Blogs

Data Science for All: What is it? Why care? How do I get it?

Data Science for All: What is it? Why care? How do I get it?

November 17, 2017 | by William Roberts, Technical Product Marketer, IBM
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.
IBM launches new Integrated Analytics System with Machine Learning

IBM launches new Integrated Analytics System with Machine Learning

November 16, 2017 | by Charles King, Principal Analyst and President, Pund-IT, Inc.
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.
5 tips for machine learning success outside of Silicon Valley

5 tips for machine learning success outside of Silicon Valley

November 15, 2017 | by Jean-François Puget, Chief Architect – Analytics Solutions, IBM
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...
Learning Machine Learning? Six articles you don’t want to miss

Learning Machine Learning? Six articles you don’t want to miss

September 28, 2017 | by Dinesh Nirmal, Vice President, Analytics Development, IBM
Digital disruption has revolutionized the way we live and do business — and machine learning is the latest wave of that revolution.

Transforming governance in the insights era

September 28, 2017 | by Ron Reuben, Offering Manager, IBM Analytics
There’s a revolution taking place within information governance. This change is driven by the growing needs of business users, and the recognition that trusted, high-quality, easy-to-find data can be the differentiator that drives better business outcomes.  
How to succeed in the multi-cloud era

How to succeed in the multi-cloud era

September 26, 2017 | by Benjamin Tao, VP, Worldwide Portfolio Marketing, IBM Analytics, IBM
In a time when data is perhaps a business’s most valuable resource, the ability to access, protect and analyze information plays a critical role in an organization’s overall multi-cloud strategy. Here's how to succeed.
When faster data science moves the world

When faster data science moves the world

September 26, 2017 | by Noah Kuttler, Marketing, IBM Data Warehouse Offerings, IBM
Learn how the IBM Integrated Analytics System, a unified data platform built on the IBM Common SQL Engine, helps do data science faster with high performance, embedded machine learning capabilities and built-in tools for data scientists to deliver analytics critical to increasing your organization’...
Accelerating time-to-market with fabricated test data

Accelerating time-to-market with fabricated test data

September 25, 2017 | by Kenneth Duemig, Worldwide Sale Lead Analytics/Optim, IBM
Protecting personal and sensitive data is vital. But, understanding the regulatory environment and available tools is just the first step. There are still challenges when building and managing test data environments. Here's how to overcome them.
Begin your cognitive enterprise journey at DataWorks Summit Sydney

Begin your cognitive enterprise journey at DataWorks Summit Sydney

August 31, 2017 | by Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM
The power of machine learning, data science and big data is no longer considered a hype or fad. It’s a reality and it’s impacting the bottom lines of businesses.
Experts answer your top data science and machine learning questions

Experts answer your top data science and machine learning questions

August 31, 2017 | by Melissa Love, Influencer Marketing Manager, IBM
There’s no doubt data science and machine learning are main areas of focus for enterprises to better their business. However, talking about data science and machine learning isn’t the same as making it a reality.

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