Data is at the center of every transaction and engagement. Collecting that data and transforming it into something usable is the key to making better decisions for your product, pricing, promotion and distribution.
Large amounts of unstructured and semi structured data is continuously generated by different sources at high speed within the entire value chain. From manufacture, distribution, RTM Sales, Retail Execution and Point of Sales. This type of big data is forcing many organizations to focus on how they process, interact and store the data. Cognizance is designed to seamlessly integrate, store and stream your data to make smarter decisions.
Data streaming is the ability to collect and process massive amounts of data generated from different sources. Typically associated with Big Data, streaming data can perform real-time analysis on streaming data without slowing processing.
Cognizance gathers data from all of its retail partners via their designated retail touch points. These touch points can be collected across the value chain, from manufacturing all the way to retail execution. This data is ingested into the Cognizance data pipeline regardless of format, the Cognizance ETL pipeline then cleans and rearranges the data into a format most efficient to be stored in our data lake.
A data lake is a single store or repository of data stored in its natural/raw format stored securely in the cloud. A data lake can include structured data from relational databases, semi-structured data, unstructured data and binary data. Data is transformed and used for tasks such as reporting, visualization, advanced analytics and machine learning.
Artificial Intelligence and machine learning are two incredibly powerful tools that allow Cognizance to find patterns in data that would previously take huge amounts of time and manpower. In order to best utilize these tools, the data that we analyze needs to be stored in a certain manner and kept in a standardized format. Enter ETL pipelines. Extract, Transform and Load pipelines are the key to making sure the Cognizance Data Lake stays clean and performant.