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.
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.
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.
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.
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.
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.
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.
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.
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.
September 25, 2014
Global climate data is massive, diverse and often internally inconsistent. Researchers who attempt to use data science to understand, predict and control global warming find themselves challenged by methodological limitations that frustrate their attempts to fathom this sprawling mosaic. Chief issues include historically thin sources, rampant auto-correlations and heterogeneous data provenance. Tackling global warming requires a harmonious balance between theory-driven domain science and data-driven statistical analysis.