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	<title>Comments on: So You Want to be an Information Architect?</title>
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		<title>By: Andrea</title>
		<link>http://www.dataversity.net/so-you-want-to-be-an-information-architect/#comment-213616</link>
		<dc:creator>Andrea</dc:creator>
		<pubDate>Mon, 25 Mar 2013 21:14:20 +0000</pubDate>
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		<description><![CDATA[This is a pretty interesting list, but I think this kind of list is adding to the confusion I am seeing in many company&#039;s attempts at hiring Data/Information Architects. For example, I know many IAs that have nothing to do with UI/interface, in fact, while at one time I was a coder - I do not code anymore. I think of this as a cluster chart - which skills cluster together (front end/back end, Big data/relational data, etc.). 
There are levels of IAs for one and there are different applications of architectural knowledge. Big Data is highly dependent on Statistics, and benefits heavily from Six Sigma, LEAN and BPM. The best use of an IA is to not overload the expectation but instead expect realistic outcomes, from an enterprise perspective I prefer to have experts execute their specialties (i.e. developers = develop code). 
The IA that takes your primordial ooze of non-integrated, non-described data and drives actionable meaning may not be the same IA that sustains the system over time. I know many IAs that are clueless in mastering data but are flawless in enterprise data management. 
So I think of this in terms of the skills required and how they develop and land - experience is a key player in quality of execution and solution.]]></description>
		<content:encoded><![CDATA[<p>This is a pretty interesting list, but I think this kind of list is adding to the confusion I am seeing in many company&#8217;s attempts at hiring Data/Information Architects. For example, I know many IAs that have nothing to do with UI/interface, in fact, while at one time I was a coder &#8211; I do not code anymore. I think of this as a cluster chart &#8211; which skills cluster together (front end/back end, Big data/relational data, etc.).<br />
There are levels of IAs for one and there are different applications of architectural knowledge. Big Data is highly dependent on Statistics, and benefits heavily from Six Sigma, LEAN and BPM. The best use of an IA is to not overload the expectation but instead expect realistic outcomes, from an enterprise perspective I prefer to have experts execute their specialties (i.e. developers = develop code).<br />
The IA that takes your primordial ooze of non-integrated, non-described data and drives actionable meaning may not be the same IA that sustains the system over time. I know many IAs that are clueless in mastering data but are flawless in enterprise data management.<br />
So I think of this in terms of the skills required and how they develop and land &#8211; experience is a key player in quality of execution and solution.</p>
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