Phunware Launches MaaS Customer Data Platform

Phunware, providers of an enterprise cloud platform for mobile, has launched Multiscreen-as-a-Service (MaaS) Customer Data Platform (CDP).

Phunware's MaaS CDP is cloud-based and includes software and proprietary algorithms that aggregate and organize customer data across a variety of touchpoints in real time to create persistent, unified customer databases. These databases benefit from more than 16 billion unique Phunware IDs and facilitate unique data segmentation that is accessible and consumable within core enterprise engagement packages, including CRM and marketing automation systems.

"Our MaaS Customer Data Platform was designed to ensure that our customers can deliver the right content to the right screen at the right time in the right place in a mobile-first world," said Luan Dang, co-founder and chief technology officer of Phunware, in a statement. "The depth and breadth of our data was built over a decade of mobile operations and includes more than a petabyte of information unique to tens of billions of discrete events across billions of devices and thousands of mobile applications that have touched our platform globally."

Phunware's MaaS CDP collects and organizes identity data, descriptive data, quantitative data, and qualitative data. It is capable of processing billions of transactions per day and 300,000 to 500,000 transactions per second for producing ready-to-use, mobile-centric datasets for analytics and data science by cleaning, enriching and organizing the underlying big data into industry standard formats for use with leading data platforms and data science tools.

Components of Phunware MaaS CDP include the following:

• Phunware Audiences, a self-service system that enables marketers to build scalable, granular mobile audiences that can be delivered to programmatic advertising platforms for activation;
• Phunware Knowledge Graph, which integrates diverse data into a mobile domain ontology and can derive deep insights and new knowledge from highly-connected, large-scale data;
• Data integration tools that populate the Phunware Knowledge Graph with real-time data updates via a streaming microservice, as well as periodic updates using bulk loader utilities;
• Visualization tools to explore and query connected data to derive insights from patterns, clusters, and connections;
• Machine learning to derive insights using clustering, classification, centrality, and pathfinding or custom algorithms to execute within the graph or on cloud-based machine learning platforms;
• Application Programming Interface (API) to create custom queries with popular programming languages to derive insights, generate mobile audiences. It simplifies knowledge extraction using RESTful programmatic methods, including polygon and radial location queries and asynchronous file exports.
• Phunware Ontology, the taxonomy that comprises formal naming and definition of entities and the relations between the entities within Phunware's Knowledge Graph. Phunware's self-descriptive, extensible ontology is based on open and connected W3C standards (RDF / OWL) that simplify the interpretation of entities, properties, and relationships between diverse data with popular vocabularies, such as schema, FOAF, and SKOS.
• Phunware Privacy that complies with the General Data Protection Regulation (GDPR) and the European Union/U.S. Privacy Shield Framework for collection, use, and retention of personal information.

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