Moving data often impacts system performance, so how do you move large volumes of data safely and securely? The importance of data movement is even more critical when you consider moving data from ground to Cloud. Joe Bostian, z Systems Data Science Architect, IBM Analytics, and Mythili
Hard to believe we've arrived at the last day of Think 2018. From keynotes to panels, informal collaborations and learning sessions, we've witnessed first-hand the excitement that conversations about data and analytics bring to business.
Think 2018 is in full swing. We’re inspired hearing from leaders across industries using analytics to transform their business. And we’re thrilled to take part in conversations about data science, machine learning, AI and much more. Here are some highlights from Wednesday at Think.
The excitement, insights and innovation at Think 2018 is truly astounding. Today we heard from IBM Chairman, President and CEO, Ginni Rometty, plus industry leaders and clients who are transforming whole business sectors.
Human beings tend to filter out events they deem unimportant. They can only process so much at any given time. Computer systems, however, must be able to handle a massive number of events in real time or near-real time to help support a wide range of applications.
http://www.ibm.com/us-en/marketplace/integrated-analytics-systemHow is data science changing with the availability of high-performance data platforms? Vikram Murali, director of IBM Integrated Analytics Systems and Db2 Event Store development, Jay Wentworth, STSM of appliance architecture, and
In this special podcast, our Making Data Simple Podcast host Al Martin is joined by Caleb Curry, data guru for IBM Hybrid Cloud, and Sam Lightstone, IBM fellow of analytics, to talk about their session at the IBM Think 2018 Conference: "Next-Generation Data Management: Power and Simplicity to the
For decades, a company’s database usually had a single job: operating as either an operational — also known as transactional — database or acting as a data warehouse. It was also typically deployed in a single location: on premises. Today, companies not only want more from their databases, but also
In this week's episode of Making Data Simple, Al Martin and Adam Storm, IBM senior technical staff member and master inventor, next-generation HTAP architect, sit down to talk about fast data. Adam also covers the pros and cons of different information architectures and the software you can use to
While the proliferation of data will be readily apparent, deciding what to do in response will be less straightforward. The majority of workloads currently sit in traditional, on-premises environments but we’ll see many of them move to private and public clouds over the next over the next five
How does artificial intelligence (AI) come into play on a day-to-day basis? In this episode of Making Data Simple, Jean-François Puget, distinguished engineer, machine learning and optimization, and Steve Moore, senior story strategist for Inside Machine Learning on Medium, join host Al Martin to
Your business and your data are both unique. For that reason, your enterprise architecture must also be tailored to fit the exact needs of your business. When data is involved, you want choices, not trade offs. And, more importantly, you want your solutions to build upon and complement one another.
Join us 27 February at 1 PM ET for "Machine Learning Everywhere: Build Your Ladder to AI." Visit the event landing page to learn more about the event and register for a calendar reminder: ibm.com/mleverywhere
In this episode of the Making Data Simple Podcast, Seth Dobrin, vice president and chief data officer for IBM Analytics, and Al Martin continue their conversation about data in 2018. Find out the six steps to make your enterprise data driven, how machine learning and AI will impact your business