Aggregated enterprise architecture wisdom
By Loren K. Baynes, Director, Global Marketing Communications, The Open Group On Monday October 19, Allen Brown, President and CEO of The Open Group, welcomed over 230 attendees from 26 countries to the Edinburgh International Conference Center located in the … Continue reading →
Chess and the Art of Enterprise Architecture and Mastering ArchiMate Edition II (both PDF) are now available as a bundle purchase with a discount of €5 for the two together. Please visit either the Chess and the Art of Enterprise … Continue reading →
Whatever business you are in innovation is the name of the game. Today it’s even more important than ever that you innovate fast and somewhat accurate. This little card is designed to help you go fast by staying small and keeping it nimble. When you should use this Whenever you need to innovate When you […]
By The Open Group Background The automotive industry is currently facing massive challenges. For the past 30-40 years, automakers have faced stiff competition in the marketplace, as well as constant pressure to make more innovative and efficient vehicles while reducing … Continue reading →
In the old days I would have liked to have an idiot’s manual to guide me through the steps of modelling the Architecture of the Enterprise I was working for. I am not talking about the practices of organization, risks, requirements… which fram…
More and more QualiWare users consider a consensus-driven management philosophy and enterprise collaboration to be a key driver for business agility and innovation. This has always been essential for QualiWare when we design our products and services. For several years, we have been surprised and disappointed that Gartner sticks to a rather traditional view on EA […]
Enterprise Architecture is facing several challenges as a discipline and a practice. In this blog post, John Gøtze outlines four central challenges, and discusses what should be done. He suggests that enterprise architecture management must focus on enterprise collaboration. The Challenges The discipline Enterprise Architecture (EA) is at a crossroads, facing four challenges: The first […]
A frequent question I get from data management and governance teams is how to stay ahead of or on top of the Agile development process that app dev pros swear by. New capabilities are spinning out faster and faster, with little adherence to ensuring compliance with data standards and policies.
Well, if you can’t beat them, join them . . . and that’s what your data management pros are doing, jumping into Agile development for data.
Forrester’s survey of 118 organizations shows that just a little over half of organizations have implemented Agile development in some manner, shape, or form to deliver on data capabilities. While they lag about one to two years behind app dev’s adoption, the results are already beginning to show in terms of getting a better handle on their design and architectural decisions, improved data management collaboration, and better alignment of developer skills to tasks at hand.
But we have a long way to go. The first reason to adopt Agile development is to speed up the release of data capabilities. And the problem is, Agile development is adopted to speed up the release of data capabilities. In the interest of speed, the key value of Agile development is quality. So, while data management is getting it done, they may be sacrificing the value new capabilities are bringing to the business.
Let’s take an example. Where Agile makes sense to start is where teams can quickly spin up data models and integration points in support of analytics. Unfortunately, this capability delivery may be restricted to a small group of analysts that need access to data. Score “1” for moving a request off the list, score “0” for scaling insights widely to where action will be taking quickly.
A frequent question I get from data management and governance teams is how to stay ahead of or on top of the Agile development process that app dev pros swear by. New capabilities are spinning out faster and faster, with little adherence to ensuring compliance with data standards and policies.
Well, if you can’t beat them, join them . . . and that’s what your data management pros are doing, jumping into Agile development for data.
Forrester’s survey of 118 organizations shows that just a little over half of organizations have implemented Agile development in some manner, shape, or form to deliver on data capabilities. While they lag about one to two years behind app dev’s adoption, the results are already beginning to show in terms of getting a better handle on their design and architectural decisions, improved data management collaboration, and better alignment of developer skills to tasks at hand.
But we have a long way to go. The first reason to adopt Agile development is to speed up the release of data capabilities. And the problem is, Agile development is adopted to speed up the release of data capabilities. In the interest of speed, the key value of Agile development is quality. So, while data management is getting it done, they may be sacrificing the value new capabilities are bringing to the business.
Let’s take an example. Where Agile makes sense to start is where teams can quickly spin up data models and integration points in support of analytics. Unfortunately, this capability delivery may be restricted to a small group of analysts that need access to data. Score “1” for moving a request off the list, score “0” for scaling insights widely to where action will be taking quickly.
In my last post, “Locking Down the Prisoners: Control, Conflict and Compliance for Organizations”, I returned to a topic that I’ve been touching on periodically over the last year, organizations as systems, which overlaps significantly with the topic of enterprise architecture (not to be confused with enterprise IT architecture of which EA is a superset). […]
Srinidhi Boray | Ingine, Inc | Bioingine.com Introducing, Ingine, Inc. it is a startup in its incipient stages of developing BioIngine platform, which brings advancement in data science around Interoperability. Particularly with healthcare data mining and analytics dealing with medical knowledge extraction. Below are some of the lessons learned discussed while dealing with the healthcare […]
I’ve just finished writing a fascinating Report for Cutter Consortium about Business Patterns and EA. For some time now I’ve been working with EA practitioners who are taking advantage of information that has already been gathered by people from backgrounds other than EA. The EA role then becomes one of converting or translating this information…