Blogs

Gartner predictions for Sales Performance Management (SPM) at Vision 2017

Sales Performance Management predictions featuring Gartner at Vision 2017

April 24, 2017 | by Kevin Gray, Senior Product Portfolio Manager, Sales Performance Management, IBM
Melissa Hilbert, a Director at Gartner Research, will be speaking in the SPM track at IBM Vision 2017 about some of the key questions and challenges lying ahead for business leaders regarding sales performance management and incentive compensation. ibm.com/vision
Big Replicate: A big insurance policy for your big data

Big Replicate: A big insurance policy for your big data

April 18, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
Dwaine Snow is a Global Big Data and Data Science Technical Sales Manager at IBM. He has worked for IBM for more than 20 years, focusing on relational databases, data warehousing, and the new world of big data analytics. He has written eight books and numerous articles on database management, and...
Development lifecycles for defining the meaning and structure of the data lake

Development lifecycles for defining the meaning and structure of the data lake

April 18, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
In the past, the relationship between the different models that might be used in defining a data warehouse was a very linear one. There may have been different model artifacts used as the team responsible for developing the data warehouse progressed through the usually waterfall-type set of...
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?

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