With the new TIBCO Spotfire® A(X) Experience, we are revolutionizing analytics and business intelligence. This new platform accelerates the personal and enterprise analytics experience so you can get from data to insights in the fastest possible way. With the fusion of technology enablers like machine learning, artificial intelligence, and natural language search, the Spotfire® X platform redefines what’s possible for analytics and business intelligence, simplifying for everyone how data and insights are generated, consumed, and acted on. Download this whitepaper to learn more, then check out the new Spotfire analytics. It’s unlike anything you have ever seen. Simple, yet powerful, it changes everything.

When any organization starts planning for cloud-native applications, it is important to consider the entire time span: from selecting a development platform until an application is truly production-grade and ready for delivery in the cloud. It can be a long journey, with many decisions along the way that can help or hinder progress. For example, at the beginning of a move to cloud-native development, it is easy for inefficiencies to occur if developers begin selecting tools and frameworks before they know where the application will be deployed. While enterprise developers want choice of runtimes, frameworks, and languages, organizations need standards that address the entire application life cycle in order to reduce operational costs, decrease risks, and meet compliance requirements. Organizations also want to avoid lock-in, whether it is to a single provider of cloud infrastructure or the latest architectural style. In addition, given the steep learning curve in cloud development, con

The performance of enterprise applications will have a direct impact on business activities and outcomes. The quality of the delivery of applications will depend on how smoothly the underlying data infrastructure operates. ? Optimal application performance and delivery is difficult to achieve in complex environments. ? Many IT infrastructure and operations teams are stretched to the breaking point. ? Predictive analytics and machine learning can be applied to great effect

"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out: Top 5 causes of downtime and poor performance across the infrastructure stack How machine learning and predictive analytics can prevent issues Steps you can take to boost performance and availability"

Cisco's 2015 Mobility Landscape Survey provides new insights into how mobility is being approached by mid-market and enterprise organizations globally Specific objectives include: • Discovering insights into current strategy and future plans • Identifying priority mobility initiatives and top challenges • Learning how IT and Business leaders’ views, priorities and objectives align and differ • Building a global and local view from over 1400 respondents across 14 countries in every geography

"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out: -Top 5 causes of downtime and poor performance across the infrastructure stack -How machine learning and predictive analytics can prevent issues -Steps you can take to boost performance and availability

Forward-thinking enterprises understand what it takes to be successful in this data-rich, increasingly automated economy. According to the Harvard Business Review Analytic Services research report The Rise of Intelligent Automation: TurningComplexity into Profit, sponsored by Oracle, at least 7 in 10 executives understand that predictive analytics (80%) and AI and machine learning (68%) are important for the future of the business. Even as executives recognize the vital role data plays in their businesses, many are unable to take advantage of the value residing in their data. The old ways of collecting, managing, storing, and analyzing data are no longer effective, and are preventing businesses from extracting potential value. Many simply can’t execute on a data-driven vision.

Stories and statistics behind successful analytics projects The adoption of analytics across the enterprise is accelerating, and with good reason. Analytics can offer a competitive advantage by helping to identify growth opportunities, circumnavigate risk and improve customer relationships. These insights are becoming crucial parts of the business strategy for executives representing a wide array of industries. Check out our latest eBook to see how some of the world’s leading companies are using analytics to meet their needs. You’ll receive diverse examples of how organizations applied the latest statistical methodologies, such as: scorecard build, regression, decision trees, machine learning and material change to uncover meaning in data. The examples represent global brands across critical industries – Financial Services, Insurance, High-Tech, Aerospace, Manufacturing and others – where analytics helped answer their most challenging questions.

"Overwhelmed by the number of vulnerabilities your team faces? Uncertain which cyber threats pose the greatest risk to your business? You’re not alone. Cybersecurity leaders have been grappling with these challenges for years – and the problem keeps getting worse. On average, enterprises find 870 vulnerabilities per day across 960 IT assets. There just isn’t enough time or resources to fix them all. More than ever, it’s essential to know where to prioritize based on risk. Download the new whitepaper “Predictive Prioritization: How to Focus on the Vulnerabilities That Matter Most” to: -Learn how to focus on the 3% of vulnerabilities that have been – or will likely be – exploited -Uncover why CVSS is an insufficient metric for prioritization – and the key criteria you need to consider -Understand the Predictive Prioritization process, which uses machine learning to help you differentiate between real and theoretical risks Ensure you’re prioritizing the right vulnerabilities for your t

"Overwhelmed by the number of vulnerabilities your team faces? Uncertain which cyber threats pose the greatest risk to your business? You’re not alone. Cybersecurity leaders have been grappling with these challenges for years – and the problem keeps getting worse. On average, enterprises find 870 vulnerabilities per day across 960 IT assets. There just isn’t enough time or resources to fix them all. More than ever, it’s essential to know where to prioritize based on risk. Download the new whitepaper “Predictive Prioritization: How to Focus on the Vulnerabilities That Matter Most” to: -Learn how to focus on the 3% of vulnerabilities that have been – or will likely be – exploited -Uncover why CVSS is an insufficient metric for prioritization – and the key criteria you need to consider -Understand the Predictive Prioritization process, which uses machine learning to help you differentiate between real and theoretical risks Ensure you’re prioritizing the right vulnerabilities for your t

Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data. Amazon CEO Jeff Bezos spoke about the potential of artificial intelligence and machine learning at the 2017 Internet Association‘s annual gala in Washington, D.C., “It is a renaissance, it is a golden age,” Bezos said. “We are solving problems with machine learning and artificial intelligence that were in the realm of science fiction for the last several decades. Natural language understanding, machine vision problems, it really is an amazing renaissance.” Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy

Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data

Looking to improve accuracy of enterprise budgeting and financial planning? Just 1% of firms can forecast costs at 90% accuracy 30 days out. This white paper by BPM Partners and sponsored by Unit4 shows how Artificial Intelligence (AI) makes a real impact on budgeting, planning, and analysis, and when complemented with deep machine learning, gives greater accuracy to data, budget rules and budget drivers. Download now and learn how AI can help auto-tune your FP&A.

The misuse or takeover of privileged accounts constitutes the most common source of breaches today. CA Threat Analytics for PAM provides a continuous, intelligent monitoring capability that helps enterprises detect and stop hackers and malicious insiders before they cause damage. The software integrates a powerful set of user behavior analytics and machine learning algorithms with the trusted controls provided by CA Privileged Access Manager (CA PAM). The result is a solution that continuously analyzes the activity of individual users, accurately detects malicious and high-risk activities and automatically triggers mitigating controls to limit damage to the enterprise.

Hear from IDC analyst Mickey North Rizza and an SAP's Neil Krefsky as they discuss what intelligent ERP is, how it will impact your business, and how to get started. We will cover how an intelligent ERP will help to to: Reduce complexity within your enterprise Speed up decision making and time to value Bring machine learning to the finance function

Learn why advanced analytics tools are essential to sustain a competitive advantage. This white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics.

E-learning is already known to be the most efficient and cost effective way to deliver training to a workforce. However, the best way to truly prepare employees for meeting organisational objectives is to focus the training on three major areas of skill development. Using a learning solution that is capable of delivering optimal training on each type of skill and checking for comprehension by examining the outcomes from other HR processes will drive performance within the company as employees develop their soft, functional and compliancebased skills.

Mainframes continue to provide high business value by combining efficient transaction processing with high-volume access to critical enterprise data. Business organizations are linking mobile devices to mainframe processing and data to support digital applications and drive business transformation. In this rapidly growing scenario, the importance of providing excellent end-user experience becomes critical for business success.This analyst announcement note covers how CA Technologies is addressing the need for providing high availability and a fast response time by optimizing mainframe performance with new machine learning and analytics capabilities.

TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.

Data is the fuel driving rapid innovation powered by artifi cial intelligence. Enterprises need modern data platform purpose-built for machine learning, accelerating insight while simplifying complex data pipelines in analytics.

IBM PowerAI Enterprise helps to make deep learning easier and faster for organizations by bringing together some of the most popular open source frameworks for deep learning, with development and management tools in a single installable package. Designed to simplify end-toend deep learning, PowerAI Enterprise allows enterprises to spend less time on data preparation, implementation and integration, and more time training neural networks for results. IBM PowerAI Enterprise version 1.1.2 includes the most popular deep learning frameworks in one installation: - BVLC Caffe - IBMCaffe - TensorFlow - PyTorch - Keras (tensorflow-keras)

Analysts and industry experts agree: Digital disruption in manufacturing is on the horizon. Technologies like the Internet of Things, dynamic enterprise management, global supply chain visibility, and machine learning are already changing the way manufacturers produce goods and interact with customers. Further changes will continue to intensify issues and reveal opportunities.

Analysts and industry experts agree: Digital disruption in manufacturing is on the horizon. Technologies like the Internet of Things, dynamic enterprise management, global supply chain visibility, and machine learning are already changing the way manufacturers produce goods and interact with customers. Further changes will continue to intensify issues and reveal opportunities.

Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data.

Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe