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
Businesses that engage in digital transformation can transform their activities, processes and models to fully leverage opportunities provided by advanced digital technologies. See how a telecommunications provider built a real-time big data analytics platform that harnesses the power of cognitive
Caregivers.com is changing the way senior care providers operate through powerful insights generated through the company’s mobile app. Justin Saul, Senior Director of Technology, Caregivers.com, explains, "with IBM Cloudant and the IBM Watson Data Platform, we were able to quickly iterate on
Without question, our lives are very different from only a couple decades ago, thanks in part to some pretty amazing technology advances including smartphones and other devices, mobile apps, an ever-growing array of social channels and more. Take a look at how one telecommunications organization
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
One thing that a recent event in Beijing, China confirmed is there’s no shortage of interest in machine learning for developers in that region. Take a look at snapshots of event highlights featuring rich content on artificial intelligence, cognitive capabilities, machine learning and more presented
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.
Historically, Master Data Management (MDM) projects have focused on creating a single view of the truth that can be consumed by business processes. Learn more about how the evolving need to utilize MDM serves as catalyst for a new solution extension offering a managed data preparation and data
When considering how to adapt to technology, the insurance industry can look to benefits reaped by the financial industry. In this episode of Finance in Focus, hear insurance experts Kathy Hutson and Sebastien Meunier discuss the opportunities cognitive computing can bring to the insurance industry.
Does successfully navigating the role of chief data officer seem like being lost in a hedge maze? If you’re confused about what to do next, these three questions can help you identify a clear path forward.
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
One of the chief data officer’s primary responsibilities is to support the company’s strategy for monetization. Learn how investing in essential technologies can help your team create a data strategy that aligns with your organization’s monetization strategy to empower the business as a whole.
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