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Agronomic Service Provider employee reviewing data lake information.

Agronomic Service Provider Unlocks Data to Increase Profitability, Improve Client Service, and Enable Scalable Growth

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Client Profile & Challenge

Our client is a private equity-owned agronomic service provider offering comprehensive and impartial input “prescriptions” and operational advice to agricultural clients while utilizing advanced digital capabilities.


Messina Group had previously completed a due diligence engagement to analyze our client, and one of the primary value creation gaps we identified was the lack of an enterprise data platform and data lake.

Despite being a data-driven service provider, this deficiency had resulted in product teams working in silos – leading to fragmented data structures, inconsistent database designs, and a disjointed array of products and tools. 

As a result, the client was leaving a significant amount of value on the table. Their data science team spent an inordinate amount of time acquiring and curating data rather than executing data analysis to drive business insights. In addition, data was not able to be leveraged for year-over-year analysis, prescription calculations, or AI/ML workloads.

By implementing a data lake, the client would be enabled to exponentially increase their ability to improve service offerings to their customers and drive business insights. 

Solution Overview

Messina Group executed a two-phase engagement to implement and operationalize the client’s data lake. The first phase focused on designing the foundation required to support the data lake, while the second phase focused on the implementation of the data lake for one of the client’s business units.

Our experts started with a use case-driven Platform Selection process to determine the best-fit platform and architecture for the client’s data lake. This evaluation began with client stakeholder interviews identifying pain points and requirements for their future state data environment. We then evaluated three platforms on their ability to fulfill these requirements, their fit into the client’s existing data architecture, total cost of ownership (TCO), and other key capabilities. It was determined that AWS Redshift was the best fit for the client’s needs.

After the platform was selected, our experts implemented the data lake for one of the client’s business units. This would enable the client to improve their service offerings for customers by providing more data-driven recommendations in their prescriptions. 

To enable this, Messina Group first gathered low-level requirements and completed data mapping from existing databases into the new platform. Soon afterward, our experts implemented the platform and adjusted the existing architecture as needed to accommodate the changes. Once this was completed, the data from existing sources was integrated into the data model of the new platform, tested, and rolled out. To ensure long-term sustainability, the Messina team also provided the client with data management best practices and a data governance model. This model established a framework for managing the client’s data assets and defined policies, standards, and roles to ensure data is accurate, secure, and accessible. It promoted data quality and proper data lifecycle management while also helping the client to foster a more data-driven culture for their business. 


Platform Selection, Platform TCO Analysis, Digital Strategy & Transformation, Business Advisory & Transformation, Advanced Analytics & AI

Technology Used:

Redshift, AWS, Postgres, SQL Server, Tableau, Terraform, AWS Glue


From strategy to execution to ROI realization, Messina helped this client lay the data foundation necessary to drive significant profitability and revenue growth. Their value proposition to their customers is their ability to deliver new analytical products, deliver them to the customer, and demonstrate the value of their services to customers.

By improving the structure of and access to their data, this engagement helped unlock data that previously presented significant usability challenges. Improved data usability meant our client could now assess and demonstrate the value of their existing products and utilize their data to assist in the development of new products more accurately. Increased efficiency meant that their data science group was able to spend time generating analyses rather than simply acquiring data.

Additionally, by implementing an updated data model, platform, architecture, and governance, we provided the foundation for future operational enhancements within the business, including their CRM and Finance. These changes enabled scalable growth and improved data-driven decision-making capabilities.

Finally, the implementation led to significant performance improvements. Previously, reporting was being run on data in their production environment, creating significant performance issues. The new data lake solution allowed the client to run their reporting in a separate environment, enabling faster and more responsive reporting.