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Enterprise Architecture vs. Technical Architecture

The most straightforward way to convey the difference between technical architecture and enterprise architecture (EA) is by looking at the scope and focus of each.

As the name suggests, technical architects are more concerned with the technicalities and the specifics of a particular technology than with technology’s place in the enterprise.

That’s not to say that they operate without the enterprise’s overall strategy in mind. They do – or should ­– but the direction for said strategy typically comes from elsewhere. Most significantly – from an enterprise architect.

In this post:

Enterprise architecture review

What Is a Technical Architect?

A technical architect works closely with development teams in a supervisory capacity – providing leadership and guidance during the project lifecycle.

They often work under more specific titles, reflecting the technology they specialize in – e.g., “Java architect” or “Python architect.”

Typically, their operations revolve around technical services and in the context of the project lifecycle – enabling technology.

It’s a role that requires good communication and relationship management skills, as well as the ability to anticipate problems, manage their time, and operate under pressure.

The Difference Between Technical Architecture and Enterprise Architecture

We previously have discussed the difference between data architecture and EA plus the difference between solutions architecture and EA.

In the latter, we described enterprise architects as having a “holistic view” of the organization, mostly focusing on things from a high-level, strategic point of view.

Although technology is something that enterprise architects are concerned with, they aren’t expected to have a deep, ground-level understanding of the tech itself.

Their broad scope and holistic view of the enterprise does not allow for this.

Technical architecture can be seen as the antithesis to this. Instead of seeing the organization from a high-level, strategic point of view, technical architects operate amongst the weeds.

They have a high focus on technology, and a low focus on how that technology fits in with the enterprise’s overall strategy.

Using an orchestra as an analogy, enterprise architects would assume the role of a conductor who is less concerned with how any individual instrument operates but requires they work together cohesively to complete the performance.

While enterprise architects have a high strategy focus, they are less detail orientated., Technical architects operate in the reverse, with solution architects somewhere in the middle.

Enterprise Architecture vs. Technical Architecture – Which One Do I Need?

Well? … Both.

It’s a straightforward and somewhat vague answer. But it’s one that needs to be given, because we’re asking the wrong question.

In a world where organizations are increasingly data-driven, any ambition to scale will inevitably scale with the complexities of the systems involved too.

With this considered, we should abandon the binary question, in favor of asking “when”  instead of “which.”

When do I need enterprise architecture? When do I need technical architecture?

The answer to both is “sooner, rather than later.”

With regard to enterprise architecture, many organizations  already are doing enterprise architecture before they have a formally recognized enterprise architecture initiative or enterprise architecture tool.

We consider these efforts “low-maturity” enterprise architecture.

But doing enterprise architecture this way can cause bottlenecks as an organization’s enterprise architecture scales. Additionally, this sort of enterprise architecture is vulnerable in its dependency on individuals.

Without a formalized approach to enterprise architecture, the whole enterprise architecture could collapse if the person in charge of the EA were to leave without contingency plans in place.

You may already be at and the point of experiencing bottlenecks. If so, you should consider formalizing your enterprise architecture approach and investing in an enterprise architecture management suite (EAMS) now.

This 2020 Forrester Report on EAMS is a great place to start your research.

The need for technical architecture implies an organization already has a complex enterprise architecture and some degree of formalization for managing it.

This, in turn, implies an organization is large and therefore has more complicated product lifecycles and higher stakes implementation procedures for new tech.

In either case, managing an organization’s enterprise architecture through manual processes and repurposed systems – some organizations attempt to make do with managing their EA via PowerPoint slides and sticky notes – is not the recommended approach.

For a scalable, manageable and efficient approach to enterprise architecture, organizations should adopt a dedicated enterprise architecture tool.

erwin Evolve is one such tool that also supports business process modeling use cases.

erwin Evolve has collaborative features at its core, meaning organizational silos that had once kept EA in an ivory tower are broken down, and the holistic view of enterprise architects is enhanced.

With erwin Evolve, users enjoy the following benefits:

  • Creation and visualization of complex models
  • Powerful analytic tools to better understand an organization’s architecture – such as impact analysis
  • Documentation and knowledge retention for better business continuity
  • Democratization of the decision-making process
  • Greater agility and efficiency in implementations and dealing with disruption
  • Lower risks and costs driven by an enhanced ability to identify redundant systems and processes

Organizations can try erwin Evolve for free and keep any content you produce should you decide to buy.

For more information on enterprise architecture, click here to get the erwin experts’ definitive guide to enterprise architecture – 100% free of charge.

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Data-Driven Enterprise Architecture

It’s time to consider data-driven enterprise architecture.

The traditional approach to enterprise architecture – the analysis, design, planning and implementation of IT capabilities for the successful execution of enterprise strategy – seems to be missing something … data.

I’m not saying that enterprise architects only worry about business structure and high-level processes without regard for business needs, information requirements, data processes, and technology changes necessary to execute strategy.

But I am saying that enterprise architects should look at data, technology and strategy as a whole to develop perspectives in line with all enterprise requirements.

That’s right. When it comes to technology and governance strategies, policies and standards, data should be at the center.

Strategic Building Blocks for Data-Driven EA

The typical notion is that enterprise architects and data (and metadata) architects are in opposite corners. Therefore, most frameworks fail to address the distance.

At Avydium, we believe there’s an important middle ground where different architecture disciplines coexist, including enterprise, solution, application, data, metadata and technical architectures. This is what we call the Mezzo.

Avydium Compass Mezzo view
Figure 1 – The Avydium Compass™ Mezzo view

So we created a set of methods, frameworks and reference architectures that address all these different disciplines, strata and domains. We treat them as a set of deeply connected components, objects, concepts and principles that guide a holistic approach to vision, strategy, solutioning and implementations for clients.

For us at Avydium, we see the layers of this large and complex architecture continuum as a set of building blocks that need to work together – each supporting the others.

Avydium Compass view of enterprise architecture
Figure 2 – The Avydium Compass® view of enterprise architecture

For instance, you can’t develop a proper enterprise strategy without implementing a proper governance strategy, and you can’t have an application strategy without first building your data and metadata strategies. And they all need to support your infrastructure and technology strategies.

Where do these layers connect? With governance, which sets its fundamental components firmly on data, metadata and infrastructure. For any enterprise to make the leap from being a reactive organization to a true leader in its space, it must focus on data as the driver of that transformation.

DATA-DRIVEN BUSINESS TRANSFORMATION – USING DATA AS A STRATEGIC ASSET AND TRANSFORMATIONAL TOOL TO SUCCEED IN THE DIGITAL AGE

 

Data-Driven Enterprise Architecture and Cloud Migration

Let’s look at the example of cloud migration, which most enterprises see as a way to shorten development cycles, scale at demand, and reduce operational expenses. But as cloud migrations become more prevalent, we’re seeing more application modernization efforts fail, which should concern all of us in enterprise architecture.

The most common cause for these failures is disregarding data and metadata, omitting these catalogs from inventory efforts, part of application rationalization and portfolio consolidation that must occur prior to any application being migrated to the cloud.

Thus, key steps of application migration planning, such as data preparation, master data management and reference data management, end up being ignored with disastrous and costly ramifications. Applications fail to work together, data is integrated incorrectly causing massive duplication, and worse.

At Avydium, our data-driven enterprise architecture approach puts data and metadata at the center of cloud migration or any application modernization or digital transformation effort. That’s because we want to understand – and help clients understand – important nuances only visible at the data level, such as compliance and privacy/security risks (remember GDPR?). You want to be proactive in identifying potential issues with sensitive data so you can plan accordingly.

The one piece of advice we give most often to our clients contemplating a move to the cloud – or any application modernization effort for that matter – is take a long hard look at their applications and the associated data.

Start by understanding your business requirements and then determine your technology capabilities so you can balance the two. Then look at your data to ensure you understand what you have, where it is, how it is used and by whom. Only with answers to these questions can you plan and executive a successful move to the cloud.

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Data Driven Enterprise Architecture for Delivering Better Business Outcomes

Forget everything that you may have heard or read about enterprise architecture.

It does not have to take too long or cost too much. The problems are not with the concept of enterprise architecture, but with how it has been taught, applied and executed. All too often, enterprise architecture has been executed by IT groups for IT groups, and has involved the idea that everything in the current state has to be drawn and modeled before you can start to derive value. This approach has caused wasted effort, taken too long to show results, and provide insufficient added value to the organization.

In short, for many organizations, this has led to erosion in the perceived value of enterprise architecture. For others, it has led to the breakup of enterprise architecture groups, with separate management of the constituent parts– business architecture, information architecture, solutions architecture, technical architecture and in some cases, security architecture.

Data Driven Enterprise Architecture for Business Outcomes

This has led to fragmentation of architecture, duplication, and potential sub-optimization of processes, systems and information. Taking a business outcome driven approach has led to renewed interest in the value enterprise architecture can bring.

But such interest will only remain if enterprise architecture groups remember that effective architecture is about enabling smarter decisions.

Enabling management to make those decisions more quickly, by having access to the right information, in the right format, at the right time.

Of course, focusing on future state first (desired business outcome), helps to reduce the scope of current state analysis and speed up the delivery of value. This increases perceived value, while reducing organizational resistance to architecture.

  • Understand their goals, objectives and pain points, and then help them to express them in clear business outcome related terms. This will take time and skill, as many business users simply asking for system changes without clearly stating their actual objectives.
  • Review your current architecture efforts and tooling. Question whether you are providing or managing data the business does not need, whether you are working too deeply in areas that may not be adding value, or whether you have your vital architecture data spread across too many disconnected tools.

This blog post is an extract taken from Enterprise Architecture and Data Modeling – Practical steps to collect, connect and share your enterprise data for better business outcomes. Download the full ebook, for free, below.