There is a lot of media attention as relates to the Internet of Things (IoT) in the world of Big Data with the promise to improve service, reduce fraud, and deliver innovative products. But none of this happens overnight. Most Big Data initiatives that strive to take advantage of IoT are typically implemented using a phased approach. Many organizations start their big data journey with data warehouse optimization to reduce costs and to establish Hadoop as part of the foundation for an architecture that can support their big data projects. The vision is to build out a data lake that can store, process, and manage all types of data including social media and IoT machine data at any scale, primarily for data exploration and discovery of new business insights.
By adding MDM and real-time streaming capabilities to a Big Data initiative, you can create a more complete view of customers and deliver real-time operational intelligence that optimally predicts next-best offers to customers, improves fraud detection and cyber security, and improves total customer experience.
The use-cases for Big Data Streaming Analytics require more sophisticated data platforms than traditional streaming analytics. For example, one Informatica customer uses big data streaming analytics as part of their risk reduction program to proactively reduce money transfer fraud and AML. Fraud and AML detection requires a big data management platform that can operationalize a predictive model which detects and responds to fraudulent and AML patterns in real-time. These predictive models evolve and improve over time through an iterative and agile approach to Big Data Streaming Analytics.
The only proven method that supports such an iterative and holistic approach to this example of Big Data Streaming Analytics and others is a Big Data Management platform that can:
Acquire all types of data at any latency: Ingest all types of data (e.g.
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