Welcome to our case studies page! Here, you'll find real-world examples of how our data services have helped companies across various industries.
From improving customer service to increasing efficiency, our case studies showcase how data-driven solutions can drive business success.
Browse through our collection to see how we've helped other companies like yours are using data to leverage business success and unlock the full potential of their business. If you have any questions or want to discuss how we can help your business, please don't hesitate to contact us.
Problem: The medical device manufacturing company was facing a challenge in gathering and processing data efficiently. The data collected by the science team was spread across multiple platforms and lacked proper data storage and reporting system. The management team needed a solution that would provide a unified data environment, secure data storage, and a user-friendly reporting system.
Solution: Our team decided to develop a solution based on Microsoft Azure and Power Platform tools to cover the entire data flow of the company. The solution created allowed for an optimal and budget-friendly data architecture that securely and efficiently stores and processes data. Power BI was used for presenting data in the form of KPIs and charts for the management team to track the company's main goals.
Results: Using our data services, the company improved operational efficiency, customer satisfaction, and overall business performance.
Technology Stack: PowerApp, MS SQL Database, Power Automate, Azure Data Factory, Azure Data Lake, Power BI
Problem: A company faced challenges in effectively utilizing their website's data for analytics purposes. The existing data warehouse was disorganized, lacked essential data, and was difficult to interpret. They required a solution that could provide a new, clean, and well-structured data warehouse, enabling easy analysis and visualization for their users.
Solution: We used Google BigQuery and DBT (Data Build Tool) to create a new data warehouse. BigQuery facilitated the storage and processing of datasets, and DBT enabled us to transform and structure the data for optimal analysis. After having the new data warehouse, we connected Metabase to it and created KPIs, Dashboards and a Visualizations.
Results: The implementation of the new data warehouse improved the accessibility and clarity of data for analytics purposes. With a clean and well-structured database, our client gained the ability to get insights from their data. The data warehouse became easy to understand and navigate, leading to enhanced decision-making capabilities.
Technology Stack: Google BigQuery, DBT (Data Build Tool), Metabase
Problem: A huge marketing agency was struggling to keep track of their campaign performance across various channels and clients. They were manually analyzing data from multiple sources, which was time-consuming and prone to errors. They needed a solution that would help them streamline their data analysis and provide insights to optimize their campaigns.
Solution: Our team implemented a business intelligence (BI) solution to help them with data consolidation, dashboard creation, and KPI tracking. We used a data visualization tool to create custom dashboards for their clients. We also integrated their BI solution with Google Analytics, Facebook Ads, and other marketing platforms to track campaign performance in real-time.
Results: They were able to analyze campaign performance across various channels and clients by identifying underperforming campaigns and optimizing them. They also used the insights provided by our BI solution to create personalized marketing strategies for their clients, which helped improve client satisfaction and retention.
Technology Stack: Tableau, Google Analytics, Facebook Ads, and other marketing platforms.
Problem: A pharmaceutical company was struggling to forecast sales and improve its inventory management. They needed to implement data-focused solutions to predict sales trends and optimize inventory levels.
Solution: Our team worked with the pharmaceutical company to implement a data pipeline using cloud technologies such as Google Cloud BigQuery and Apache Airflow. We used machine learning models to analyze sales data and predict future trends, which helped the pharmaceutical company to optimize its inventory levels and improve sales forecasting.
Results: Our data services helped the pharmaceutical company improve its sales forecasting by 20% and optimize its inventory levels, resulting in a 15% profit increase.
Technology Stack: Google Cloud, BigQuery, Apache Airflow
Problem: A major online retailer was struggling to increase revenue and improve customer engagement despite having a large customer base. They needed to optimize their data flow and use data science to predict customer behavior and preferences.
Solution: We worked with the retailer to implement a data pipeline using cloud technologies such as Amazon S3 and AWS Glue. We used machine learning models to analyze customer data and predict their behavior, which helped the retailer to improve their marketing and sales strategies.
Results: Using our data services, the retailer saw a significant increase in revenue and customer engagement, with a 20% increase in sales and a 30% increase in customer retention.
Technology Stack: Amazon S3, AWS Glue, Python, Machine Learning
Problem: The top-rated banking company needed a new data architecture that would optimize data storage and transformation processes, and also prevent fraud by implementing machine learning models.
Solution: The project was built on Microsoft solutions, utilizing Azure Cloud Technologies. Real-time analytics was done using Azure Event Hub and Stream Analytics, and batch processing was done using Azure Data Factory and Databricks notebooks.
Results: Our data services helped implement the new data architecture resulting in a fast and optimal solution that provided meaningful outputs for business decision-making. The system gathered real-time data, processed it on the fly, and implemented complex machine-learning models.
Technology Stack: Azure Data Lake, Azure Event Hub, Azure Stream Analytics, Azure Data Factory, MS SQL Database, Azure Databricks, and Power BI.
Problem: A major financial institution was struggling to optimize its finances and reduce costs. They needed data science to predict future financial trends and improve their decision-making processes.
Solution: Our team worked with the institution to implement a data pipeline using cloud technologies such as Google Cloud Storage and Apache Beam. We used statistical models to analyze financial data and predict future trends, which helped them to improve their budgeting and forecasting processes.
Results: Our data services helped the bank reduce costs by 15% and improved its decision-making processes, resulting in a 10% increase in profits.
Technology Stack: Google Cloud Storage, Apache Beam, Python, Statistics
Problem: A manufacturing company was struggling to predict equipment failures and improve its maintenance processes. They needed to predict when the equipment would fail and schedule maintenance accordingly through the help of data science.
Solution: Our team worked with the manufacturing company to implement a data pipeline using cloud technologies such as Microsoft Azure and Apache Kafka. We used predictive analysis and ML models to analyze equipment data and predict when the equipment would fail, which helped the manufacturing company to schedule maintenance and reduce downtime.
Results: Our data services helped the manufacturing company reduce downtime by 25% and improved its maintenance processes, resulting in a 15% increase in productivity.
Technology Stack: Microsoft Azure, Apache Kafka, Azure Machine Learning
Problem: A payment processing company was struggling to detect and prevent fraud. They turned to data science to predict fraudulent behavior and improve fraud detection processes.
Solution: We worked with the payment processing company to implement a data pipeline using cloud technologies such as Amazon Redshift and AWS Lambda. We used machine learning models to analyze transaction data and predict fraudulent behavior, which helped the payment processing company to improve its fraud detection processes and reduce losses.
Results: Our data services helped the payment processing company reduce fraudulent transactions by 30% and improved its fraud detection processes, resulting in a 20% reduction in losses.
Technology Stack: Amazon Redshift, AWS Lambda, Machine Learning