Over the past decade at Real Story Group we’ve been privy to (and participated in) dozens of digital and marketing technology transformation initiatives.

All start with the best of intentions.  Some get bogged down after initial CapEx bursts result in a long train of higher OpEx obligations.  Some lose steam when the enterprise discovers its underlying content and data estates aren’t mature enough to support new customer experiences at the glass.  Some successfully transition to evergreen modernization programs that create consistent incremental value and perhaps unlock new business models.

One of the first things I look for when advising on these efforts is: where’s the Enterprise Architect? If the answer comes back, “we don’t have one,” it bodes trouble ahead.

What Enterprise Architects Do

Full disclosure, I’m not an Enterprise Architect (EA) myself.  But I’ve worked with enough good ones over the years to appreciate what they do.

You can find various definitions of Enterprise Architects. For our purposes here, let’s just say that EAs work to align an organization's business strategy and operational model with processes and IT services to achieve key objectives. As a practical matter, they maintain blueprints for designing and implementing IT solutions that meet those objectives.

Put another way, EAs help transition visions to reality.  Alas, many digital transformation initiatives start out long on vision and kinda short on the reality part, which unfortunately can put EAs in the difficult position of explaining real-life constraints.  Yet a strong EA will also point out real opportunities.

Role of an EA in Digital and MarTech Modernization

First of all, an EA should own annotated blueprints for your MarTech stack – along with adjacent systems – in both “as-is” and “to-be” incarnations.  Of course, the to-be version can prove trickier, and this is an area where Real Story Group typically helps out.  Various kinds of blueprints can fall into scope here, but one essential model is a logical visualization of key marketing technology services, categorized into groups and tiers, with cues about how and where they intersect.

Addressing this as-is version for a moment, I’m frequently surprised by the number of enterprises that cannot muster a diagram of their MarTech / Digital / Customer Data stacks.  Or they rely on some sort of infographic that doesn’t designate meaningful categories or the relationships among systems, with no identification of key centers of gravity.  They don’t tell you what’s really going on. Hard to know where you're going if you don't know where you're starting from.

Like any other picture, an effective as-is stack diagram tells a story, about decisions you've made -- good or bad -- and what you’ve prioritized to date in MarTech.

An effective to-be stack blueprint makes an argument: about the best way to align your Digital and MarTech investments with evolving business strategies and objectives.  It should encapsulate a strategic agreement on where your stack is going, and why. It’s different than an actionable roadmap (we do those, too), but does imply a path.

Source: Real Story Group (RSG)

RSG's generic Stack Reference Model above requires some shaping to fit into any specific enterprise context. Yet it still incorporates several arguments about the future of MarTech, most notably around a "legless" architectural pattern. Contact us for other details.

Beyond Blueprints

EAs also play a useful role for MarTech leaders on an ongoing basis. In my experience, a good EA will serve as a resource to as well as a check on colleagues.  EAs become a valuable resource to Digital and MarTech product managers (you have product managers, right? …right?), who inevitably need to understand the bigger picture when working through capability roadmaps.  EAs also serve as a check on scope; for example, preventing a new platform from spinning up yet another customer messaging service, when you already have more than enough of those in your stack.

EAs can also explain to busy Finance leaders why certain things are needed or superfluous.  They also serve as useful explainers about why some MarTech and Customer Data services represent more of a cost of doing business, rather than a more traditional ROI justification.  To be sure, some EAs are better than others at this.

In the longer run, established EAs provide institutional memory.  MarTech talent tends to migrate from company to company, and too frequently gets cut back during temporary business down-turns.  In those cases, EAs bring institutional knowledge about the when, why, where, and how of your MarTech stack.  They can answer questions like: What will happen downstream if we decommission the SQL Server instance that happens to aggregate some key customer transaction data?

EAs may sometimes annoy you by pointing out the things your enterprise should have addressed five years ago to prepare you for today’s challenges, but they almost surely won’t be wrong. Here as elsewhere, EAs frequently think about long-term capacity building. This means they can bring useful input into resource needs and decisions about what and where your MarTech team should outsource.

More to Add?

I’m thinking there’s more you could add here. Feel free to do so on LinkedIn.  And then please share this post with your EA friends so we can all learn together…