Blogs

Hack the weather with deep data science

Hack the weather with deep data science

September 3, 2015 | by James Kobielus, Big Data Evangelist, IBM
Although weather is one of the most significant influences on business performance in a wide array of industries, it is also one of the least predictable. Join us at “Hack the Weather” to learn how smart data applications could usher in the next stage of meteorological forecasting.
Data visualization playbook: Determining the right level of detail

Data visualization playbook: Determining the right level of detail

August 31, 2015 | by Jennifer Shin
The most efficient way for data scientists to convey vital information is through visualizations—but if they aren’t easily understood, critical insights may become lost. Here’s a quick and handy guide to creating data visualizations that are appropriately detailed to ensure maximum effectiveness.
Using the right technology for classification and text analytics

Using the right technology for classification and text analytics

August 27, 2015 | by Jacques Roy, WW Technical Sales - Big Data: InfoSphere Streams, Informix TimeSeries, IBM
A document classification model can join together with text analytics to categorize documents dynamically, determining their value and sending them for further processing. Learn how a quick, efficient solution can create business advantage.
Why we need a methodology for data science

Why we need a methodology for data science

August 24, 2015 | by John Rollins, Chief Data Miner, Analytic Solutions Team, Netezza, an IBM Company
No less than traditional scientists, data scientists need a guiding strategy for solving problems. Such a methodology should directly address the problem at hand and should provide a framework for obtaining answers and results. Learn more about the Foundational Methodology for Data Science and how...
Analyzing the evolution of streaming analytics architectures

Analyzing the evolution of streaming analytics architectures

Part 2 of 2

August 11, 2015 | by Kimberly Madia, Worldwide Product Marketing Manager, InfoSphere Streams, IBM
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of...
Embracing real-time, streaming analytics in the insight economy

Embracing real-time, streaming analytics in the insight economy

Part 1 of 2

August 7, 2015 | by Kimberly Madia, Worldwide Product Marketing Manager, InfoSphere Streams, IBM
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data...
Top 5 trends that are making life easier for data professionals

Top 5 trends that are making life easier for data professionals

August 7, 2015 | by Kimberly Madia, Worldwide Product Marketing Manager, InfoSphere Streams, IBM
An ever-changing business environment is forcing data professionals to rethink their work methods—but fortunately, help is available. Here are five trends that are making life easier for data professionals: the emergence of Apache Spark, opportunities for greater skill reuse, growth in online and...
Creating buyer personas with advanced predictive analytics

Creating buyer personas with advanced predictive analytics

July 29, 2015 | by Kaitlin Noe, Analytics Solutions Social Specialist, IBM
No one wants to be classified as a “type”. Yet in order to meet customer expectations of 1-1 marketing, companies need to do exactly that—to use advanced analytics to create buyer personas. See how these personas will allow your company to optimize every marketing touch for improved ROI.
Driving in-memory data warehousing into the big data cloud

Driving in-memory data warehousing into the big data cloud

July 27, 2015 | by James Kobielus, Big Data Evangelist, IBM
The modern data warehouse (DW) lives in the cloud and is rapidly evolving into an in-memory platform for high-performance in-database analytics. As evidenced by IBM’s launch last year of dashDB and the latest enhancement release to the service, the fully managed in-memory cloud DW is already a...

How to deliver actionable insights from data streams

July 22, 2015 | by Avi Patwardhan, Product Marketing Manager
Stream computing makes dependably analyzing continuous data streams from sensors, social media or mobile device data efficient and effective. Even with multiple uses for big data in every industry, the end goal for organizations is to take advantage of stream computing to capture previously...

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