The Huffington Post – May 15, 2013

Why Companies Should Love Their Data — Unequivocally – Paul Vallée

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Wall Street Journal – March 26, 2013

New Machines Make Use of Sun Microsystems’ Technology, Taking Aim at IBM

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Just when you thought you had mastered all the data-riffic buzzwords out there, another rears its trendy head. Never mind big data, we’re talking about “datafication,” the notion that organizations today are dependent upon their data to operate properly — and perhaps even to function at all. Wait, isn’t that what big data is supposedly all about? Not really, says Andrew Waitman, CEO of Pythian, a data infrastructure management and consulting firm.

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January, 2012

Trends in Security: Mitigating the risks of the data explosion

While many organizations are still coming to grips with the brute impact of the data explosion, others are already starting to experience some of its deeper consequences. Massive volumes of data and the increasing integration of data-related systems are producing new risks of human error that require a shift in how companies approach their data administration.

Every step leaves a data trail
The story of King Midas, who turned everything he touched into gold, is in some ways a good analogy for the phenomenon of “datafication.” Nearly everything we touch during our professional and personal lives now turns into some kind of data: when we do our banking, when we book a flight, when we text our friends, when we log onto a webinar, when we download files from the company server.

Businesses create trillions of bytes of data each day. People share more than 30 billion pieces of content a month on Facebook. Passive devices like sensors in cars, computers, smartphones and energy meters log trillions of bytes more. To get a handle on all the information flying around, companies are integrating and consolidating their systems. Integration typically involves having dozens of machines work together in sophisticated ways to serve single applications. Consolidation refers to the trend in which multiple users house their IT assets together for cost efficiencies.

While integration and consolidation are essential, they are also creating an “all eggs in one basket” scenario that organizations need to be aware of—and address. Maybe surprisingly, because we’re talking about data and automated systems, one of the key areas of risk to mitigate is actually human behavior.

The price of a small mistake
Mistakes are costly for any business. And with the advent of datafication, where single systems are carrying more and more responsibility for the business, they’re becoming even costlier. If you’re an online retailer, your entire business depends on your e-commerce platform. If it goes down or is compromised in any way, you face revenue losses, productivity losses, reputational damage and more. In the datafied world, ‘mission critical’ is more critical than ever—meaning even the smallest mistake by an internal or external database administration team can spell catastrophe.

Other fields have extensive experience dealing with this. Healthcare, civil aviation, nuclear safety—all of these have extensively studied the risk associated with human error and how to minimize it. The THERP (techniques for human error prediction) model used in those disciplines needs to be applied in the data sphere as well.

THERP looks at all the potential kinds of errors a person might commit on the job. For errors of omission and sequence, a good checklist and proper training are usually a solid defense. Errors of commission—performing work on the wrong server, forgetting an important “where” clause in a delete statement, shutting down a server outside the appropriate maintenance window because of a time-zone conversion mistake—can be more challenging to prevent.

Can human error be eliminated?
Automating processes, of course, can help prevent human error. Where processes can’t be automated, ensuring they are as fine-tuned and streamlined as possible minimizes the number of opportunities for mistakes to be made.

In working on complex data installations for organizations such as Western Union, Toyota and LinkShare, Pythian has gained valuable experience in the area of human reliability. Pythian studies every incident in much the same way the Transportation Safety Board investigates a plane crash, getting down to the root cause and implementing processes to prevent the same error from being repeated. Application of the THERP approach has enabled Pythian to reduce its human error rate to one every 5.5 person years.

While human error can never be eliminated, it is highly reducible. As organizations seek to manage the sheer volume of data being generated today, in 2013 they should look beyond the purely technical side of things to ensure they are protecting themselves from the risks that data dependency and systems integration can bring.

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Paul Vallée talks to the Ottawa Business Journal about managing a global team.
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Summer 2012
Paul Vallée talks to The Voice about building a market abroad while working in Ottawa.
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NoCOUG Journal, August 2012 – Maris Elsins

This article is the third in a series by Pythian experts that is being published as the “Performance Corner” column in the NoCOUG Journal. SQL Plan Management (SPM) is a feature introduced with Oracle Database 11gR1 that effectively addresses the long-standing problem of unpredictable effects caused on the performance of SQL statements by the changes of optimizer statistics, system configuration, and schema objects, or the patching and upgrading of the database software. In many cases the cause of the degraded performance is the different execution plan produced by the optimizer for the same statement, compared to the one used before.

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April 27, 2012

The Canada China News interviewed Paul Vallée, Founder and Executive Chairman of Pythian to discuss how Pythian attracts top talents from around the world by providing all employees with a sound work environment, great compensation and benefits, and a broad horizon for career development. The linked file includes the original article and our attached translation.

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IOUG Select Journal – Q2, 2012

In this article Gwen Shapira explains what big data is and how it is used effectively, specifically inside an Oracle Big Data Appliance.

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IOUG Select Journal – Q2, 2012

Alex Gorbachev’s IOUG Select article about Oracle Database Appliance; is the first, and so far the only, Oracle engineered system targeted on the lower segment of the database market. The innovation of Oracle Database Appliance (ODA) is in its architectural simplicity. In this article, you will learn the architecture of this appliance, where it is useful (and where it is not) and the independent benchmarks Alex performed.
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