Digital Transformation and Data Engineering Services for a Global Paints and Coatings Manufacturer




Data Analytics and AI

Background of the Customer

A leading manufacturer of paints, coatings, and specialty materials with operations worldwide. Headquartered in Pittsburg and serving over 70 countries worldwide, they serve customers in construction, consumer products, industrial and transportation markets, and aftermarkets.

Key Objectives/Challenges

The customer wanted to streamline their data management and decision-making processes. They intended  to gather data from all their plants worldwide into a single data lake hosted in Azure. To achieve this goal, the company wanted to engage a data science team to develop predictive analytics algorithms for inventory management, pigment color formulation, and other processes. As part of this initiative, the team needed to ingest, clean, transform, and manage data coming in from different plants worldwide in different formats. This process is complex and has many challenges, such as ensuring data accuracy, completeness, and consistency across all plants. The team also had to create data models that cater to the various needs of the data analysts, data science team, and reporting team.

Our Solution

STL Digital is providing high-quality consulting services to the data engineering team, leveraging their expertise in different Azure products such as Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure Database for MySQL, Azure Analysis Services, Azure Synapse Analytics, Azure Stream Analytics, Event Hubs, Azure Functions, Microsoft Purview, Azure DevOps, Azure Monitor, and Azure Policy. The team is involved in mainly two projects, namely Micro batching and Inventory AIML, which require data engineering expertise. They are developing predictive analytics algorithms that help the client make better-informed decisions regarding inventory management, pigment color formulation, and other processes.

The Impact

Through their partnership with STL Digital, the client was able to:

Create a robust data engineering process for ingesting and transforming data from different plants worldwide.

Develop predictive analytics algorithms to enable better decision-making regarding inventory management, pigment color formulation, and other processes.

Create micro-batches for existing batch processes to enable more recent data availability.

Optimize the modeling process to result in fast and efficient code.

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