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5 Must-Dos When (Re-)Starting Your EA Practice

Posted by Matthew Grant on October 5, 2022
5 Must-Dos When (Re-)Starting Your EA Practice

Starting or restarting your enterprise architecture practice is a golden opportunity to level up your data-driven strategies, increase efficiency, reduce costs, and create a more collaborative culture across your organization. But taking advantage of this opportunity calls more than simply adopting an EA solution and expecting quick results.

For a new EA strategy to deliver real business results, IT leaders and other key contributors must know where to prioritize and focus their efforts. That’s what LeanIX Chief Customer Officer, Christian Richter, explained at the LeanIX Connect Summit in Boston. According to Christian, there are five key “must-dos” to succeed in starting or restarting your EA practice:

  1. Embrace small data and learn to say "No."
  2. Integrate and automate while valuing the experience of your colleagues.
  3. Treat EA as a product, not a project.
  4. Start with a real business problem, rather than the most intellectually appealing problem.
  5. Recognize that EA is a team effort.

In this article, we’ll recap some of the most important insights from Christian’s talk and walk through each of these 5 must-dos in detail so you can apply them to your business.

Where does EA stand before a customer adopts a new EA solution?

To really understand the 5 must-dos for starting or restarting your enterprise architecture strategy, it helps to know where EA typically stands in organizations before they launch these new efforts. Christian outlined a few common traits he sees in client companies before they officially adopt LeanIX.

Sometimes, organizations already have a competing solution in place. Unfortunately, its complexity has often resulted in poor data quality and difficulty producing meaningful reports and insights.

Companies without an incumbent solution (about 70%) have their data dispersed across multiple Excel spreadsheets, Visio, or PowerPoint presentations. This means they have no single source of truth, no common language or taxonomies, version-control problems, and ad-hoc data gathering processes.

What they need is a centralized, intuitive solution and a smart EA implementation strategy to go with it. The 5 must-dos that reflect the advice Christian often shares with LeanIX customers to maximize success during the onboarding process and as part of his team's ongoing collaboration with clients.

The 5 Must-Dos 

#1: Embrace small data and learn to say "No"

Less is often more when it comes to the data in your enterprise architecture management tool. Many companies make the mistake of trying to use their EA tool as a repository for every data attribute and use case simply because of the visibility such a tool offers. 

This mistake, however, can lead to many of the same challenges companies faced when they first considered adopting a new EA approach.

Here’s what belongs in your EA platform:

  • Relevant, related metadata that helps drive business decisions
  • Data that describes the enterprise as a whole (in breadth, not depth)
  • References that help explore deep-dive data found in adjacent solutions
  • Content that empowers teams and individuals to achieve their own unique goals

Data that goes beyond this may be important, but it is better suited for other tools or platforms. For example, data about servers and instances more appropriately belongs in a CMDB database, while project resource planning updates belong in a PPM platform.

The point is: Be intentional about your EA solution, keep its purpose clear, and make sure it contains the right data (not ALL the data!).

Of course, this also means being prepared to say "no" to new use cases that don't fit your EA strategy. These guiding questions can help you decide what belongs and what doesn't:

  • Can this data be correlated with existing data in the inventory?
  • Is it okay if this data is exposed to everyone in the company?
  • Is there someone who can be accountable for data maintenance and quality?
  • Does this data fulfill a long-term information need?

As a rule of thumb, you should be able to answer “yes” to at least one of these questions (ideally more) for the data to belong in your EA platform.

#2: Integrate and automate while valuing the experience of your colleagues

Integrating LeanIX with your other software applications is a critical step in streamlining and automating data collection. That said, connecting sources to your solution doesn't mean you will suddenly have all the data you need.

Some data is easily discoverable via integration and automation, while other types require manual intervention. Specifically, they require finding and talking to the people within your organization who have the right information.

Readily discoverable data includes SaaS information (your applications, usage, and cost), software and hardware product models, interfaces and application connections, and technology lifecycles. Information concerning business capabilities, business criticality, strategic alignment, functional fit, ownership, and specific use cases all require discussions or interviews with business stakeholders to be meaningful in your EA platform.

For example, automation may reveal that you switched from one accounting software tool to another a few months prior, but without added human insight, you won’t have any context around why the decision was made.

#3: Treat EA as a product, not a project

Companies all too frequently view the implementation of a new EA strategy as a one-time project, rather than seeing EA as a product needing ongoing maintenance, improvement, and evolution. Viewing EA as a project typically results in a failure to secure continued resources and budget. It also reflects a static scope for EA that doesn’t account for emerging use cases or the need to build out the EA practice.

Envisioning your enterprise architecture practice as a product, on the other hand, establishes EA as a vital discipline within your organization — one that requires prioritization, transparency, and change management to support the cultural shift it will bring to the organization.

When enterprise architecture is implemented as a product, it establishes EA as a critical function with buy-in from business leaders. Since the purpose of a product is to meet the needs of customers, a product mindset helps prioritize  EA initiatives and goals. This in turn calls for regular reviews and retrospectives aimed at improving the EA product and all associated workflows.

Above all, it ensures that EA effectively and continuously serves the organization by both supporting and influencing the overarching business strategy.  

#4 Start with a real business problem, not the most intellectually appealing problem

It can be tempting to start or restart your enterprise architecture practice with lots of modeling, but that is not the best approach.

Yes, starting with a focus on unified product models or target architectures or data models that encompass the entire enterprise can have a real intellectual appeal. Nevertheless, that doesn’t mean you should start there.

Instead, when starting out you should prioritize activities that directly address a business problem your company is experiencing. Not only will this approach end up delivering immediate value, it will drive buy-in from leaders in your organization. Examples of real business problems include:

  • Accelerating post-merger integration
  • Identifying dependencies for an upcoming carve-out
  • Moving a target percentage of applications to the Cloud
  • Reducing costs by eliminating duplicate or unneeded applications

Ideally, you should start with one critical business problem and then build on your success.

#5: Recognize that EA is a team effort

When starting or restarting their enterprise architecture practice, many companies consider hiring an experienced team of EAs to execute their strategy. Unfortunately, finding and hiring EAs is easier said than done.

The good news is that the skills required to execute a solid EA strategy already exist in various roles across your organization.

Software engineers and data scientists have the needed technical skills. Consultants and strategy managers have the business acumen. Project and product managers have the organizational knowledge required for specific initiatives. And so on.

When you think about the skill sets EA depends on, it becomes clear why Christian refers to EA as "a team sport.” Still, while you do need a team, you don't necessarily have to hire one. Instead, you can leverage the experience, skills, and institutional knowledge of your existing team. In fact, their embeddedness often means they can quickly have a measurable impact. 

Putting it all together

Adopting an EA tool is only the first step to starting or restarting your enterprise architecture practice. Implementing — and then maintaining — a successful EA strategy requires emphasis on the right priorities, intentional data management, and a collaborative approach.

When you commit long term to the 5 must-dos Christian outlined in his talk, you can expect your ROI to grow and for EA to become a key contributor of business value for your company.

If you would like to watch Christian’s talk in its entirety, you can do so here. [Free registration required].

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