Big Data Case Studies: Successful Projects executed by Orzota

Read about some of the Big Data projects Orzota has executed. The selection includes big data case studies in a diverse set of industries including finance, retail, and high tech and includes data architecture, implementation, and analytics.

Fast SQL Queries

Running complex SQL queries is essential for business analysts. When the data sets are very large, performance can be a challenge. We reduced query times from minutes to seconds for a large retailer trying to run multi-dimensional queries on Hadoop.
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big data case studies: customer-analytics
customer-analytics

Customer Churn Analytics

For any consumer-facing company such as banks, retail e-commerce, telecom, etc. one of the key factors to revenue and profitability is customer retention. An e-commerce company wanted to understand the factors that led to customer churn and predict which customers may churn.
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Customer Knowledge Solution

Companies like Amazon serve dynamic content, send personalized recommendations in email campaigns and tailor ads based on our browsing behavior; but this hides the fact that 80% of marketers fail at personalization. A Big Data based Customer Knowledge Solution helps solve the problem.

cks-new

 

social

Real-time Social Analytics Platform

A Silicon Valley startup building a social analytics platform ran into performance issues as they started scaling the amount of social media data to analyze. They needed an architecture that could ingest a large amount of social media data in real-time.
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Data Warehouse Augmentation

A large manufacturing company wanted to improve the reliability of their products by predicting component failure and taking proactive action to repair/replace it. This would result in a saving of millions of dollars for their business operations. Solving the problem required receiving and processing very large amounts of sensor data sent by the components.
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datawarehouse

 

Offload Data

Off-load Data Warehouse

A large bank wanted to reduce the load on their Enterprise Data Warehouse as it was reaching its capacity and upgrading it was prohibitively expensive. Maintaining multiple copies for backup and high availability added to the cost of the warehouse. At the same time, it was important that the business users have access to the same analytics and reports using the same BI tools.
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