Match and analyze disparate volumes of structured and unstructured customer data for deeper customer insights
Find and connect the customer data that matters most
IBM InfoSphere® Big Match for Hadoop helps you analyze massive volumes of structured and unstructured data to gain deeper customer insights. It can enable fast, efficient linking of data from multiple sources to provide more complete and accurate customer information — without the risks of moving data from source to source. The solution supports platforms running Apache Hadoop such as Cloudera.
Enables you to find information buried in your data — like Twitter feeds, email, call center logs — and match them to entities, such as customers from other sources, to gain a more complete picture.
Uses probabilistic matching technology, combined with big data accelerators and text analytics, to extract relevant information and help you connect customer identities at the speed of business.
Reduces bottlenecks in data parsing and integration by loading all data natively in the Hadoop environment for faster analysis and integration, with fewer constraints.
Features
Uses statistical learning algorithms and a probabilistic matching engine running natively within Hadoop for fast and more accurate customer data matching.
Provides configurable prebuilt algorithms and templates to help you deploy in hours instead of spending weeks or months developing code. Uses distributed processing to accelerate matching of big data volumes.
Provides support for Java and REST-based APIs, which can be used by third-party applications.
Provides search functions, as well as export — with entity ID — and extract capabilities to allow data to be consumed by downstream systems.
Provides Spark-based utilities and visualization to further enable analysis of results. Spark’s advanced analytics and data science capabilities include near real-time streaming through micro batch processing and graph computation analysis.
Uses statistical learning algorithms and a probabilistic matching engine running natively within Hadoop for fast and more accurate customer data matching.
Provides configurable prebuilt algorithms and templates to help you deploy in hours instead of spending weeks or months developing code. Uses distributed processing to accelerate matching of big data volumes.
Provides support for Java and REST-based APIs, which can be used by third-party applications.
Provides search functions, as well as export — with entity ID — and extract capabilities to allow data to be consumed by downstream systems.
Provides Spark-based utilities and visualization to further enable analysis of results. Spark’s advanced analytics and data science capabilities include near real-time streaming through micro batch processing and graph computation analysis.
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