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	<title>Comments for Mastering Data Management</title>
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		<title>Comment on Where East Meets West: ehealth in China and the US by ICMCC News Page &#187; Where East Meets West: ehealth in China and the US</title>
		<link>http://masteringdatamanagement.com/index.php/2012/05/08/where-east-meets-west-ehealth-in-china-and-the-us/comment-page-1/#comment-21073</link>
		<dc:creator>ICMCC News Page &#187; Where East Meets West: ehealth in China and the US</dc:creator>
		<pubDate>Thu, 10 May 2012 13:12:26 +0000</pubDate>
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		<description>[...] Article Lorraine Fernandes, Mastering Data Management, 9 May 2012 [...]</description>
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		<title>Comment on Training as a Safety Net: Kim May by Kim&#8217;s Training Podcast</title>
		<link>http://masteringdatamanagement.com/index.php/2012/05/07/training-as-a-safety-net-kim-may/comment-page-1/#comment-21069</link>
		<dc:creator>Kim&#8217;s Training Podcast</dc:creator>
		<pubDate>Tue, 08 May 2012 17:34:23 +0000</pubDate>
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		<description>[...] The podcast is on Crysta&#8217;s site; click here. [...]</description>
		<content:encoded><![CDATA[<p>[...] The podcast is on Crysta&#8217;s site; click here. [...]</p>
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		<title>Comment on New ebook: Understanding Big Data by TDWI Chicago Preview: Data Warehousing, Analytics &#38; More &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/26/new-ebook-understanding-big-data/comment-page-1/#comment-21067</link>
		<dc:creator>TDWI Chicago Preview: Data Warehousing, Analytics &#38; More &#124; Mastering Data Management</dc:creator>
		<pubDate>Mon, 07 May 2012 20:00:34 +0000</pubDate>
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		<description>[...] be giving away hard copies of Understanding Big Data for your reading pleasure – and we just might have a few Watson hats tucked away for people up to [...]</description>
		<content:encoded><![CDATA[<p>[...] be giving away hard copies of Understanding Big Data for your reading pleasure – and we just might have a few Watson hats tucked away for people up to [...]</p>
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		<title>Comment on My Wife, Heater: An Identity Crisis by Who’s Afraid of the Big Bad Data? &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2010/12/15/my-wife-heater-an-identity-crisis/comment-page-1/#comment-21061</link>
		<dc:creator>Who’s Afraid of the Big Bad Data? &#124; Mastering Data Management</dc:creator>
		<pubDate>Tue, 01 May 2012 17:12:14 +0000</pubDate>
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		<description>[...] is this possible, you might ask? As I’ve alluded in previous blogs, it’s all about the dissemination of bad [...]</description>
		<content:encoded><![CDATA[<p>[...] is this possible, you might ask? As I’ve alluded in previous blogs, it’s all about the dissemination of bad [...]</p>
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		<title>Comment on MDM for Chief Privacy Officers: A healthcare provider example by Series: CxOs have a lot on their minds. Is MDM one of them? &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/25/mdm-for-chief-privacy-officers-a-healthcare-provider-example/comment-page-1/#comment-21056</link>
		<dc:creator>Series: CxOs have a lot on their minds. Is MDM one of them? &#124; Mastering Data Management</dc:creator>
		<pubDate>Wed, 25 Apr 2012 22:11:38 +0000</pubDate>
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		<description>[...] MDM for Chief Privacy Officers: A healthcare provider example [...]</description>
		<content:encoded><![CDATA[<p>[...] MDM for Chief Privacy Officers: A healthcare provider example [...]</p>
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		<title>Comment on Series: CxOs have a lot on their minds. Is MDM one of them? by MDM for Chief Privacy Officers: A healthcare provider example &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2012/02/29/series-cxos-have-a-lot-on-their-minds-is-mdm-one-of-them/comment-page-1/#comment-21055</link>
		<dc:creator>MDM for Chief Privacy Officers: A healthcare provider example &#124; Mastering Data Management</dc:creator>
		<pubDate>Wed, 25 Apr 2012 22:07:49 +0000</pubDate>
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		<title>Comment on 37 Definitions of &#8220;Employee ID&#8221; by Defining Terms: Agree, or Doom your Project &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/24/37-definitions-of-employee-id/comment-page-1/#comment-21039</link>
		<dc:creator>Defining Terms: Agree, or Doom your Project &#124; Mastering Data Management</dc:creator>
		<pubDate>Tue, 24 Apr 2012 15:18:29 +0000</pubDate>
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		<description>[...] 37 Definitions of &#8220;Employee ID&#8221; [...]</description>
		<content:encoded><![CDATA[<p>[...] 37 Definitions of &#8220;Employee ID&#8221; [...]</p>
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		<title>Comment on Defining Terms: Agree, or Doom your Project by 37 Definitions of &#8220;Employee ID&#8221; &#124; Mastering Data Management</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/16/defining-terms-agree-or-doom-your-project/comment-page-1/#comment-21038</link>
		<dc:creator>37 Definitions of &#8220;Employee ID&#8221; &#124; Mastering Data Management</dc:creator>
		<pubDate>Tue, 24 Apr 2012 14:41:59 +0000</pubDate>
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		<description>[...] Defining Terms: Agree, or Doom your Project [...]</description>
		<content:encoded><![CDATA[<p>[...] Defining Terms: Agree, or Doom your Project [...]</p>
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		<title>Comment on Defining Terms: Agree, or Doom your Project by Paula Wiles Sigmon</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/16/defining-terms-agree-or-doom-your-project/comment-page-1/#comment-21037</link>
		<dc:creator>Paula Wiles Sigmon</dc:creator>
		<pubDate>Tue, 24 Apr 2012 14:16:31 +0000</pubDate>
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		<description>That sounds like a pragmatic approach, Axel.  Perhaps it’s a bit like creating an enterprise data warehouse for key enterprise-wide data, while still deriving value from departmental or divisional data marts for data that has more parochial interest.

In your description, one of the key steps would be the one where you recommend linking objects in the local model with semantically corresponding objects of the global model.  If that step isn’t consistently implemented, then we’d be back in that familiar but undesirable territory characterized by islands of data, islands of metadata, islands of integration.</description>
		<content:encoded><![CDATA[<p>That sounds like a pragmatic approach, Axel.  Perhaps it’s a bit like creating an enterprise data warehouse for key enterprise-wide data, while still deriving value from departmental or divisional data marts for data that has more parochial interest.</p>
<p>In your description, one of the key steps would be the one where you recommend linking objects in the local model with semantically corresponding objects of the global model.  If that step isn’t consistently implemented, then we’d be back in that familiar but undesirable territory characterized by islands of data, islands of metadata, islands of integration.</p>
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		<title>Comment on Defining Terms: Agree, or Doom your Project by Axel Troike</title>
		<link>http://masteringdatamanagement.com/index.php/2012/04/16/defining-terms-agree-or-doom-your-project/comment-page-1/#comment-21032</link>
		<dc:creator>Axel Troike</dc:creator>
		<pubDate>Sun, 22 Apr 2012 17:11:15 +0000</pubDate>
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		<description>Paula,

Though being desirable in an idealized world, the attempt to unify the terminology is quite problematic in larger organizations, as the standardization process throughout multiple disciplines of the business and IT is not only very time-consuming, but
- may result in artificial deviations from traditional terminology for certain disciplines 
- will leave &quot;winners&quot; and &quot;losers&quot;
- will start again with the next M&amp;A
- may create naming conflicts with the next acquisition of standard software.

To avoid, at least to minimize the above effects, I recommend to decompose the business data model into a &quot;global&quot; data model and several &quot;local&quot; data model (one per each &quot;department&quot; / business discipline). In a nutshell, the approach should be as follows.

&lt;em&gt;For entities and attributes that are supposed to be used &quot;globally&quot;, i.e. by more than one business discipline:
- Create a global model.
- Agree globally on the semantics and definitions.
- Derive a unique, logically meaningful term / name for each object (&quot;common item&quot;) in the global model.

For each business discipline:
- Create entities and attributes in a local model.
- Link objects of a local model with the semantically corresponding objects of the global model (as far as a local model intersects with the global model).
- Agree locally on the semantics and definitions (as far as they are not &quot;governed&quot; by the global model).
- Derive a unique, logically meaningful term / name for each object (common item) in the local model (as far as it is not &quot;governed&quot; by the global model).
- Allow local synonyms for the common items in the global model.&lt;/em&gt;

The above approach reduces frictions among departments with partially overlapping local models and creates a cross-reference system of common items and synonyms. There is no need to &quot;boil the ocean&quot;, but local models can be created as projects come up and be iteratively integrated with an incrementally growing global model.
 
Professional and practical data modeling tools will support the above mentioned steps to link and integrate the global model with local models and to maintain the lineage between the &quot;same&quot; objects in different models.</description>
		<content:encoded><![CDATA[<p>Paula,</p>
<p>Though being desirable in an idealized world, the attempt to unify the terminology is quite problematic in larger organizations, as the standardization process throughout multiple disciplines of the business and IT is not only very time-consuming, but<br />
- may result in artificial deviations from traditional terminology for certain disciplines<br />
- will leave "winners" and "losers"<br />
- will start again with the next M&amp;A<br />
- may create naming conflicts with the next acquisition of standard software.</p>
<p>To avoid, at least to minimize the above effects, I recommend to decompose the business data model into a "global" data model and several "local" data model (one per each "department" / business discipline). In a nutshell, the approach should be as follows.</p>
<p><em>For entities and attributes that are supposed to be used "globally", i.e. by more than one business discipline:<br />
- Create a global model.<br />
- Agree globally on the semantics and definitions.<br />
- Derive a unique, logically meaningful term / name for each object ("common item") in the global model.</p>
<p>For each business discipline:<br />
- Create entities and attributes in a local model.<br />
- Link objects of a local model with the semantically corresponding objects of the global model (as far as a local model intersects with the global model).<br />
- Agree locally on the semantics and definitions (as far as they are not "governed" by the global model).<br />
- Derive a unique, logically meaningful term / name for each object (common item) in the local model (as far as it is not "governed" by the global model).<br />
- Allow local synonyms for the common items in the global model.</em></p>
<p>The above approach reduces frictions among departments with partially overlapping local models and creates a cross-reference system of common items and synonyms. There is no need to "boil the ocean", but local models can be created as projects come up and be iteratively integrated with an incrementally growing global model.</p>
<p>Professional and practical data modeling tools will support the above mentioned steps to link and integrate the global model with local models and to maintain the lineage between the "same" objects in different models.</p>
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