Blogs

Well-versed in big data and analytics? Consider being an IBM Press author

Well-versed in big data and analytics?

Consider being an IBM Press author

June 28, 2016 | by Natalie J. Troia, Marketing Program Manager, IBM
Are you a big data and analytics subject-matter expert? Do you enjoy writing? Would you like to be published? Check out IBM Press and the great opportunity to be a big data and analytics author. Share your expertise with readers from customer and partner organizations, colleagues and the greater...
How can data scientists collaborate to build business applications?

How can data scientists collaborate to build better business applications?

June 10, 2016 | by James Kobielus, Big Data Evangelist, IBM
We asked five social influencers how data scientists can collaborate to build better business applications. See what they had to say.
End-to-end analytics in the cloud

End-to-end analytics in the cloud

June 9, 2016 | by John J. Thomas, Distinguished Engineer and Director, IBM Competitive Project Office, IBM
Deriving actionable insight from data and analytics is shifting to unified, cloud-based platforms that can be used by a variety of analysis personas. Take a look at a national retail chain scenario demonstrating how a comprehensive portfolio of end-to-end analytics in the cloud can provide the...
Highlights from the Apache Spark Maker Community Event

Highlights from the Apache Spark Maker Community Event

June 8, 2016 | by James Kobielus, Big Data Evangelist, IBM
Stay on the cutting edge with these highlights from the Apache Spark Maker Community Event as you sit in on interviews with industry leaders and explore the power of the IBM Data Science Experience.
Experiencing deeper productivity in open data science

Experiencing deeper productivity in open data science

June 7, 2016 | by James Kobielus, Big Data Evangelist, IBM
Reimagine the data science experience as an open experience with this IDE, which aims to facilitate a full range of development tasks, from data acquisition and data mining to prototyping and programming. When you do, discover how you can use Apache Spark and R to pursue open analytics by building...
Innovative business applications: The disruptive potential of open data science

Innovative business applications: The disruptive potential of open data science

June 1, 2016 | by James Kobielus, Big Data Evangelist, IBM
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment...
Boosting the productivity of the next-generation data scientist

Boosting the productivity of the next-generation data scientist

May 24, 2016 | by James Kobielus, Big Data Evangelist, IBM
Use open-source tools to supercharge the data science lifecycle, giving data science teams a boost as they work to provide compelling results in the complex team environments that mark modern corporations. Learn how you can make open data science an ongoing part of your business environment when...
What is text analytics?

What is text analytics?

Making the complex simple

May 19, 2016 | by Mike Ferguson, Managing Director of Intelligent Business Strategies Limited, Intelligent Business Strategies Limited
Whether organizations want to extract customer data beyond names and addresses from unstructured data sources; pull specific dates, times or monetary amounts; predict trends from sentiment data; or engage in many other uses, text analytics is the way to go. Learn the details of text analytics, and...
It takes a team: Collaboration and workflow in open data science

It takes a team: Collaboration and workflow in open data science

May 19, 2016 | by James Kobielus, Big Data Evangelist, IBM
Data science takes collaborate teams of data scientists engaging in productive, open data development initiatives that can ensure strong workflow, governance, security and management. See why open environments are revolutionizing the data science landscape.
Spark and R: The deepening open analytics stack

Spark and R: The deepening open analytics stack

May 19, 2016 | by James Kobielus, Big Data Evangelist, IBM
As Spark continues to mature into mainstream adoption in the data science community, the open data analytics stack and open source tools grow more robust, giving data scientists rich core workbenches to develop evermore innovative applications.

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