Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.

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.

The need to process and analyze large volumes of data being generated is causing many businesses to expand the capabilities of their BI systems. Read why a next-generation BI system that utilizes multiple data sources and also offers predictive analytics can provide the needed guidance.

Read about Nucleus Research’s findings that organizations earn an average of $13.01 for every dollar spent on analytics applications such as business intelligence (BI), performance management (PM) and predictive analytics.

Analytic discovery technology enables analysis of multiple data sets of various sizes and structures and is similar to data mining. Discover how analytic discovery goes beyond data mining by enabling non specialists to explore data and gain insights.

This report explores how midsize companies are harnessing the power of business analytics to exploit more data, equip more business users with decisions support, and ultimately deliver substantial performance improvements.

IBM has again risen to the top of Hypatia Research’s leader board in this Voice of the Customer landscape update. Hypatia cites IBM for its “unrivaled” portfolio of software components, consultants and professional services.

See why organizations are increasingly recognizing that to be competitive, they need to be data-driven, for which they need not only strong analytics and BI capabilities at all decision levels, but also an effective way to transform data into information and ensure its optimal delivery.

This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.

Cloud computing, consolidation/virtualization and integrated computing systems strain existing data center networks. IBM solutions help re-configure networks for greatly improved performance.

Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.

You need a database designed to control both the infrastructure and personnel costs that form the IT budget. The next generation of IBM DB2 helps organizations get more value from their big data to improve their IT economics. Major innovations provide out-of-the-box performance gains that go beyond the limitations of in-memory-only systems to support decision making at the speed of business.

In order to derive business value from Big Data, practitioners must have the means to quickly (as in sub-milliseconds) analyze data, derive actionable insights from that analysis, and execute the recommended actions. While Hadoop is ideal for storing and processing large volumes of data affordably, it is less suited to this type of real-time operational analytics, or Inline Analytics. For these types of workloads, a different style of computing is required. The answer is in-memory databases.