Enterprise Architecture

EA Heuristic #5: Address the concern of “you are echoing back what I told you”

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(this article is part of the series “12 Heuristics for Enterprise Architecting“)

Stop parroting me! (photo credit: Ferran Pestaña) 

In some ways, the “as-is” phase of enterprise architecting exercises do not generate new content. It brings together observations from different parts of the organization and synthesizes them. Though the synthesis often produce “fresh” insights, these insights might seem obvious to the organization in retrospect, as they are often associated with pains that the organization has been suffering under. In our EA exercise, we received feedback on our “as-is” analysis that ranged from being “spot on” to “echoing back what I told you”, and that seem confusing until we sat down and analyzed the issue.

Learning from this experience, we could have explained to the organization why the EA team was not simply echoing back to them using the earlier mentioned logic. What we did do was to explain to them the EA methodology, and how we relied on it to systematically analyze the organization.

Announcing: Third Annual Enterprise Summer School

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Week 31 is for students and researchers as well as practitioners in the field of enterprise architecture, who want to spend a pracademic week together and share and learn more about EA. 
Dates: 30 July – 3 Aug 2012
Location: IT University of Copenhagen
REGISTER NOW
Themes:

EA as a Discipline and a […]

Gartner et al. – gettin’ there on EA

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Nice to see that even the ‘big fish’ are finally ‘gettin’ there’ on the real scope of enterprise-architecture… A month ago we saw Open Group begin to re-frame their previous IT-centred approach to EA into a new style of ‘enterprise transformation’. (The conference was still more IT than anything else, of course, but at least […]

EA Heuristic #4: Collect and analyze data with reusability in mind

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Metal Bottle Cap Flowers (credit: urban woods walker)
(this article is part of the series “12 Heuristics for Enterprise Architecting“)
EA exercises involve collecting a lot of data and distilling insights from them.  It seems wasteful if the fruits of these efforts are used just once and then thrown away.  Instead, when collecting data and doing analysis on them, it is useful to think about how the data and analysis can be re-used.

Specifically, as discussed in Heuristics #2: Guess, Validate, Iterate: Time-bound architectural efforts, the insights are likely to contain some inaccuracies, so expect gradual refinement of the data and analysis.  To do that, how the analysis is derived from the data should be clear.  In addition, involve as much as possible the people who are going to update it in the future.  The more familiar they are with the collected data and analysis, the more likely they will reuse it in the future.

During our EA exercise, we created a chart showing the key products offered by the organization, along with each product’s importance and satisfaction level.  We created a first draft of the chart, then we explained to the organization how we did it, and then they refined it.  We hope that they will reuse this chart, and we are more confident of it since they have already updated it once.  In addition, when we created the summary of our key findings, we tried as much as we can to resist the temptation of listing down gut feels and focus on findings that are backed up by data.

EA Heuristic #4: Collect and analyze data with reusability in mind

by

Metal Bottle Cap Flowers (credit: urban woods walker)
(this article is part of the series “12 Heuristics for Enterprise Architecting“)
EA exercises involve collecting a lot of data and distilling insights from them.  It seems wasteful if the fruits of these efforts are used just once and then thrown away.  Instead, when collecting data and doing analysis on them, it is useful to think about how the data and analysis can be re-used.

Specifically, as discussed in Heuristics #2: Guess, Validate, Iterate: Time-bound architectural efforts, the insights are likely to contain some inaccuracies, so expect gradual refinement of the data and analysis.  To do that, how the analysis is derived from the data should be clear.  In addition, involve as much as possible the people who are going to update it in the future.  The more familiar they are with the collected data and analysis, the more likely they will reuse it in the future.

During our EA exercise, we created a chart showing the key products offered by the organization, along with each product’s importance and satisfaction level.  We created a first draft of the chart, then we explained to the organization how we did it, and then they refined it.  We hope that they will reuse this chart, and we are more confident of it since they have already updated it once.  In addition, when we created the summary of our key findings, we tried as much as we can to resist the temptation of listing down gut feels and focus on findings that are backed up by data.