IBM Big Data Hub

NOVEMBER 15, 2017

Machine learning concerns in Silicon Valley tend to be different from those elsewhere in the U.S. — and outside of the U.S. So, here are five tips for those hearing about machine learning efforts in Silicon Valley, but who work elsewhere. These suggestions consider where machine learning and data science are headed on a large scale — as opposed to the fascinating (but often narrow) research happening in Silicon Valley.

Machine Learning 95

Machine Learning Data Science 95

Bruno Aziza

NOVEMBER 15, 2017

VEDA — Verizon Enterprise Data Analytics—is an enterprise organization that addresses data management, data governance, data warehousing and data lakes and common analytical and AI technologies.

Big Data 71

Big Data Data Lake Data Governance Enterprise 71

Smarten

NOVEMBER 14, 2017

Leave it to the Software! Predictive Analytics for the Faint of Heart! Assisted Predictive Modeling , Predictive Analytics. Those terms strike terror in the hearts of most business users, and that is understandable. These mysterious-sounding terms sound complicated and lead business users to imagine confounding, confusing algorithms, and endless strings of numbers.

Predictive Analytics 64

Predictive Analytics Analytics Predictive Modeling Forecasting 64

Perceptual Edge

NOVEMBER 13, 2017

One of the most popular shows in the early years of television was hosted by Art Linkletter, which included a segment called “Kids say the darndest things.” Linkletter would have conversations with young children who could be counted on to say things that adults found entertaining. I’ve experienced my own version of this in recent years that could be described as “Researchers say the darndest things.

Recreation/Entertainment 55

Recreation/Entertainment Visualization Statistics Data Processing 55

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

IBM Big Data Hub

NOVEMBER 17, 2017

Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.

Data Science 90

Data Science IT 90

Bruno Aziza

NOVEMBER 16, 2017

Data rules the world, but who rules the data? The companies that collect it? The servers that store it? The cables and satellites that transmit it? Or the laws that keep it flowing into the right hands—and away from the wrong ones? Welcome to the world of data governance.

Data Governance 56

Data Governance IT 56

Nutanix

NOVEMBER 14, 2017

State and local government organizations in the U.S. face daunting IT challenges as they look to improve service delivery while controlling costs. Key obstacles they face are a reliance on older IT systems, lack of skilled employees and inadequate budgets.

IT 20

IT 20

IBM Big Data Hub

NOVEMBER 17, 2017

Long before humans go to the moon or Mars, we'll need to send unmanned missions to establish the early infrastructure. Don Haderle, a scholar of the mainframe, recently suggested that mainframe computer systems will become the obvious solution for the moon, Mars, and beyond. Learn why.

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82

Bruno Aziza

NOVEMBER 15, 2017

Three days of thought-provoking discussion and stimulating ideas about the impacts of Big Data, AI, Robotics and Innovation.

Big Data 57

Big Data 57

Cloudera

NOVEMBER 16, 2017

Cloudera Data Science Workbench (CDSW) is a self-service collaboration platform for data scientists. It offers: Secure access to Cloudera data. On-demand compute. Support for Python, R, and Scala. Workflow automation, version control, and sharing. GPU acceleration for deep learning on demand. Now, with Release 1.2, CDSW is easier than ever to deploy and manage.

Data Science 40

Data Science Deep Learning Big Data Software 40

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

IBM Big Data Hub

NOVEMBER 13, 2017

In most modern-day organizations, external macros tend to be very influential — leaving little bandwidth for optimizing data governance. And, as stricter data storage and security compliance regulations come into play, it's becoming more and more critical for organizations to ensure they have the right data and that it's properly stored and managed, both for compliance and insights.

Data Governance 57

Data Governance Interactive Optimization Management 57

IBM Big Data Hub

NOVEMBER 16, 2017

Information analytics has never been a “one size fits all” proposition. That applies to the hardware and software technologies organizations employ, the information being parsed and the goals of specific projects.

Machine Learning 50

Machine Learning Analytics Software Technology 50

Cloudera

NOVEMBER 13, 2017

As the market moves toward cloud-based big data and analytics, three qualities emerge as vital for success. While many services will get some traction without meeting all three goals, they will also disappoint users and cause perpetual headaches for IT. At Cloudera, we see these undisputable attributes to be: Easy – Certainly no one goes out looking for a harder way to do their job.

Analytics 40

Analytics Big Data Machine Learning Cost-Benefit 40