Disruptive innovations such as big data, machine learning, cognitive computing and cloud-based services are presenting analytics professionals with rapid transformation that impacts business. As a result, organizations are adopting new best practices for data analysis processes. Dig into this first
In the highly competitive video game industry, keeping players engaged and loyal is crucial in capturing a major share of this lucrative market. Doing so involves using big data analytics to harness innovation and understand what players expect.
When looking for course-changing insight, connecting the dots of information can range from meticulous exercises to bursts of inspiration. However this wow-factor insight may be derived, it can take organizations and industries on a new trajectory characterizing the dawn of an era in which
As telecommunications companies offer a wider range of services, the amount of data they must process is increasing exponentially. This podcast discusses how telcos can use Apache Hadoop to keep up with rapid data growth.
Just as today’s businesses need to speed operations and cut costs, the same goals hold true for major sporting events such as the Australian Open Tennis Championships. To help make the Australian Open 2016 event the ultimate experience for online fans and the fans in attendance, see how the IBM
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond
Today, more than ever, businesses need to put the ability to analyze data into the hands of business analysts, data scientists, stakeholders and decision makers. Take an evolutionary look at how Apache Spark and big data discovery are just beginning to open up a diverse and powerful set of
Data engineers have much to learn from water management professionals, who have mastered the art of keeping filtered water on tap—ready at a moment’s notice. As information volumes begin to deluge data repositories and outpace traditional approaches, data professionals must use every tool at their
Last year I made a few predictions about what would come to pass in the big data landscape in 2015. How close was I? Let’s revisit some of my 2015 predictions to see if they came to fruition and whether there were any surprises.
Dealing with slow technology is a major concern for anyone who needs quick access to analytic insights. For this reason, it’s vital to have a data warehouse appliance with sufficient speed that allows all users to make the most of its analytic power.
Stream computing combines data streams with an increasingly broad range of applications designed to help businesses solve problems of all kinds. Learn more about how you can capture data streams and infuse them into your applications.
Organizations that have built a data-driven culture are seeing gains in efficiency and capability that are allowing them to become leaders in their industries. Learn how you can begin integrating data science into your organization, enabling new ways of activating data as part of an enterprise-wide
Is your organization stuck at the edge of Hadoop adoption, searching for a path to broad use that doesn’t hold back your most proficient users? Big data discovery technology aims to help you bridge the chasm between early adoption and majority use, bringing rank-and-file users into the fold without