Blogs

James Kobielus
Big Data Evangelist, IBM
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As IBM's big data evangelist, James Kobielus is IBM Senior Program Director, Product Marketing, Big Data Analytics solutions. He is an industry veteran, a popular speaker and social media participant and a thought leader in big data, Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management and next best action technologies.

Machine learning molds the material world

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.  Read More

Big data and the power of positive curation

December 11, 2014
Curation addresses a purpose that stewardship never has in the data analytic governance context. Whereas stewardship refers to data’s trustworthiness, curation addresses the quality criterion of relevance. Consequently, curators might be regarded as being responsible for a “single version of what’s worthy of your consideration.” The repositories in which curated information is kept are not usually official systems of record, but, rather, systems of insight, in the broader sense of the latter. Read More

Cognitive computing: Programming the artificial mind

November 20, 2014
When you want to take artificial intelligence out of the realm of imagination and poetry, and bring it squarely into practical reality, you need computational tools. The tools need to help your cognitive application developers write the leanest models possible. Developers need frameworks, languages and libraries for building and tuning neural networks and other cognitive constructs for most efficient parallel processing of individual data inputs and the outputs of the nodes within a vast artificial neural network. You can acquire these tools from various sources, such as IBM Watson Developer Cloud. And there are as many approaches for building computational applications that learn from data and automate cognitive processes as there are for building traditional application logic. Read More

Chief data officer: My mixed and nuanced musings on the need for one

November 13, 2014
When people say that "data is the new oil," they're usually making a general statement on how deeply modern organizations depend on data to drive transactions, analytics and processes in general. It's in that context that many organizations decide to appoint something called a chief data officer (CDO) to oversee this precious resource. Personally, I'm not sure that the responsibilities of a CDO, as described in these sources and elsewhere, are all that different from the older concept of a chief information officer (CIO). Regardless of what we call this business-focused function (CDO, CIO or even chief analytics officer), it's important that there be a C-level executive who is responsible for overseeing governance of the organization's data resources, ensuring that they be applied effectively to achieve important business outcomes and transforming the organization into a more data-savvy culture. Read More

Cognitive computing as a wearable prosthetic

November 6, 2014
Wearable cognitive prosthetics sounds like science fiction, but it’s easily within the reach of today’s technology. From a healthcare analytics standpoint, image-analytics wearables could help many people who suffer from diverse memory, perception and learning impairments.  Read More

Bringing cloud-based modernization to data warehousing and analytics

October 28, 2014
IBM this week announced dashDB, which brings cloud-based modernization to data warehousing and analytics. dashDB helps customers to ensure that infrastructure doesn't stand in the way of them realizing fast value from an agile enterprise data warehouse. Many smaller and mid-sized users have little data-warehousing infrastructure to begin with, so a public cloud service such as dashDB might make perfect sense for them as an on-ramp. And many larger enterprises can benefit from dashDB as a robust cloud-based platform that supplements on-premises platforms within their multi-tier data-warehousing infrastructure. Read More

What's keeping data science from playing a more central role in public policy?

October 23, 2014
Data science by itself is an ineffectual civic-governance tool if it lacks strong champions who can wield it to get things done in the legislative, executive and judicial branches. Big data analytics can influence public policy if it helps frame a compelling case in the minds of decision makers for taking this or that action. And if data scientists can show that a counterintuitive scenario is more valid than "common sense" or "gut feel" on a particular decision, they just may change the terms of debate in public policy discussions. Read More

Collaborations and correlations in the common cause

October 16, 2014
I'm impressed with initiatives in the U.S. data scientist community to volunteer their time to worthy causes at home and abroad. Clearly, most of the data scientists who participate in communities such as New York-based DataKind have day jobs to pay the bills. But they see larger humanitarian causes (reuniting refugees, curing infectious diseases, feeding hungry populations and guaranteeing civil rights to the disenfranchised for example) that can benefit from the smartest data scientists applying their best efforts and most sophisticated tools to the task. To sustain the engagement of the data science community in these common causes, what's needed is for people and institutions to open source all of their decision-support assets: data, analytics, tools, platforms and, of course, expertise. Read More

Distributing data science brainpower more equitably among the haves and have-nots

October 9, 2014
Data scientists, like anybody else, tend to gravitate to where the jobs are, especially those that fetch higher salaries, offer the resources needed to achieve their dreams and promise more rewarding career paths. For that reason, larger employers with well-established, amply funded big data initiatives tend to have an advantage over smaller organizations when it comes to recruiting the best and brightest data scientists. In order to more equitably distribute data scientist expertise among the haves and have-nots, the requisite skills, tools and platforms need to become more widely available at low or no cost. Read More

Using analytics to help hospitals avoid inadvertently sickening patients and their caregivers

October 2, 2014
The invisible spread of infections in healthcare facilities has continued to run rampant. Healthcare associated infections (HAIs) remain a serious threat everywhere in the world. Nevertheless, pathogen-caused infections, though they spread invisibly in healthcare environments, can be illuminated through judicious deployment of advanced analytics. Indeed, advanced analytics, which involves applying statistical methods to trustworthy data, has long been used to reveal invisible patterns of all sorts. Consequently, their potential role in HAI identification and risk mitigation should be obvious. Read More

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