The Seagram Building is not just iconic – it rewrote the rules of office architecture. New York City is full of iconic buildings, yet only a handful, like the Seagram Building, have been so widely copied. Its unique form, bronze-and-glass skin, and open plaza set a new standard, one that would be echoed in corporate towers across the world. Its designer, Ludwig Mies van der Rohe, a pioneer of modern architecture, taught and designed the campus at the Illinois Institute of Technology from 1938 to 1958 and is also an original designer of the Martin Luther King Jr. Memorial Library in Washington, D.C.
For Mies, architecture was more than construction, it expressed the spirit of an era. His buildings were designed for thought, movement, and life, not just for admiration. For him, minimalism was a philosophy: remove the unnecessary and let the essential speak. Mies emerged from Germany’s Bauhaus School, whose principle, “form follows function”, guided modern design. The Bauhaus sought to unify art, craft, and industry, emphasizing spatial efficiency and logical planning. Despite its brief existence in Germany (1919–1933), the movement profoundly shaped twentieth-century visual culture. Mies later exerted a lasting influence on U.S. architecture, and his principle “less is more” remains foundational to minimalist design.
Another significant influence on minimalist architecture comes from traditional Japanese design. For centuries, Japanese architecture has been characterized by simplicity, clean lines, and a close relationship with nature.
“ Japanese minimalism, shaped by Zen, treats restraint as a virtue, using light, texture, and space to elevate the ordinary. ”
With a preference for aesthetics and simple objects, traditional Japanese design is considered the perfect source of inspiration for minimalism.
This was an effort to direct your focus to the fact that minimalism is the key to simplicity. The idea of minimalism is careful understanding and removal of the unnecessary. Consequently, only the necessary elements are used, each of which should serve a specific purpose. This approach reduces decorations, resulting in clear, uncluttered designs. It’s about letting go of excess and embracing a simpler life so we can focus on what actually matters – the essential. More meaning, less noise.
Early in my IT career, my team struggled for months with a tricky commission-and-bonus calculation issue that had been missed in the requirements. I solved it faster than expected, surprising many. Looking back, I see that diving into the details while keeping my thinking simple revealed a solution far less complicated than we assumed, where math, business logic, programming, and common sense came together. Complexity gave way to simplicity. Years later, a critical application, the fraud detection engine, suddenly stopped working. The complex, rule-based system flagged fraudulent payments, and once again, business and tech teams blamed each other. What helped was listening to a subject matter expert, reviewing recent payment data, query plans, and processing costs. By focusing on essentials, questioning assumptions, and keeping things simple, we resolved the issue efficiently. I’ve seen many moments like this throughout my career. Recently, one really stood out to me when an executive director shared how much she connected with my belief in keeping things simple. She asked me to help build a clear, workable process to bring data sources for an enterprise data lake into Governance.
Steve Jobs always sought a consistent design language for his products. The Apple designers were fascinated by the idea of distilling that complex function down to something that appears simple and user-friendly. Jobs described Apple’s design principle as influenced by the Bauhaus. Jonathan Ive, Apple’s product designer, once said, “We wanted to get rid of anything other than what was absolutely essential.” Job’s main demand was “Simplify!
“ Simplicity is about perfecting the process. ”
Data Governance is complex because it’s not just about standing up technology capabilities; it involves many moving parts. Data lives in hundreds of systems, teams work in silos, and change can meet resistance. On top of that, people’s buy-in, skills, and the need to balance competing priorities are often overlooked. When you step back, it becomes clear that Data Governance is as much about people as it is about data.
I don’t see a better path to real adoption of Data and AI Governance without embracing simplicity. Adoption is where most efforts fail. Connecting hundreds of systems, harvesting massive amounts of metadata, and managing quality can become expensive and overly complex, with little focus on real value. Tools are hard to use, processes are confusing, and even basic searches on Data Catalog and discovery feel difficult.
“ Simplicity isn’t optional; it’s essential for Data and AI Governance. ”
For example, tracing data lineage from provisioning to authoritative and originating sources often appears complex and costly, yet it should follow minimalist principles focused on what is essential to show on the lineage. Studies show that most organizations struggle to keep business glossaries accurate and data catalogs up to date, making effective Data Governance a persistent challenge. Despite frequent promising sales pitches for Data Governance tools, few really address real use cases and offer a seamless technological experience; even those enhanced with AI often overpromise without delivering practical value. Invest in Data Governance that works, not just in tools that sit idle like that in any art museum.
The in-house solutions companies proudly talk about often feel like a vintage IT roadshow that doesn’t focus on the user experience. Data classification still relies on rudimentary business rules, and unstructured data is treated as chaotic and too complex to handle. Few realize how costly it is to ignore unstructured data in governance efforts.
This is my dilemma as a practitioner. If we’re still stuck dealing with old tech debt, when would we get a chance to focus on emerging trends like dealing with AI Governance? When will we start paying attention to what the business really needs from us in Data and AI Governance? When will we move beyond static catalogs, data dictionaries, and glossaries that add little value? And why do our technical solutions often feel so soulless and lifeless? When will we focus on doing less but delivering more?
I’m still baffled by how long “Minimal Viable Product” (MVP) has stuck around. Seriously, who gave it celebrity status? Every IT program manager talks about MVPs, yet it’s often misunderstood. Many think it means the simplest product, tied to “simplicity.” Too often, teams release an MVP only to be told to build something else without learning or improving, leaving behind technical debt. In trying to deliver an MVP, we sometimes achieve the opposite. MVP has become a corporate ritual: build it, ignore it, call it learning. Hit repeat.
According to Market Growth Reports, companies devoted to providing user experience tools and services were worth $548.91 million in 2023 and are expected to reach $1.36 billion by 2029. Simplicity doesn’t just help users feel better; it drives effortless action. The rise of AI with ML puts the future of user experience in our hands.
For me, simplicity is part of who I am and my 1lessclick® approach. I strive to design processes, tools, and solutions that are usable, efficient, accessible, and elegant. Minimalism drives intuitive, elegant solutions for our clients, built with balance, harmony, and clarity at the core. True simplicity means creating experiences that feel like a natural extension of yourself, making them effortless for others. My goal is to deliver clients a satisfying, simplified user experience while building sophisticated engineering solutions. I aim to bring the art of simplicity to the 1lessclick® Experience, designing solutions where “doing less and getting more” is blended with the Bauhaus principle “form follows function”.
Our clients and stakeholders recognize our integrity, feel at ease, and willingly engage in meaningful dialogue that lays the cornerstone for effective Data and AI Governance. In a fast-paced world where sensitivity is high and trust is often low, we remain grounded and sincere, building friendships, partnerships, and lasting connections.
