This white paper discusses the top three challenges in big data environments and how IBM InfoSphere Big Match for Hadoop helps address them.

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How can organizations tap into the vast flow of data, weed out the information that matters, and then link that meaningful sentiment to specific customers? This ebook explores how an enhanced 360-degree view of the customer optimizes and facilitates more personalized customer interactions.

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To help enterprises create trusted insight as the volume, velocity and variety of data continue to explode, IBM combines the power of IBM InfoSphere Master Data Management with the IBM big data portfolio.

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A solid information integration and governance program should include automated discovery, profiling and understanding of diverse data sets. IBM InfoSphere is designed to do all of these things by evolving information integration and governance to meet the challenges presented by big data.

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Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data. To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.

Read the white paper and learn:

• Three important guidelines to follow when optimizing big data integration workloads
• Two primary components that comprise the Hadoop platform
• Five fundamental big data integration best practices

Help your organization minimize risks and costs and maximize ROI for your Hadoop projects.

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IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop.

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This white paper presents seven essential elements for high-performance information integration and how they apply to business and technical decision makers responsible for designing, building, supporting and using scalable data processing systems.

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Predictive analytics can help drive significant improvement to an organization's bottom line.This eBook from Ventana Research explores the many ways businesses can use predictive analytics to grow revenue, shrink costs and improve margins.

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Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science, but the barrier to entry has been high.

Data science platforms are engines for creating machine-learning solutions. This report evaluates 16 providers of data science platforms.

Read this white paper to learn:

• How Apache Spark is broadening access to machine learning
• Various machine learning use cases and scenarios
• Why Apache Spark is the ideal platform for machine learning

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Introducing Notebooks: A power tool for data scientists Fast, flexible and collaborative data exploration and analysis Data exploration and analysis is a repetitive, iterative process, but in order to meet business demands, data scientists do not always have the luxury of long development cycles. What if data scientists could answer bigger and tougher questions faster? What if they could more easily and rapidly experiment, test hypotheses and work more collaboratively on interactive analytics?

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IBM named a leader for data science platforms

Data science platforms are engines for creating machine-learning solutions. This report evaluates 16 providers of data science platforms.

Read the report to learn:

• Why IBM is recognized as a leader
• Why data science, analytics, and machine learning are the engines of the future

Complete the form to read the report today.

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The synergy between predictive analytics and decision optimization is critical to good decision making. Predictive analytics offers insights into likely future scenarios, and decision optimization prescribes best-action recommendations for how to respond to those scenarios given your business goals, business dynamics, and potential tradeoffs or consequences.

Together, predictive analytics and decision optimization provide organizations with the ability to turn insight into action—and action into positive outcomes.

In this white paper, you’ll gain a better understanding of:

• The difference between predictive and prescriptive analytics
• How predictive and prescriptive actions complement one another to help you achieve optimized business decisions
• IBM’s approach to creating a powerful end-to-end decision management system

Read the paper today to discover how to bridge the gap between insight and action.

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