December 18, 2014
Computational modeling has revolutionized all branches of the physical sciences, engineering and design. Leading-edge work in these fields is pushing new computational frontiers at nano scales. Computation-centric methods allow researchers to model, simulate and assess a much wider array of options far more rapidly than old-fashioned physical techniques. However, the incredible productivity of computational prototyping carries a downside: far more candidate molecules can be simulated than can reasonably be assessed by human researchers. The bottom line is that when you build bigger haystacks, you need more powerful tools for finding the golden needles that may be buried deep within.
December 18, 2014
This past summer we witnessed an incredible phenomenon seemingly capturing the world by storm: The ALS Ice Bucket Challenge. Facebook estimates 28 million people posted about ALS between June 1 and August 28, including comments and tags.
December 16, 2014
“As you dig deeper into unstructured data where feelings, emotions and other often messy information reside, you have to embrace qualitative metrics which don’t fit neatly into traditional quantitative analysis,” says David M. Lawson, co-founder & CEO of NewSci, LLC and this week’s Big Data & Analytics Hero.
December 9, 2014
To fulfill the promise of analytics, we must put a lot more effort into delivering these projects right, the first time. We must think through each of the traditional success criteria and ask ourselves the burning question: how is delivering analytics different? This starts from gaining executive support, creating a business case, putting a team together, conducting proof of concept, quantifying benefits realization and socializing results for enabling analytics driven organizational change.
December 9, 2014
Jake Porway, founder and executive director of DataKind and this week’s Big Data & Analytics Hero, shares that they’ve found that "cross-sector collaborations between data scientists, managers, designers, foundations, nonprofits and more are critical for really making lasting change.” When we all come together and lock elbows we can use data to really make a difference.
December 3, 2014
What are the opportunities of data refinement from the IT point of view? Does self-service data refinement devalue IT? Does it simply create more work for IT, cleaning up after business users who have run amuck?
December 2, 2014
In 2014, there were several important shifts that occurred in the world of big data that business executives around the globe cannot afford to ignore. In part one we introduced four transformative shifts affecting the fast-paced digital marketplace; now in part two we will take a closer look at the first two shifts.
November 25, 2014
“Roof stability and effective support is quite a challenge,” declares Ben Snyman, VP of Underground Mining and Smart Services at Joy Global and this week’s Big Data Hero. “If the roof has a problem, production halts. We cannot afford to wait around for a report telling us there is a problem.
November 24, 2014
Data refinement is one of the most important revelations in the big data market. The idea is simple: you want to take advantage of and use all sources of big data. But when each individual user needs only information relevant to them, what’s needed is a data refinery. It automatically cleans, matches, secures and profiles data—that’s what is meant by refinement.
November 20, 2014
Last month at Insight 2014, IBM made an exciting announcement: IBM DataWorks is available now. The reaction was overwhelmingly positive; clients who have or will soon have a data lake were very keen on the notion of data refinement. In fact, a data refinery is a natural fit with a data lake.