Setting up single source of truth for a poultry company

About client

Our client is a group of farming companies, one of India’s largest producers of broilers and run poultry mills with production capacity of production capacity of 800 tonnes per day. They have multiple divisions and state-of-art poultry labs, each responsible for manufacturing high-quality poultry food, equipment, commercial broiler farming, parent and layer stock, and more. Operating for around 2 decades, they have become of the leaders in food and agriculture industry.

Challenges they faced

Our client manages a group of companies, each with their own products, clients, and farming assets, they lacked centralized business insights, inconsistent reporting, and other decision-making challenges. Here are detailed problems the client encountered with their data landscape.

Multiple sources of data, each in different formats: The client regularly had to deal with data from various sources: Microsoft dynamic, TMS, mobile application and flat files, etc. The lack of standardization made it difficult to generate centralized business reports.

Data integration & ease of access: The client had limited self-service view nor data teams could extract custom data inferences. If they had to, they encountered issues dealing with security, access control, ETL & pipelining challenges, and many more, stemming from inconsistent data systems and varied data formats.

No scope for scalability: Their aim is to set up a scalable, reliable, and agile BI landscape that could support their extensive reporting needs, where business insights extraction will not be costly, time consuming, and strenuous.

Their data landscape had the potential for rich predictive and prescriptive reporting, which they were unable to do, as the data wasn’t centralized to the BI landscape.

Solution from datakulture

Our plan was to build a unified data layer, that applies facilitates data movement from multiple sources to the standardized destination layer, following a meta-data driven approach. A centralized data warehousing system is built here, aggregating data from 5 of their 11 companies.

Here's what we did in six steps:

We built a robust data ingestion pipeline using our Data Ingestion Accelerator framework, which integrated structured and semi-structured data (SQL Server, flat files, REST APIs, and other data) into a common BI layer.

Data moves from layers—Source → Bronze → Silver → Gold layers, making the system scalable, reusable, and adaptable across new companies or domains.

Further data validation and cleansing happens through Python and PostgreSQL routines, storing clean, denormalized data, and then transferring it to ClickHouse for fast reporting.

To make insights accessible to users, tools like Tableau, Metabase, and custom React apps were used. Visualized data into insightful reports using Tableau.

Also focused on detailed auditing, error handling, governance, and data tracking mechanisms, which was orchestrated using Apache Airflow and custom agents.

The entire process happened in phased, incremental development with continuous feedback, error tracking, resolution, and deployment.

How did this help?

The client’s management team, who had no visibility on existing stock count, sales, and sudden demand changes, could now clearly track all, with suitable, simple visualized reports. This not only improved their everyday decision-making but also impacted their inventory stocking in accordance with demand changes.

Final thoughts

The automated data ingestion, transformation, and reporting reduced the human effort, errors, and scalability concerns, extracting clean business inferences and delivering them on time. This solution turns fragmented, disconnected data into a single source of truth—delivered at speed, at scale, and with well-set-up governance. For a group of companies proceeding toward unified goals, it lays the foundation for advanced analytics, AI initiatives, and enterprise-wide reporting, while making day-to-day data access effortless.