There is so much talk about data as a new natural resource. The amount of data organizations and citizens across the globe produce, is authored in many systems and consumed by various organizations and users in different formats. This begs the following questions: Who owns this data? And why it is
It is said that more data has been created in the past two years than in the entire preceding history of mankind. It would be interesting to find out how much of this data has been analyzed and put to good use. Analyzing and harnessing big data is undoubtedly the major challenge of the day for all
The unprecedented evolution of social media data’s influence on business can have tremendous impact on how customers are integrated into organizational goals and practices. See why more organizations than ever are using social media data to take a customer-centric approach to evolving their
In a recent CrowdChat discussion, a group of Hadoop and Spark subject matter experts from the IBM Analytics group discussed using cloud-based Hadoop and Spark services as a lever for business agility. From their contributions we drew ten hot topics and themes for experts in all areas of the big
Some organizations misunderstand the optimized way to use Hadoop and Spark together, primarily because of their complexity. But investing in both technologies enables a broad set of big data analytics and application development use cases. See what Niru Anisetti and Rohan Vaidyanathan have to say
IBM has identified a number of common problems that many businesses find themselves facing in their various stages of Apache Hadoop and Apache Spark adoption. As a result, IBM has developed a set of support services to help customers accelerate time-to-value outcomes and reduce risk when building
At the core of many big data architectures is Apache Hadoop and Apache Spark. Organizations adopting these technologies for their big data journey are nevertheless at different levels of maturity. Hear what Prasad Pandit had to say in an interview with Andrea Braida about how IBM is evolving its
IBM Insight at World of Watson 2016 had a lot of worldwide focus on cognitive capabilities and their application in analytics, commerce and security. And yet, the General Data Protection Regulation (GDPR) adopted in 2016 and applicable in 2018, seemed to garner quite a bit of interest among
Learn how IBM SPSS Statistics can enhance the value that statistical analysis adds to a business, and find out how you can tap into the power of high-performance statistical modeling in your own organization.
Many forward-thinking organizations want to investigate how big data analytics helps them outthink and outperform the competition. However, many also are challenged with finding the right talent to run the operations, keep the data secure and figure out how to leverage the myriad tools at their
Censuses have long provided governing authorities with data that helps them make decisions affecting the lives and livelihoods of people everywhere. In modern times, however, companies look for more than just a name when doing business—and that’s where master data comes in. Find out how your
Essentially, Monte Carlo simulations predict an outcome not from the actual values of input data (which aren’t known) but from the likely (aka “simulated”) values of that data (based on their probability distributions). These simulations can prove invaluable for assessing risks in many real-world
Although spreadsheets offer a stable, attractive option when working with numbers, they can fall far short when they are applied to enterprise-scale statistical analytics. Weigh the limitations of spreadsheets against the benefits of a sophisticated, enterprise-grade statistical analysis tool for
The Internet of Things continues to be a land of opportunity in so many areas. Take a look at this overview of steps to innovation and success factors along with the risks and pitfalls to avoid in your Internet of Things journey.
Open source tools continue to foster non-stop innovation throughout the Insight Economy. So it’s no surprise that open-source languages—most notably, R--have moved to the center of enterprise statistical analytics and data management.