Blogs

The 7 drivers of effective decision optimization

The 7 drivers of effective decision optimization

July 29, 2016 | by Hans Schlenker, Decision Optimization Offering Manager, IBM
To drive coordinated planning across diverse business functions, and deliver huge value to planners and decision-makers, the most efficient approach is to use common decision optimization tools that address business and process specifics.
Insight Ops: The road to a collaborative self-service model

Insight Ops: The road to a collaborative self-service model

July 28, 2016 | by Tim Vincent, IBM Fellow and VP, CTO for Information & Analytics Group, IBM
Now introducing the “Insight Ops” model. This new model will embrace and enable an agile environment for discovery and exploration and manage the transition necessary to deploy the insight to make it actionable.
Unstructured and structured data versus repetitive and non-repetitive data

Unstructured and structured data versus repetitive and non-repetitive data

July 25, 2016 | by W.H. Inmon, Owner, Forest Rim Technology
Maybe classifying data as structured or unstructured isn’t so simple. What is structured to some may not be structured to others and vice versa. When it comes to the business value of data, consider another way to look at data—whether it is repetitive data or non-repetitive data.
Why data science should be your top priority

Why data science should be your top priority

July 11, 2016 | by Brooke Lawrie, Content Marketing Manager, Data Science, IBM Analytics, IBM
What is the key to staying ahead of the competition? Quite simply, data science. See why innovative companies have embraced the power behind data and analytics to move themselves way out in front of competitors.
Time is money, but for decision debt collaboration costs less

Time is money, but for decision debt collaboration costs less

Where chief data officers find their own technical debt

June 30, 2016 | by Matthew Mikell, Market Manager, IBM
Don’t let your business come to a standstill as a result of technical debt. Discover how a decision debt approach to tools and analytics help overcome the quick-fix solutions that contribute to technical debt and its impact on business.
Datapalooza 101: Why you need to make time for Datapalooza

Datapalooza 101: Why you need to make time for Datapalooza

June 29, 2016 | by Joseph Skarda, Intern, Global Client References and Events, IBM
Even if you aren’t a data scientist, Datapalooza is definitely the way to go. See why you can expect to get a lot out of attending a Datapalooza event near you.
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...
The power of machine learning in Spark

The power of machine learning in Spark

June 13, 2016 | by Max Seiden, Lead Spark Engineer, Platfora
Spark’s built-in machine-learning library (MLlib) provides a key differentiator from predecessor open source technologies and leverages Spark’s distributed, in-memory execution model. Take a look at some practical applications for specific Spark machine-learning algorithms in three advanced...
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.
InsightOut: The role of Apache Atlas in the open metadata ecosystem

InsightOut: The role of Apache Atlas in the open metadata ecosystem

Frameworks for open metadata and governance

June 10, 2016 | by Mandy Chessell, Distinguished Engineer, IBM Analytics Group CTO Office, IBM
What makes Apache Atlas different from other metadata solutions is that it is designed to ship with the platform where the data is stored. It is, in fact, a core component of the data platform.

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