Blogs

What to do with all that machine learning data

What to do with all that machine learning data

April 18, 2017 | by Susara van den Heever, Product Manager – IBM Decision Optimization, IBM
Many businesses are starting to notice that without converting the masses of new data generated by the Machine Learning (ML) wave to Smarter Decisions, the impact falls short of expectations.
Building a cognitive data lake with ODPi-compliant Hadoop

Building a cognitive data lake with ODPi-compliant Hadoop

April 7, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
For today’s data scientists and data engineers, the data lake is a concept that is both intriguing and often misunderstood. While there are many good resources about data lakes on ibm.com and other websites, there is also a lot of hype and spin. As a result, it can be difficult to get a clear...
Analytics and the cloud: NoSQL databases

Analytics and the cloud: NoSQL databases

Schemaless databases and the role they play

April 6, 2017 | by Steven Lockwood, Executive Information Architect, IBM
Although NoSQL database technology has been around for a long time (before SQL actually), not until the advent of Web 2.0, when companies such as Google and Amazon began using the technology, did NoSQL’s popularity really take off. Market Research Media forecasts NoSQL Market to be $3.4 Billion by...
Cognitive technology for competitive advantage in credit risk management

Cognitive technology for competitive advantage in credit risk management

April 4, 2017 | by Peter Collins, Financial Services Specialist, IBM
A blog promoting a recent IBM Risk white paper on the advantages of cognitive technologies for more robust, and comprehensive credit risk management across the credit lifecycle.
What the Academy Awards mix-up teaches us about data integration

What the Academy Awards mix-up teaches us about data integration

March 30, 2017 | by Douglas Thompson, Executive Architect, IBM
The Academy Awards provided a great example of the challenges of data integration. The business output of the data integration processes in the award ceremony is the announcement of a winner in a specific category.
Recapping the IBM Chief Data Officer Strategy Summit Spring 2017

Recapping the IBM Chief Data Officer Strategy Summit Spring 2017

March 28, 2017 | by Oliver Clark, Social Media Execution Strategist, IBM
Take a look at highlights from the IBM Chief Data Officer Strategy Summit Spring 2017 in San Francisco, California in a collection that includes a full social recap, videos, quotes and more.
Incorporating machine learning in the data lake for robust business results

Incorporating machine learning in the data lake for robust business results

March 28, 2017 | by Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM
Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?
The data governance story: Building a business language glossary

The data governance story: Building a business language glossary

March 28, 2017 | by Marc Haber, Offering Manager, IBM InfoSphere Information Governance Catalog, IBM
Data is often the catalyst that drives business direction and growth. However, if data is cryptic and not understood, then how can such data contribute to such direction or growth? Just like in life, we learn from our past, as we gain direction and insight from previous events or activities to make...
 How does machine learning work?

How does machine learning work?

March 20, 2017 | by Bob Yelland, UKI Big Data Marketing Manager, IBM
Machine learning—a branch of artificial intelligence—is changing not only how we interact with machines, but how we relate to the world around us.
Everybody is Sherlock Holmes in the era of Watson-powered team data science

Everybody is Sherlock Holmes in the era of Watson-powered team data science

March 20, 2017 | by Benjamin Tao, VP, Worldwide Portfolio Marketing, IBM Analytics, IBM
Data science is a team sport that involves specialists with complementary skills and aptitudes. Successful data science initiatives leverage high-performance team collaboration. Like the fictional sleuth and his partner, IBM’s customers in the data science community must have the right mix of...