The core of Apache Hadoop consists of storage known as Hadoop Distributed File System (HDFS) and processing called MapReduce. Hadoop has the ability to store, manage, and analyze large amounts of data – structured and unstructured quickly, reliably and flexibly and that to at a low-cost. One of its major benefits include – distributed processing of data local to each node in a cluster. Its reliability stems from the fact that when a node fails Hadoop processing is re-directed to other nodes in the cluster and data is automatically re-replicated in preparation for future node failures.
Finesse has a team of Big Data and Hadoop experts that can jump in with best in class practices, processes and tools to get you moving in the right direction with your predictive analytics initiative. Put our experience to test. Drop us a line and let’s start the conversation.
Problems that can be solved using Predictive Analytics:
It cost 10X more to acquire a new customer Vs keeping an existing one. A combination of data management and analytics could help you minimize overlooking critical signals regarding customer engagement and customer interaction. Using Big Data technology allows organizations to build a 360degree customer profile to capture, grow and retain customers.
Optimize Supply Chain and Improve product quality and service across the supply chain.
Improve insights in localize job markets and matching of job demand and supply
The Client a consulting company specializing in the implementation of Business Development Initiatives within Automotive Dealerships wanted to the achieve the following:
Key Performance Indicators Measured were:
Based on in-store customer’s location data various metrics and customer engagement KPIs were designed. Graph based machine learning and Clustering algorithms were used to identify customer groups. Then a second level algorithms were developed to estimate