Overview
A leading manufacturer of paints, coatings, and specialty materials with operations worldwide is undertaking a digital transformation to streamline their data management and decision-making processes. They aim to gather data from all their plants worldwide into a single data lake hosted in Azure. To achieve this goal, the company has engaged a data science team to work on this data and develop predictive analytics algorithms for inventory management, pigment color formulation, and other processes. The reports are generated in Power BI. The data engineering team responsible for this initiative is receiving consulting services from STL Digital.
Business Challenge
As part of this initiative, the team needs 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 must also create data models that cater to the various needs of the data analysts, data science team, and reporting team. They are working on creating micro-batches for existing batch processes to enable more recent data availability. The team is also optimizing the modeling process for fast and efficient code.
Solutions
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.
Results
Through their partnership with STL Digital, the client has been able to create a robust data engineering process for ingesting and transforming data from different plants worldwide. The team has developed predictive analytics algorithms to enable better decision-making regarding inventory management, pigment color formulation, and other processes. They have created micro-batches for existing batch processes to enable more recent data availability. The team has optimized the modeling process to result in fast and efficient code. The client now has a more streamlined data engineering, analytics, and reporting process.
