Blogs

2014 Highlights: Technologies and Solutions

IBM Data magazine—the forum for smarter business—showcases selected technologies-focused articles from 2014

In addition to its new editor in chief, fresh look and feel, and advances in reader engagement through its IBM Data magazine LinkedIn group, IBM Data magazine sports a reconfigured content categorization system. As a result, the publication’s informative content is in synch with the evolution of IT technologies and innovations that impact today’s enterprises. The Technologies category encompasses a vast array of data management topic areas, from analytics, architectures and platforms, big data, cloud and cognitive computing, databases, and lifecycle management, to Internet of Things and sensor data, mobile computing, social media, and more.

As part of its end-of-year holiday season series, IBM Data magazine presents the second installment of selected feature articles from 2014 that offer coverage of important topic areas specifically in the Technologies category. And be sure to check out the first installment in this series, “2014 Highlights: Strategies and Solutions.” It spotlights selected 2014 feature articles covering topics in the Strategies category.

The editors of IBM Data magazine wish to thank the readers. We look forward to publishing more high-quality data management content from contributing thought leaders, experts, and insiders in 2015. If you would like to be a contributor, visit the magazine's Become a Contributor page.

Happy Holidays!


Understanding the Data Value Chain

Understanding the Data Value Chain   By Edd Dumbill
November 2014
Adopt a different view of data as a raw material for the data lifecycle business resource
 

What’s the Big Deal About Query Optimization?

What’s the Big Deal About Query Optimization? Part 1 By Suresh Sane
October 2014
Part 1: SQL queries have grown quite complex, and the challenges for optimization need to be considered
Part 2: Take account of recent improvements and where query optimization may be headed
 

Look at Big Data in Different Ways to Find Business Value

Look at Big Data in Different Ways to Find Business Value By W. H. Inmon
September 2014
There are many ways to view big data, but which way reveals the business value possibilities?
 

Hadoop Meets the Mainframe

Hadoop Meets the Mainframe By Gord Sissons
August 2014
InfoSphere BigInsights brings the agility and flexibility of Hadoop to System z environments
 

Data Lakes, Analyst Observations, and Reality

Data Lakes, Analyst Observations, and Reality By Tom Deutsch
August 2014
Fit-for-purpose architectures can bring business outcomes down to earth
 

Measuring Confidence in Data

Measuring Confidence in Data By Paula Wiles Sigmon
July 2014
An IBM-commissioned survey provides insight into information governance implementation
 

The Joy of Continuous Models for Streaming Data

The Joy of Continuous Models for Streaming Data By David Birmingham
June 2014
Set up scalable, on-demand data processing in PureData for Analytics powered by Netezza
 

The Emergence of the Analytics Architect

The Emergence of the Analytics Architect By Ahmed Fattah
June 2014
As analysis of big data matures, analytics architects bring a key set of skills for achieving business objectives
 

Keeping the Trains Running On Time

Keeping the Trains Running On Time By Sourav Mazumder, Matthew Riemer, and Boris Vishnevsky
June 2014
IBM data scientists exploit open and social media data to investigate approaches for smarter city operations
 

Developing Error-Free Native Stored Procedures

Developing Error-Free Native Stored Procedures By Tony Andrews
May 2014
Enterprise IT can apply SQL Procedural Language best practices to optimize DB2 for z/OS native stored procedures
 

When Is Big Data Worth Keeping and Governing?

When Is Big Data Worth Keeping and Governing? By Vincent McBurney
May 2014
Build quality into big data with an iterative, policy-based governance approach
 

Reaching Near–Real-Time Data Replication

Reaching Near–Real-Time Data Replication: Part 1 By Christian Lenke and Olaf Stephan
May 2014
Part 1: Replication swiftly synchronizes geographically dispersed systems, isolates workloads, and helps avoid downtime
Part 2: Apply a replication approach to typical data replication use cases for efficient migration
 

Hidden Biases that May Cloud Cognitive Computing

Hidden Biases that May Cloud Cognitive Computing By James Kobielus
April 2014
Consign scrutiny of unconscious biases that data scientists bring to their analytics and algorithms
 

A New Age for Database as a Service

A New Age for Database as a Service By Mike Miller
April 2014
The Cloudant NoSQL database as a service emerges in an era of tremendous cloud-based opportunity
 

Exploring Streaming Data in Real Time

Exploring Streaming Data in Real Time By Kimberly Madia and Dan Potter
April 2014
Turn streaming data into instant actionable insight by applying visual data discovery to data in motion
 

What Makes In-Memory Computing So Next Generation?

What Makes In-Memory Computing So Next Generation? By Nancy Hensley
March 2014
IBM DB2 with BLU Acceleration intelligently and rapidly processes data at the speed of thought
 

Exploring the NoSQL Family Tree

Exploring the NoSQL Family Tree By Sam Bisbee
March 2014
Gain an understanding of today’s NoSQL database evolution
 

10 Mistakes Enterprises Make in Big Data Projects

10 Mistakes Enterprises Make in Big Data Projects By Krish Krishnan
March 2014
Avoid common pitfalls when planning, creating, and implementing big data initiatives
 

Data Warehouse Architectures for Multinational Organizations

Data Warehouse Architectures for Multinational Organizations By Thomas Eunice
February 2014
Part 1: Consolidating data across regions or international borders requires careful assessment of data warehouse models
Part 2: Discover three approaches to data warehousing that address reporting challenges for worldwide enterprises
Part 3: Look to organizational culture and strategy before technology when deciding on a data warehouse model
 

When Dependent and Independent Variables Are Not Enough

When Dependent and Independent Variables Are Not Enough By David Mould
January 2014
Why data scientists need other data components to build, measure, and monitor predictive models