NGDATA today announced the availability of Lily 2.0, which is designed to give companies a 360 degree view of their customers through structured, unstructured and semi-structured data from multiple sources.

Lily 2.0 includes a new set of modules including: the Lily Customer Database, Lily Customer Applications, NGDATA Data Services as well as enhancements to the Lily Data Repository. Lily targets a variety of Big Data sources, accesses complete customer profiles in real-time and takes into account a customer’s latest interactions across multiple channels (CRM, social media, mobile) instead of relying on batch data to make marketing and sales decisions. 

“The ability to provide a comprehensive view of the customer and predict their propensity towards purchasing products will provide companies with a distinct competitive advantage. Today’s consumers often make purchase decisions based on their most recent actions, interactions and social engagements, in addition to their historic behavior,” said Benjamin Woo, managing director of industry research firm Neuralytix, Inc. and analyst at GigaOM Pro.

New modules include:

• The Lily Customer Database: uses matching and machine learning capabilities to correlate all data from hundreds of internal and external sources to a unique customer. By aggregating data based on behavioral data, Lily can build and maintain customer records that contain all data related to each individual customer including purchase history, web clickstreams, geolocational information and social interactions. The Lily Customer Database integrates with applications such as SAP Business Objects, Tableau, SAS/ACCESS, Microstrategy, QuickView and Hive for exploratory analytics and business intelligence applications.
• Lily Customer Applications: The Lily Recommendation Engine uses machine-learning algorithms such as collaborative filtering to deliver targeted, contextual and personalized product recommendations. Enterprises can predict an individual’s preferences toward a product or service by observing group behavior such as purchase history, brand affinity, social interaction, POS transactions, web clickstreams and geo-location information to deliver highly accurate recommendations. The Recommendation Engine can be integrated into any existing application such as mobile wallet or a fraud detection application.
• The Lily Data Repository: delivers new connectivity, indexing, scalibility, support capabilities for SolrCloud as well as certification on the latest Hadoop and HBase versions. As a result, enterprises can aggregate, store, index and retrieve a wide variety of internal and data into a native Big Data platform.
• NGDATA Data Services: augments existing customer data in Lily with other data sources such as social network data or reference databases, such as Lexis-Nexis and market and consumer data. Pre-integrated into the Lily Customer Database, NGDATA Data Services speeds up implementation by making external data available.