Best of TDWI's Data Digest
Online articles that caught our eye focus on big data, geospatial data, and predictive analytics.
Efficiently Transporting 8.6 Million People with Big Data
(Source: Forbes)
The bustling metropolis of London is far too large and crowded for most Londoners to use personal transport, which means a large reliance on public transport. Thanks to the electronic Oyster card system, Transport for London can track where riders get on and off the underground or on the bus and use the data generated to better improve their system.
Small Data is the Present, Big Data is the Future
(Source: Datanami)
Enterprises admit that they only analyze 12 percent of the data they collect, leaving a huge amount of data uselessly lying around. The amount of data collected is staggering and this article explores the benefit to enterprises of focusing on collecting smaller amounts of data they can analyze in a shorter time.
Hesitancy, Not Incompetency, Roadblock to Big Data Implementation
(Source: ADT Magazine)
Hadoop has recently been in the news for its less-than-expected number of adopters. Now a new survey reports that more than half of respondents say they have no immediate plans to invest in big data. The AG Software survey polled big companies about Hadoop adoption; keeping data secure was the biggest fear when it came to big data, not that enterprises were too incompetent to implement big data, as some believe.
5 Use Cases for Geospatial Data
(Source: Datanami)
Geospatial data can be just as important as any other type of data when it comes to data analytics. It takes the right kind of analytics to squeeze out importance from geospatial data. This article provides five ways geospatial data is utilized efficiently today.
Interdisciplinary Teamwork the Key to Unlocking Big Data
(Source: Information Week)
Big data is being implemented everywhere, with most enterprises sharing data across departments to improve efficiency. However, that is all most enterprises do across departments. Big data requires interdisciplinary teamwork to get the most out of analytics, an enterprise practice that has not been widely adopted yet.
Identifying High-Risk Patients with Predictive Analytics
(Source: Health IT Analytics)
Predictive analytics run on clinical results revealed the patients at the highest risk of developing complications from hepatitis-C. The University of Michigan's predictive analytics algorithm was more successful than past industry efforts in part because it had more data to work with, likely thanks to efficient big data warehousing.
Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at pungry.com.