IBM SPSS Modeler is a powerful, versatile data and text analytics workbench. Learn how you can build accurate predictive models quickly and intuitively, without programming. So you can use data to understand the current state of your organization and get a view into the future.
Data mining uncovers patterns in data through a variety of predictive techniques. By engaging in data mining, organizations like yours gain greater insight into external conditions, internal processes, your markets – and your customers.
HP Proactive Care helps prevent problems and stabilize IT by utilizing secure, realtime, predictive analytics and proactive consultations when your products are connected to HP.
• Problem prevention
• Predictive analytics for proactive reports
• Personalized consultation with a Technical Account Manager
• Rapid access to advanced technical experts
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This white paper outlines some of the major factors affecting inventory and supply chain management, and covers practices and capabilities that distinguish the most successful approaches to this complex process. You'll learn the factors to consider as you evaluate the maturity of your organization's supply chain and inventory management processes as well as recommendations for incremental improvements that can enable you to cost-effectively upgrade your inventory management system, one step at a time.
As part of the IBM® SMART Program, Andrews Distributing worked with Southern Methodist University to develop a predictive analytics solution that mines data on 1,000 SKUs sold at 10,000 retailers, and reveals new insights about beer category sales in different retail environments.
Four technology trends—cloud computing, mobile technology, social collaboration and analytics—are shaping the business and converging on the data center. But few data center strategies are designed with the requisite flexibility, scalability or resiliency to meet the new demands. Read the white paper to learn how a good data center strategy can help you prepare for the rigors and unpredictability of emerging technologies. Find out how IBM’s predictive analytics are helping companies build more accurate, forward-looking data center strategies and how those strategies are leading to more agile, efficient and resilient infrastructures.
IBM’s Social Media Analytics solution helps your company take advantage of everything social media has to offer in terms of getting closer to your customers, combining social media analysis and reporting with IBM SPSS predictive analytics technology.
This white paper discusses how IBM InfoSphere can support the integration and governance of Big Data in healthcare. The white paper reviews three case studies including predictive analytics with Electronic Medical Records, time series data in a neonatal intensive care unit and predictive pathways for disease.
In this paper – which is based on a webinar hosted by the Direct Marketing Association (DMA) and sponsored by SAS – we will take a look at how these technological advancements can enable you to become more predictive and prescriptive in your digital and integrated marketing efforts.
AgilOne is a cloud-based predictive intelligence company that is experiencing a high growth rate. The company needed to find, and quickly deploy, a scalable storage solution to support its big data performance and future scalability requirements. Today, utilizing its 3PAR StoreServ storage solutions, AgilOne boasts a lower cost per terabyte, higher efficiency, and the ability to rapidly accommodate its new customers.
Watch this video to see how Denmark’s leading retailer, Coop Danmark, is using predictive analytics and real-time data access to understand customer demand and make more profitable merchandising decisions.
When you have voluminous data sets growing at an unabated rate, in formats that are unique and complex, how do you address your backup and recovery needs? While relying on traditional backup and recovery solutions can complicate matters, a better approach is to use a solution that enables your IT organization to make decisions that are more intelligent by integrating predictive analysis and an adaptive approach to backup and recovery.
For banks today, having more ways to communicate with customers is a good thing. But it has also made it harder for banks to figure out where and how to most profitably commit their marketing resources.
Leveraging predictive analytics, First Tennessee Bank is applying the ultimate acid test. It’s combining a granular understanding of the needs of customer segments with real P&L data to optimize its marketing spend, focusing on programs that deliver the highest ROI.
First Tennessee’s ability to target its campaigns more intelligently has increased its response rate by 3.1 percent, cut key marketing costs by nearly 20 percent and enables the bank to get the most from its resources.
In a competitive landscape that favors the fastest and the smartest, financial services firms that invest in building sophisticated insight and predictive analytics will be better positioned to emerge as market leaders.
Customer Profitability Analytics enables banks to analyze customer, account, product, and transaction data and apply costing models to determine a bank-wide view of profitability. Applying predictive analytics, they can model future behavior and derive a lifetime value for each customer.
It’s an exciting yet daunting time to be a security professional. Security threats are becoming more aggressive and voracious. Governments and industry bodies are getting more prescriptive around compliance. Combined with exponentially more complex IT environments, security management is increasingly challenging. Moreover, new “Big Data” technologies purport bringing advanced analytic techniques like predictive analysis and advanced statistical techniques close to the security professional.
Large organizations can no longer rely on preventive security systems, point security tools, manual processes, and hardened configurations to protect them from targeted attacks and advanced malware.
Henceforth, security management must be based upon continuous monitoring and data analysis for up-to-the-minute situational awareness and rapid data-driven security decisions. This means that large organizations have entered the era of data security analytics.
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To develop the visibility, agility and speed to deal with advanced threats, traditional security strategies for monitoring, often based around security information and event management (SIEM) systems need to evolve into a central nervous system for large-scale security analytics. In particular, four fundamental capabilites are required:
1. Pervasive visibility
2. Deeper analytics
3. Massive scalability
4. Unified view
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