Though already a trend we can currently observe, the pharmaceutical sector will see rewards in decisions being made more accurately and more effectively thanks to improvements in scalable cloud based information processing, applications and data storage. This new compute model is providing novel analytical capabilities, which utilize and harvest information presented as structured as well as unstructured data. Industry refers to such previously generally unavailable data as “Dark Data.”
This video describes the problems with the current publishing model for Scientific papers and suggests a more open model. The concept of Open Access applies to many aspects of the way we treat and manage data and information. Security and Compliance are two areas where Open Access is compromised on the face of it. But if we categorize data as Regulatory, Private and Public by Column then there are more opportunities to exploit the value of data and derive more knowledge and in turn value from it.
GroupM, the world’s largest ad buyer by billings, is centralizing its IT to create the cost savings needed to rollout Big Data analytics across its 120 offices around the world… The company’s current approach has made tracking IT expenses difficult. The centralized approach will allow executives to monitor the return on Big Data investments through Apptio, an IT management platform that tracks costs associated with technology allocation and charges business units for what they spend. “We want to make sure we’re making these large scale investments as efficiently as possible,” Mr. Donnarumma said.
See on blogs.wsj.com
The current pace of technology change and the learnings available from the first wave of data warehousing mean that the Big Data cycle will be rapid. Big Data holds big promise, but the sins of the past – siloed implementations, data fragmentation and duplication, rigid data modeling, and poor information governance – will derail initiatives unless banks think innovatively and plan carefully.
See on ovum.com
Stephen Few on Big data http://t.co/J3NJfSzL. As Lance Armstrong says “it is not about the bike”.
Will new sources of data and our ability to store and interact with greater volumes of data lead to new insights?
Perhaps. I certainly hope so. The point that I’m making, though, is that few if any of these new insights will emerge from anything that BI vendors are marketing as Big Data technologies. They will emerge from people using their brains to think smarter about data. They will emerge from effective interaction with data, rooted in statistical skill and supported by tools—especially visual analysis tools—that are well designed to augment human perception and cognition. In other words, they will emerge when people find and learn how to use the loom that’s needed to weave data into meaningful insights.