Crystal: Price optimization with advanced analytics

Crystal — a leader in Colombia's textile industry — partnered with Centraal to sharpen their price benchmarking strategy. We built an advanced data analytics platform that transformed how they make decisions.

Client context

Crystal is a standout company in Colombia's textile industry, known for innovation and a broad product range. In a highly competitive market, Crystal wanted to maintain and strengthen its leadership position.

The challenge

Crystal identified key opportunities to improve their pricing strategy:

1. Raise the quality and consistency of market data
2. Adapt quickly to changing information sources
3. Improve cross-department collaboration for price analysis
4. Accelerate data-driven decision-making
5. Boost efficiency in competitive price analysis

These were crucial for Crystal to stay competitive in a dynamic market.

The Centraal solution

We built an Azure-hosted data platform designed specifically to tackle Crystal's challenges:

1. Automated Data Pipeline: We built a system that extracts and processes information from multiple sources continuously and efficiently.
2. Data Validation System: We implemented Great Expectations to establish and enforce the rigorous data quality rules Crystal defined.
3. Real-Time Alerts: We created a system that instantly detects and notifies anomalies or inconsistencies in data, enabling fast corrective action.
4. Process Automation: We eliminated manual reviews, cutting errors and freeing the team's time for higher-value work.

Centraal streamlined our data governance with a rock-solid digital platform that improved data extraction, curation, and quality across our market benchmarking project. They turned our data into strategic decisions, guaranteeing quality and efficient collaboration.

— Data Architect, Crystal

Results and benefits

Shipping our solution transformed Crystal's pricing strategy:

• Significant acceleration of the price analysis process.
• Much faster response times to market changes.
• Better early detection and correction of data anomalies.
• Stronger collaboration between departments.
• Improved quality and reliability of benchmarking data.
• More informed, strategic decision-making.

We built an advanced analytics platform using Azure Databricks for large-scale data processing, Azure Functions to automate processes, and Great Expectations to guarantee data quality. We used Azure Queue Storage and Azure Logic Apps for workflow orchestration and integration, Azure Data Lake for storage, and Miro for team visual collaboration.