Blogs

Think 2018: Our favorite highlights from Monday

Think 2018: Our favorite highlights from Monday

March 19, 2018 | by Elly Shin, Social Media Strategist, Unified Governance & Integration, IBM
Think 2018 is the biggest IBM conference of the year covering all things tech. And, to be sure you don't miss a moment, here are highlights from Monday, March 19, the first day of the event.
New hyper-fast data ingestion enables smarter decisions

New hyper-fast data ingestion enables smarter decisions

March 16, 2018 | by Steve Astorino, Vice President, Development, IBM Private Cloud Analytics Platform
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.
Dispelling myths about the IBM Integrated Analytics System

Dispelling myths about the IBM Integrated Analytics System

March 15, 2018 | by Hemant Suri, Senior Offering Manager, IBM
There are some misleading messages in the market about the IBM Hybrid Data Management and its data warehouse strategy. So here’s some clarification.
Want to succeed as a CDO? 6 articles to read this week

Want to succeed as a CDO? 6 articles to read this week

March 5, 2018 | by Jennifer Clemente, IBM
Learn more about the right way to approach your data governance governance strategy in 2018 by checking out our top performing articles.
3 principles for climbing the AI ladder with IBM Governed Data Lake

3 principles for climbing the AI ladder with IBM Governed Data Lake

February 12, 2018 | by Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM
Recently, we capped off the first leg of the “Enabling digital business with an IBM governed data lake” road shows in the Asia Pacific region with our customers and partners.
5 ways to turn data into insights and revenue with cognitive content analytics

5 ways to turn data into insights and revenue with cognitive content analytics

February 9, 2018 | by Trips Reddy, Senior Content Manager, IBM Watson
Many companies are expected to pursue data management, advanced analytics and cognitive computing to stay competitive and drive revenue. Except for a handful of leaders such as LinkedIn, Netflix, Nordstrom, Target and Verizon, most companies are still struggling to close the gap between data...
Are critical business insights hiding inside your unstructured data?

Are critical business insights hiding inside your unstructured data?

IBM Content Analytics can help

February 5, 2018 | by Akiko Murakami, Software Engineer, Architect and Lab Advocate of Watson Explorer, IBM Japan
The search function is a very powerful tool, assuming you have concrete keywords or concepts to find in your data. And that does not even take into account the size of the information you might be searching.
How content analytics helps manufacturers improve product safety and save lives

How content analytics helps manufacturers improve product safety and save lives

February 2, 2018
Manufacturing problems can have a serious impact on businesses. This is especially true when these problems manifest themselves as product safety issues causing injury or even death.
Gain cognitive insights and build scalable cognitive solutions with IBM Watson Explorer Community Edition

Gain cognitive insights and build scalable cognitive solutions with IBM Watson Explorer Community Edition

January 29, 2018 | by Amit Kumar, Product Marketing Lead, IBM Watson Explorer, IBM
Imagine if you could ask questions with the ease of natural language and discover insights from all the data in your organization without worrying about different data formats.
Top 10 IBM Big Data & Analytics Hub blog posts of 2017

Top 10 IBM Big Data & Analytics Hub blog posts of 2017

December 4, 2017 | by Erika Ulring, Editor-in-Chief of the Big Data and Analytics Hub, IBM
Readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read blog posts about incorporating machine learning, choosing the best possible data model, determining how to make the most of data science skills, working with open source frameworks and more....