The Heat Now Pings from Text Radar: November 9 to November 15
November 20, 2012
This week Text Radar published some interesting articles regarding the state of big data and text analytics as a burgeoning field.
“Return on Investment Measured More Accurately Using Big Data Analysis” explains the value of big data as a tool to measure marketing ROI.
When discussing how analyzing big data is a good example of how analyzing big data is helping to determine business models, the article states:
“Cloud technologies, and the advancements in data analysis give foundation to accelerating the trend. Advanced technologies like active analytics (“decisioning”), advanced algorithms, etc. are proving to be extremely effective at fueling the Big Data engine. In the new world we live in, data isn’t something to be stored and ignored, but analyzed and utilized for its valuable insights.”
Having a big data plan is integral to the success of any modern day company. “IBM Outlines Four Phases of Big Data Adoption and Recommends Ways to Create Value” outlines a 2012 data analytics study put out by IBM.
The article states:
“IBM outlined four phases of big data adoption, which include educate, explore, engage and execute. These stages are defined as follows:
* Educate. This phase focuses on knowledge gathering and market observations.
* Explore. After completing the education phase, companies will develop a strategy and roadmap based on business needs and challenges.
* Engage. During the third phase, a business will pilot big data initiatives to validate value and requirements.
* Execute. Companies in the fourth phase have deployed two or more big data initiatives and are continuing to apply advanced analytics.”
Similarly, “5 Important Tips for Staying Ahead of Growing Unstructured Big Data,” shares tips for setting storage policies, classifying data, evaluating infrastructure, and data analysis.
When explaining how to appropriately use metadata, the article states:
“Making effective use of unstructured data requires an approach to organizing and cataloging content. In order to use the content, it’s helpful to know what that content is. Some systems automatically capture process-related metadata, or attributes such as creation date, author, title, etc. However, applying metadata to actual content such as content summaries, companies or people mentioned, or topic keywords can be considerably more useful.”
For those who want a data management solution that makes smarter content out of unstructured data, consider Smartlogic’s Semaphore Content Intelligence Platform.
Jasmine Ashton, November 20, 2012