WibiData, Inc. operates a platform for data storage, serving, and analysis. It offers graphical tools to export the data from its distributed data repository into relational database. The company also provides schemas, real-time data retrieval, and analyst tools; WibiEnterprise, an enterprise software platform for developing and deploying real-time big data applications; and WibiRetail, a software platform for retailers to deploy algorithmically-driven personalized shopping experiences. WibiData, Inc. was formerly known as Odiago Inc. and changed its name to WibiData, Inc. in 2012. The company was founded in 2010 and is based in San Francisco, California.
375 Alabama Street
San Francisco, CA 94110
Founded in 2010
WibiData Launches WibiRetail
Jun 11 14
WibiData announced the launch of WibiRetail, a new software platform designed for retailers to rapidly deploy algorithmically-driven personalized shopping experiences. Ideal for marketing, merchandising and data science teams, WibiRetail empowers retailers to deliver experiences that amaze and delight their customers. WibiRetail has been designed from the ground up to address the gap between the consumer experiences that modern machine-learning and predictive analytics technologies are capable of delivering and what currently exists. Most large retailers, whether department stores, specialty stores or mass merchandisers, are supporting their digitally-connected customer touchpoints with segmentation, manual analysis and one size fits all product recommendation solutions. The result is a universe of shopping experiences that are largely conservative, unimaginative and undifferentiated.
WibiData Launches WibiEnterprise 3.0 to Power Real-Time Big Data Consumer Applications
Nov 25 13
WibiData announced the launch of WibiEnterprise 3.0, an enterprise software platform for developing and deploying real-time Big Data Applications. WibiEnterprise 3.0 bridges the gap between Hadoop and the application layer, empowering companies to connect consumer applications with sophisticated real-time, data-driven features like personalized content, predictive recommendations, dynamic micro-segmentation, personally relevant search results and anomaly detection. WibiEnterprise 3.0 is based on company's Kiji Project, an open source framework for building Big Data Applications, and is used in production by some SaaS, retail and financial services companies. Kiji was conceived to directly address a common and growing problem: companies have invested heavily in capturing and storing their customer data, typically using a combination of products and services in the Hadoop and HBase ecosystems, but are falling short in utilizing their data and putting insight to use in the form of real-time, revenue-generating, dynamic applications. As a result, many companies are not yet seeing any return on their big data infrastructure investments. Sophisticated data modeling and in-application analytics often requires coordinating a complex integration of advanced data science, developer tools and corresponding talent. WibiEnterprise 3.0 simplifies this process by allowing data scientists to explore data, develop and train models, and deploy the best models to production where they are scored on the fly, delivering real-time, individualized and contextually relevant experiences across application channels. Application developers can use WibiEnterprise 3.0 to easily record information in real-time for use in dynamically updated predictive models, and deliver results to front-end applications, whether on the web, mobile devices, or other digital channels. WibiEnterprise 3.0 gives companies the ability to collaborate across functions, experiment with the best analytical models and create better application experiences by reducing friction in sales and servicing across application channels. The core features of WibiEnterprise 3.0 are: Schema Management: a framework for defining tables and datasets that allows for consistent evolution over time without downtime. Batch Processing: a framework for bulk imports, complex analysis tasks and other MapReduce jobs. Machine Learning Model Lifecycle: a framework for authoring machine learning models, batch model training, model deployment and real-time model scoring. Data Exploration: a framework for ad hoc query and analysis of application customer data. Application Integration: a framework for integrating with other applications through RESTful interfaces for reads, writes and predictive model scoring.