud environment. Such solutions require a deep and detailed understanding of the questions the business is trying to answer. Two aspects of this analysis need to be carefully considered: the measurement of how the data is evaluated / retrieved, and the duration of the storage required to meet the business need.

An investigation of the analytic requirements compared the actual data used and the costs to store that data. An output of this work generated a model of analytics behavior for specified data sets. Once the model was created, we were able to deliver a solution that shows the actual usage of existing data.

Contrasting the modeled behavior with log reads and usage, we were able to refine the actual data model which allowed for 95% of data to be moved to a lower-cost storage solution resulting in a savings of 80% for storage costs, with no impact to business needs.

Benefit to the Business

Every company storing data can benefit from this type of cost-benefit analysis. As new devices add large amounts of data, it is imperative to add an intelligent and flexible data retention strategy.

Prioritizing the analysis of data usage and data retention will enable companies to maximize value while minimizing investment. This type of modeling can be performed on all domains of data for a company, while ensuring that data remains safe and in compliance with all requisite regulations and requirements.

Leveraging the return-on-investment (ROI) provides a means to better evaluate and address current and future data storage requirements.