IBM Lays Industry Specific Powerful Behavior Based Predictive Analytics

predictive analytics predictive models 300x183 IBM Lays Industry Specific Powerful Behavior Based Predictive Analytics

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The challenge for organizations is to capture, manage and make sense of their data in real time so that many employees can make better decisions faster. In the course of time, predictive analytics evolve and becomes accessible to all enterprise users, regardless of their industry.

IBM is trying to position the company as the leader in predictive analysis and announced a new set of 20 new predictive analytic solutions unique to each industry. Using the new IBM applications, companies can get quick answers to questions such as how many different color combinations fabrics should we continue to sell in our stores? People who spend time at the table after 20:00 are they more likely to be exposed or late to repay their bank loans? When should we stop the production of an oil well to pump maintenance?

The new generation of IBM solutions can be applied to several sectors – banking, telecommunications, insurance, automotive, energy and other sectors. At the heart of these solutions is a set of data preparation tools for the integration of specific data sources for industry such as pre-built models and predictive analysis of its own, and tables interactive and specific board to help business users to share findings between teams and organizations.

IBM says, “the new solutions draw on company’s vast industry and analytics expertise from over 50,000 client engagements. Each solution includes pre-built predictive analytic modeling patterns and interfaces for focused industry use cases, as well as data preparation capabilities to manage unique data and streamline the collection and preparation of data for analytics. With interactive and role-specific dashboards, business users can share predictive insights across teams and organizations that can give them a deeper understanding of their customers, assets and operations to help them make better decisions and act with greater speed and fewer resources.”

In the area of Banking, the AML Monitoring and Analytics for Financial Services, the Multi-Channel Fraud Analytics and Behavior-based Customer Insight operate, respectively, in terms of mitigating money laundering risks, prevention of financial fraud and customization customer experience.

The Asset Analytics for Rotational Equipment is designed by IBM solutions for the chemical and oil industry and uses the predictive analysis to anticipate operational breaks and enhance the reliability and availability of critical equipment.

In the financial segment, Regulatory Compliance and Control and Trade Compliance Analytics enable better risk management and regulatory compliance and comprehensive view of the market, using analytical capacity.

In the area of consumer products, the technology company has created a solution that you want to study the customers’ consumption behavior and their reactions to certain marketing strategies. The Social Merchandising allows enhancing the operational efficiency of companies that wish to reach a particular group of consumers.

In the sphere of insurance, the IBM Analytics Solution Insurance gives a greater customer experience and reduce the dropout rate, the Producer Life Cycle and Credential Management help insurers automate processes, and the Property & Casualty Claims Fraud enables a more efficient identification and needs of fraud incidents.

For the media and entertainment area players, IBM developed the Behavior-based Audience Insight to a better understanding of their audiences and more efficiently create advertising and marketing campaigns and new content.

In April, following in the footsteps of its closest competitor Microsoft, Amazon announced the Amazon Machine Learning, a fully managed cloud service designed to draw useful information from mountains of data that it is sometimes difficult to exploit, for reasons of complexity, time or energy.


CloudTimes

SAP HANA Dresses for Internet of Things and Predictive Analytics

saphana 300x95 SAP HANA Dresses for Internet of Things and Predictive AnalyticsInternet of Things is a new and hot territory today. In last few weeks, Google has demonstrated its interest in the IoT market with project Brillo, a project to connect any device used, not only smartphones, tablets, computers and smartwatch, but also those that are part of everyday life such as home appliances, cars, surveillance systems etc. Cisco also announced the addition of 35 new members and new Intercloud initiative for the Internet of Everything market.

Now, SAP plans to offer companies better tools for big data and the Internet of Things. The latest version of SAP HANA called Service Pack 10 (SPS 10) allows customers to communicate with objects connected to the scale of the business and more efficiently manage large amounts of data. The function of synchronization of remote data, which can synchronize the data between the enterprise and remote sites to the network edge, is one of the most notable innovations in the new version.

Developers can now build IoT Mobile apps and generate large volumes of data that take advantage of the data synchronization distance between the company, and Hana remote locations via the integrated SAP database embedded SQL Anywhere technology. So some data previously scattered in various production areas such as restaurants, and remote locations such as gas stations, vending products, and other sources can be traced, be accessible and re-injected more easily.

This concerns the applications of the Internet of Things, for example, the analysis of data from sensors in the field to better plan preventive maintenance actions that avoid the occurrence of faults. Moreover, the extensive capabilities of integrating Hana data is compatible with the latest Hadoop distribution Cloudera and Hortonworks. Among other remarkable features are the faster data transfer with Spark SQL and the ability to move data between storage tiers.

The opening up to the IoT not only about SAP SQL Anywhere. HANA sees improved other related functions such as streaming data from thousands of sensors in the field and their peaks of transmissions. There is also an architecture IoT layered in which some devices are the gateway and pre-process the events of their area to a deputy before transferring the data to HANA.

Also, SAP expanded language support for the Texanalyse to 32 languages. The SAP HANA text mining now supports SQL syntax. Thus, it will easier for developers to write new applications based on text mining.

SAP has also evolved the analysis capabilities of its software, not just in the core of its Hana platform, but also in its predictive analytics portfolio. For example, version 2.2 of the SAP Predictive Analytics suite has been modified to accommodate large data sets that can be used for predictive modeling.

Its automatic predictive library has been enriched by a large number of algorithms. Other more oriented enhancements such as integration of R model comparison capabilities are also included in the new additions.

In March, IBM announced investment of $ 3 billion to set up a new dedicated business unit for Internet of Things. The new offer is initially aimed at companies with tourism market, logistics, insurance, public services, transport and retail. Samsung also announced the launch of SmartThings Open Cloud and Artik platform, which will help developers create innovative products and services for the IoT using their connected devices.


CloudTimes