There has been a lot of consolidation in the process/architecture space in the last few years, mostly driven by PE firms. But why is that so, and why does it seem that there is no alternative to this business model?
Back in the day there were foundations behind the companies, or they were privately held, and the only thing (besides a few smaller players you might not even have heard of) that I see are some open-source projects in the automation space … mostly driven by the decision to go closed-source by Camunda.

One of these projects is SpiffWorks, and we invited the CEO of the company behind it, Dan Funk, to our little show. He is an expert in identifying organizational and technological patterns, using visualizations and written communication to build consensus around technical directions. Dan is committed to aligning technology initiatives with business objectives, fostering a culture of continuous improvement, and mentoring engineering talent.

Dan is also a thought leader and co-authored numerous publications as the technical lead for a web-based research application promoting healthier patterns of thinking using interpretation bias training. In addition to this, Dan is the co-founder of the Makerplace in Staunton, VA, where he established a makerspace offering low-cost access to state-of-the-art electronics tools, laser cutters, CNC machines, a pottery studio, and woodworking equipment.

In this episode of the podcast, we talk about:

• Dan's background
• Open-source projects require community support to thrive—SpiffWorks aims to bridge the gap between business and technical teams; Python is chosen for its readability and ease of use in process automation.
• Building a sustainable open-source project involves finding a viable business model, and community engagement is crucial for the success of open-source initiatives.
• Open-source software is foundational to modern technology infrastructure.
• The future of process automation lies in making technology accessible to non-technical users.
• Effective communication is key to resolving conflicts between business and technical teams.
• The open-source model can be compared to a city with shared infrastructure. Support for open-source projects can (and should) come from larger companies benefiting from them.

You can take a look at Spiff Works at https://spiff.works/ and reach out to Dan via LinkedIn: https://www.linkedin.com/in/funkdan/.

Please reach out to us by either sending an email to hello@whatsyourbaseline.com or signing up for our newsletter and getting informed when we publish new episodes here: https://www.whatsyourbaseline.com/subscribe/.

To implement AI (and processes) correctly, you need good data. But what does that mean? Well, firstly it means that you can define your data product and to achieve that you need good data governance.

But are we now in a super-nerdy topic? No, this is what we all do in some form or another … but in different fidelities and maturities.

To shed some light on the topic of data governance, we invited Angelika Rinck for this episode. She started her career studying public administration and then served in the German federal police before switching to the regular industry (in the aerospace industry, and while that might not be enough, she studied economics in parallel).

Somehow she found her way into consulting and is working now in digitalization and IT projects. Her main focus here is product lifecycle management and data governance.

In this episode of the podcast, we talk about:

• Angelika’s career journey: from e-commerce working student in Hamburg to aerospace, engineering, and ultimately major IT and data governance initiatives.
• Her first agile project—complete with a physical Kanban box—sparked her love for IT project management and structured delivery.
• A detour into underwater orienteering reveals surprising parallels to data work: precision, navigation, and making decisions in the dark.
• Defining data governance: the framework of rules, processes, and responsibilities that guide how organizations create, use, secure, and improve data.
• Why it matters: Governance drives clarity, accountability, and value creation—not just control or compliance.
• Understanding the difference between data governance (framework and value creation) and data management (the operational “doing”).
• A common failure pattern: organizations naming “business data stewards” without training, tooling, or understanding the expectations.
• Governance only works when decentralized experts feed real issues into a central team—not when policies are pushed top-down in isolation.
• Data products demystified: they’re the outcome of well-governed data—reusable, high-value information assets that improve processes, decisions, speed, or cost.
• Real examples: using historical field data instead of simulation data to accelerate engineering calculations or using decades of bird-flight video to predict weather with AI.
• Risks of bad data with AI: incorrect system guidance, support tickets exploding, contradictions between outdated documents, and misplaced trust in “the easy button.”
• Governance foundations: critical data identification, metadata transparency, ownership, RASCI clarification, and understanding who creates, changes, and consumes data.
• The messy reality: access rights often don’t match process needs—leading to shortcuts, bypasses, and unintended process redesign opportunities.
• Final takeaway: data governance isn’t bureaucracy—it's a structured path to value, clarity, and safer AI adoption, but it requires real effort, definitions, ownership, and cultural change.

You can reach Angelika on LinkedIn here: https://www.linkedin.com/in/angelika-rinck-b93a7019b/.

Please reach out to us by either sending an email to hello@whatsyourbaseline.com or signing up for our newsletter and getting informed when we publish new episodes here: https://www.whatsyourbaseline.com/subscribe/.

In this episode, we welcome Finnish enterprise architect and author Eetu Niemi to explore what it means to make enterprise architecture (EA) “lightweight”—practical, collaborative, and relevant in the real world. From frameworks to fiction writing, from ivory towers to coffee-fueled collaboration, this conversation dives into how to make EA actually work for organizations.

With over 16 years of experience in architecture consulting at CGI, Coala, and Accenture, Eetu has guided more than 45 private and public organizations in transforming their business and IT landscapes. He specializes in enterprise and solution architecture, helping organizations align technology with strategy, improve EA practices and tools, and strengthen information security.

A published author and recognized thought leader, he wrote the first EA book in Finnish and two bestsellers on IT consulting and frequently shares insights through blogs, newsletters, and speaking engagements. Holding a PhD in enterprise architecture benefit realization and an MSc (Econ.), his cross-industry work spans finance, telecom, manufacturing, and the public sector—delivering results in EA modeling, governance, and tool implementation with platforms such as BiZZdesign, Ardoq, and Sparx EA.

In this episode of the podcast, we talk about:

• Eetu’s background — Author, architect, and advocate for democratizing enterprise architecture so it’s accessible beyond the ivory tower.
• Rethinking EA’s relevance — Success comes when EA shifts from being “nice diagrams” to being indispensable guidance that helps organizations plan, adapt, and reduce risk.
• Defining “lightweight EA” — It’s all about communication and cooperation, using models as tools for dialogue, not as ends in themselves.
• Avoiding EA’s common traps — Filling every box in a framework or modeling everything down to cables and servers misses the point. EA should focus on solving real business problems.
• Where to draw the line — Model at the logical level (applications, processes, data) — not every internal detail. EA is the layer above IT and process modeling, not a replacement for them.
• Kickstarting EA right — Start small, plan with stakeholders, and document goals and methods early. Collaboration beats over-engineering every time.
• Who to talk to first — Don’t wait for the C-suite; start where you have access, build trust, and work your way upward.
• Quick wins matter — Focus on tangible outcomes like system maps for upcoming projects — those early wins open doors and earn invitations “to the next party.”
• Light tools for light EA — Begin with approachable modeling tools instead of overcomplicated platforms. Save the big systems for when you truly need them.
• Governance without the grind — Keep EA blueprints current but concise. A handful of well-maintained diagrams is better than hundreds of forgotten files.
• Collaboration is key — EA succeeds through engagement: creating models with people, validating them with people, and helping those people make better decisions.
• Selling the value — Show how EA helps others succeed — whether that’s IT planning, compliance, or transformation — and you’ll overcome “I have no time for this” resistance.
• EA + AI = opportunity — Complexity is growing, not shrinking. AI can help classify, visualize, and assist — but architects still provide the judgment and storytelling.
• Making EA stick — Keep the practice alive through persistence and visibility. Even when budgets tighten, lightweight EA thrives by staying practical, connected, and useful.

You can reach Eetu on LinkedIn here: https://www.linkedin.com/in/eetuniemiphd/

Please reach out to us by either sending an email to hello@whatsyourbaseline.com or signing up for our newsletter and getting informed when we publish new episodes here: https://www.whatsyourbaseline.com/subscribe/.