The three things that matter most in the new age of enterprise architecture

30 Apr 2018

by Magnus Valmot

For many years, enterprise architecture was a discipline shrouded in mystery. It was regarded as complicated and somewhat perplexing, but that is all changing. The future of EA is focused on transparency, and enabling businesses to easily adopt changes that will provide long-term financial benefits.

Enterprise architecture (EA) isn’t new. It has played a crucial role in helping businesses understand their internal infrastructure for over 30 years, but in that time, it has been somewhat resistant to change. While the digital landscape it oversees evolved dramatically around it, those practicing EA tended to remain steadfastly loyal to the discipline’s roots.

digital landscape

And, largely due to EA’s inability — or perhaps reluctance — to change, it has journeyed through plenty of turbulence. In 2007, Gartner predicted that ”by 2012, 40 percent of enterprise architecture programs will be stopped”, only to state in 2015 that “70 percent of organizations are starting or restarting an EA program”. As these two very disparate quotes suggest, EA has recently gone through a period of great transition and change, and it was sorely needed.

Significant lessons have been learned over last decade or so, but the primary takeaway is this: EA needed to change in order to remain relevant, and such change meant completely reevaluating EA so as it make it as beneficial to businesses as possible.

Enterprise Architecture transition

“EA has recently gone through a period of great transition and change, and it was sorely needed”

So what matters most in EA today, and what approaches should be utilised?

Our research has revealed that three things matter most to progressive enterprise architects, and to CxOs endorsing new EA practices.

1. Ease and automation of input: Out-of-the-box integrations and no-UI-learning-needed EA participation features

For EA to deliver ’quick wins’ in addition to laying the foundations for more long-term transformation, the EA platform’s design plays a critical role. This means having pre-built data import integrations from Excel, AWS, Swagger, ServiceNow, MuleSoft, CMDBs, etc., in addition to enabling no-UI, no-software-training-needed EA participation features. This allows diverse and broad stakeholder communities to participate in business process mapping, analysis, and validation feedback loops.

All EA programs are only as good as the data in the platform, hence why it is crucial this is highly automated and simple to operate manually.

Enterprise Architecture programs

“All EA programs are only as good as the data in the platform”

artificial intelligence and information augmentation

2. AI and IA: Artificial intelligence and information augmentation

AI is a complicated subject, and that is partly because its parameters are in a constant state of flux. As technology advances, and as computers become more capable of understanding and subsequently learning, AI is revealing itself to have more and more applications across any number of business functions.

In the context of EA, AI is showing particular promise across graph-based EA platforms, where data is, by default, in a structured format. AI allows enterprise architects to run automated — even self-learning — graph searches across many layers of different data sets, all inter-connected on the same graph.


"AI is a complicated subject, and that is partly because its parameters are in a constant state of flux"

Given the complexity of EA tools, their ever-expanding functional realms, and the nature of their use for analysis based on syndication of large data sets across different business realms, this is an area where AI will drive real results, delivering maintenance simplification and intelligent insight directly to appropriate stakeholders, all in a personalised way.

However, most organisations may still be better served focusing on IA (information augmentation), instead of rushing to immature first generation AI solutions. Putting IA ahead of AI in EA means ensuring data is structured, and that there are appropriate links (relationships) between different data sets.

3. Business user friendly output: Enabling EAs to tell stories and communicate with non-EAs

EA has too much jargon. EA visualisations have tended to be incredibly cluttered, meaning it can be difficult for non-EA employees to see what is relevant to them. EA tools are rarely designed for communication, so it is here that most EAs could use an additional helping hand.

When it comes to software, most designers look to create programs that are intuitive, and can be operated without the need for incredibly specialised knowledge. What this means is that complex tooling requiring month-long training sessions will soon be a thing of the past. For EAs, this is something that can’t come soon enough.

The EA platforms set to dominate the scene in the years to come will need to deliver customizable, easily digestible visualisations natively from the platform, in an automated way. With EA well and truly established as a discipline at the forefront of digital transformation and business process re-engineering, it is vital that the right tools are used to guarantee the most favourable returns, and help organisations expand and evolve effectively.

New call-to-action

Magnus Valmot

Magnus is dedicated to building great teams of people in a shared mission to bring trusted, tangible value to our customers and everyone we work with.


Subscribe to our newsletter to get the latest news, views and opinions straight to your inbox.