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<div class="vevent h-event"> <h1 class="summary p-name">Good Data, Bad Info @DAMA PDX - November 16</h1> <div class='date'><time class="dtstart dt-start" title="2017-11-16T08:30:00" datetime="2017-11-16T08:30:00">Thursday, November 16, 2017 from 8:30</time>–<time class="dtend dt-end" title="2017-11-16T11:30:00" datetime="2017-11-16T11:30:00">11:30am</time></div> <div class="location vcard p-location h-card"> <a class="url" href='http://calagator.org/venues/202395720'><span class='fn org p-name'>The Standard</span></a> <div class="adr p-adr h-adr"> <div class="street-address p-street-address">900 SW Fifth Ave</div> <span class="locality p-locality">Portland</span> , <span class="region p-region">OR</span> <span class="postal-code p-postal-code">97204</span> <div class="country-name p-country-name">USA</div> (<a href='https://maps.google.com/maps?q=900%20SW%20Fifth%20Ave,%20Portland%20OR%2097204%20USA'>map</a>) </div> </div> <div class="description p-description"> <p>Join your fellow Portland area Lovers of Data for breakfast, networking, and a data talk by Michael Scofield from Loma Linda University; hosted by your local Portland DAMA chapter. We meet at in Downtown Portland at 8:30am for registration and networking with the presentation from 9 to 11:30am.</p> <p>RSVP Required. More Information and RSVP at: <a href="https://damapdx.wordpress.com/2017/10/05/november-2017-good-data-bad-info/">https://damapdx.wordpress.com/2017/10/05/november-2017-good-data-bad-info/</a></p> <p>Free for members and employees of corporate members.<br>Students only $5 (to cover breakfast and speaker travel).</p> <h3>About "Good Data, Bad Info"</h3> <p>Producing decision-able information from lots of raw data often requires complex processes which must be carefully designed and architected.</p> <p><b>Mind the Gap:</b> This presentation looks at the gap between raw data and decision-able information. Most of the chatter about data quality focuses upon raw, granular data describing discrete events and entities inside the enterprise and around it. But for data warehouses and decision-support, data must be converted into useful information meaningful to the decision-maker.</p> <p><b>Raw or Derived? Trend and Pattern Examples:</b> We will also examine numerous examples between raw data and derived data (“information”) which show us trends, patterns, etc. Examples come from astronomy, military intelligence, and criminology. For example, in image analysis (in the intelligence community) we see a succession of “derived” data as we move from pixels to clues about the enemy’s strategic intent.</p> <p><b>The 9 MetaData Types - Which and Why:</b> As an enterprise is inundated with more data (raw, intermediate, and advanced information) the designing and population of good metadata becomes even more important. We will look at nine distinct kinds of metadata. Not all kinds are of equal importance, but we will show the value of each. The larger the organization, and the more diverse the “institutional memory”, the more important it is to properly document the data asset for broader exploitation of its value.</p> <p><b>Correct Conclusions? Handle with Care:</b> We will also look at how incorrect conclusions may be drawn because of mishandling of the process to derive “higher level” information from the raw data.</p> <h3>About DAMA Portland</h3> <p>The Portland Metro Chapter of the Data Administration Management Association has been serving the Portland data community since 1984. We are a not-for-profit, vendor independent, professional association dedicated to advancing the concepts and practices of enterprise information and data resource management.</p> <p>Our primary purpose is to promote the understanding, development and practice of managing data, information and knowledge resources as key enterprise assets.</p> <p>The DAMA Portland Chapter is dedicated to delivering thought provoking data-centric presentations that will make you more successful in your job. Please join me, the rest of the DAMA board, and other fellow lovers of data at this regular chapter meeting in downtown Portland.</p> <p>Thanks for your support of local Data Education, we look forward to seeing you there!</p> </div> <h3>Links</h3> <ul> <li><a class="url u-url" href="https://damapdx.wordpress.com/2017/10/05/november-2017-good-data-bad-info/">Website</a></li> </ul> </div>

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Saturday, November 4, 2017 at 12:04am.

Good Data, Bad Info @DAMA PDX - November 16

Thursday, November 16, 2017 from 8:30–11:30am

The Standard

900 SW Fifth Ave

Portland, OR 97204, USA (map)

Website

https://damapdx.wordpress.com/2017/10/05/november-2017-good-data-bad-info/

Description

Join your fellow Portland area Lovers of Data for breakfast, networking, and a data talk by Michael Scofield from Loma Linda University; hosted by your local Portland DAMA chapter. We meet at in Downtown Portland at 8:30am for registration and networking with the presentation from 9 to 11:30am.

RSVP Required. More Information and RSVP at: https://damapdx.wordpress.com/2017/10/05/november-2017-good-data-bad-info/

Free for members and employees of corporate members.
Students only $5 (to cover breakfast and speaker travel).

About "Good Data, Bad Info"

Producing decision-able information from lots of raw data often requires complex processes which must be carefully designed and architected.

Mind the Gap: This presentation looks at the gap between raw data and decision-able information. Most of the chatter about data quality focuses upon raw, granular data describing discrete events and entities inside the enterprise and around it. But for data warehouses and decision-support, data must be converted into useful information meaningful to the decision-maker.

Raw or Derived? Trend and Pattern Examples: We will also examine numerous examples between raw data and derived data (“information”) which show us trends, patterns, etc. Examples come from astronomy, military intelligence, and criminology. For example, in image analysis (in the intelligence community) we see a succession of “derived” data as we move from pixels to clues about the enemy’s strategic intent.

The 9 MetaData Types - Which and Why: As an enterprise is inundated with more data (raw, intermediate, and advanced information) the designing and population of good metadata becomes even more important. We will look at nine distinct kinds of metadata. Not all kinds are of equal importance, but we will show the value of each. The larger the organization, and the more diverse the “institutional memory”, the more important it is to properly document the data asset for broader exploitation of its value.

Correct Conclusions? Handle with Care: We will also look at how incorrect conclusions may be drawn because of mishandling of the process to derive “higher level” information from the raw data.

About DAMA Portland

The Portland Metro Chapter of the Data Administration Management Association has been serving the Portland data community since 1984. We are a not-for-profit, vendor independent, professional association dedicated to advancing the concepts and practices of enterprise information and data resource management.

Our primary purpose is to promote the understanding, development and practice of managing data, information and knowledge resources as key enterprise assets.

The DAMA Portland Chapter is dedicated to delivering thought provoking data-centric presentations that will make you more successful in your job. Please join me, the rest of the DAMA board, and other fellow lovers of data at this regular chapter meeting in downtown Portland.

Thanks for your support of local Data Education, we look forward to seeing you there!