Business Solutions

Ad Targeting

Online advertisers and marketers measure their effectiveness — and grow their revenues — based on their ability to place a relevant ad where a motivated consumer will see it. This “battle for the consumer’s mind” relies on precise Big Data analytics that integrate behavior, location, demographics and even such external factors as the weather forecast.

• Reliably handle thousands of impressions and click data events per second
• Bring multiple data sources to bear for greater customer context
• Leverage intelligent algorithms to serve better ads and gain real-time insight

See the App Reference Design we developed that addresses this use case: Ad Publisher Conversion Tracking.

Customer Insights

When enterprises know in real-time what consumers think about their products or brands, they can take immediate action to monetize opportunities they may have missed, or to take corrective action to respond to an emerging customer satisfaction issue or to repair a negative impression. Big Data applications such as social media monitoring, sentiment analysis, micro-segmentation and behavior analysis provide the essential insights.

• Keep a real-time pulse on multiple data streams, including social media, customer transactions and website clickstreams
• Ask detailed questions of data, and quickly develop applications for repeatable insight

See the App Reference Designs we developed that addresses this use case: Customer Experience Management & Social Media Analytics.

Personalization

A vital strategy for enterprises is to give consumers a highly personalized, interactive experience that is tailored as much as possible to each consumer’s preferences and interests. The goals are clear: to increase loyalty, customer satisfaction and, of course, sales. Personalization requires real-time, dynamic content insertion, on-the-fly customization and complementary product recommendation all driven by sophisticated Big Data analytics.

• Power dynamic content and recommendations with insight-producing real-time analytics
• Implement Big Data using existing web developer talent
• Eliminate the system operations burden of Big Data

Business Intelligence

The modern enterprise needs to use all the data from every source to analyze and understand the operational metrics that drive better business decisions. The focus of BI solutions is to collect, measure, analyze and report through comprehensive decision support systems. In addition to leveraging new Big Data sources, enterprises also need to integrate legacy BI tools and data.

• Integrate with virtually any data source in the enterprise including CRM, social media, ERP, machine data, unstructured document data and more
• Integrate with all leading and legacy BI tools and solutions
• Gain deep visibility into the processes and metrics that drive your business
• Quickly deploy powerful BI applications

Log Analysis

Computer networks, websites and sensor grids generate massive amounts of data capturing the details of every event and interaction. This data provides a wealth of operational information and critical insights to help enterprises optimize network and website operations, and even to improve consumers’ website experience.

• Reliably handle extremely high data throughput from hundreds of server and application data sources
• Eliminate the system operations burden of Big Data
• Leverage all the best-of-breed Big Data processing frameworks, including Hadoop, Storm, Kafka and more

Mobile Data Analytics

There are nearly as many smartphones as there people on the planet, and each one (talking about the phones) creates a lot of data that can be used to drive an almost limitless number of mobile apps. The critical element that powers these mobile apps is real-time, location-based Big Data analytics.

• Tap into your distributed network of mobile data sources with a high throughput, streaming data collection framework
• Power rich insights with a fully-equipped Hadoop data science laboratory
• Gain deep visibility into data flows and key metrics

Fraud Detection and Risk Analysis

Enterprises that operate online in the financial sector, as well as other industries, are an especially vulnerable target for fraud and other data-driven malicious activity. Enterprises continuously sift through the details of every measurable event and transaction seeking the often-subtle signatures that indicate an act of fraud or some other business risk factor. Predictive analytics and forensic data analysis are key techniques, requiring effective use of massive amounts of data.

• Develop powerful, insight-producing analytics with a fully-equipped Hadoop data science laboratory
• Leverage multiple data sources to optimize algorithm success
• Focus on gaining insights, not managing infrastructure

Machine-to-Machine and Sensor Data

The so-called “Internet of things” is comprised of countless machines and sensors that collect and communicate an endless stream of data. Smart meters that optimize power consumption in homes and office buildings, RFID tags that streamline logistics, remote sensors that monitor un-attended facilities around-the-clock — the data from all kinds of devices must be combined and analyzed in real-time to effectively manage enterprise assets and resources.

• Tap into your distributed network of machines and sensors with a high throughput, streaming data collection framework
• Build out complete enterprise workflows and production applications on a reliable, fault-tolerant data backbone
• Gain real-time visibility into data flows and key business metrics

See the App Reference Design we developed that addresses this use case: Predictive Analytics for Manufacturing.