James Kobielus

James Kobielus is a big data evangelist at IBM and the editor in chief of IBM Data magazine. He is an industry veteran, a popular speaker, social media participant, and a thought leader in big data, Apache Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management, and next-best action technologies.

facilitating-flow

Facilitating Flow

Data needs to flow to be of any use. That flow needs to pervade the often complex infrastructures through which data is processed and delivered and mirror the rhythm of activities within which the data is being originated.

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Technologies

How to Game the Turing Test

A Turing test may mimic human intelligence, but true intelligence is acquired through life experiences

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industrial-data-hammering

Industrialized Data Hammering

Industrial processes fascinate me, not just in their automation, speed, and efficiency, but also in their ability to give precise shape to an otherwise unruly raw material.

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inference-plubming

Inference Plumbing

Information flows through many channels as it rushes from myriad sources to the places where people apply it in real-world situations. Data’s diversity is a challenge that can best be met through implementation of the right tools for integration, capacity, and delivery of its precious payload of insight and guidance.

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distinguishing-datas-latency-from-its-actionability copy
Database and platform

Distinguishing Data’s Latency From Its Actionability

Does in-memory persistence remove technological barriers that prevent business outcomes at any latency?

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finding-fast-answers

Finding Fast Answers

Smart thoughts come quite readily to the prepared mind. Finding insights inside big data is easy if you’ve prepared, organized, and tuned it all for speedy access.

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cracking-the-code

Cracking the Code

Nature doesn’t reveal its deepest secrets to the casual observer. The edifice of scientific knowledge has risen because of the diligence and ingenuity of many smart people over many centuries. But it’s never perfectly secure in its foundations

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black-swans-sitting-ducks-and-geospatial-analytics
Strategies

Black Swans, Sitting Ducks, and Geospatial Analytics

Find out how data scientists can use geospatial analytics to help mitigate risks from natural disasters

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thinking-like-a-data-scientist

Thinking Like a Data Scientist

The best data scientists are critical thinkers par excellence. Acquiring skills in quantitative analysis and programming is not enough, and even a strong background in some domain specialty doesn’t guarantee a data scientist’s effectiveness.

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automating-the-information-lifecycles

Automating the Information Lifecycle

Enterprise data is useless if it doesn’t flow where it’s needed. And it’s just a waste of precious resources if it takes an army of technicians and analysts to put it in the right shape and context.

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holistic-data-management

Holistic Data Management

Holistic data analytics environments help organizations to access, manage, and leverage their information assets comprehensively.

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enterprise-grade

Enterprise Grade

When you’re betting the business on your infrastructure, stringent requirements come into play. Determining whether you have an enterprise-grade big data platform requires many interlocking considerations. Compliance, extensibility, functionality, performance, reliability, and security are among the many criteria you need to build into both the infrastructure and your operating procedures. Lacking comprehensive controls and assurances in all these areas exposes the organization and all its assets to excessive risk. In IBM Data magazine the first week of 2015, we have …

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