The Work Is the Objective: What I Bring to the Table
I help organizations understand how people, information, technology, infrastructure, and data fit together so they can put computers to work for people and accelerate the work they exist to do.
The Work Is the Objective: What I Bring to the Table
Why my path from the farm to signals intelligence, information technology, infrastructure, and data science shapes how I advise organizations today.
I grew up on a dairy farm near Pickford, Michigan. Looking back, I think that is where I began developing the way I approach technology, organizations, and complex systems.
On a farm, everything is connected. Equipment, electricity, weather, animals, crops, time, money, and people all have to work together. When something breaks, the problem is rarely confined to one piece of equipment. You have to understand how the failure affects everything around it, determine what matters most, and decide what needs to happen next because the work still has to get done.
That experience taught me something I have carried throughout my career: technology is valuable only when it helps accomplish the work.
My career has taken me through electrical engineering, computer engineering, signals intelligence, information technology, networking, organizational leadership, and data science. On paper, those may look like different disciplines. I see them as interconnected layers of the same system.
I began college at Michigan Technological University, where I spent three terms studying electrical engineering. I later transferred to Lake Superior State University and spent three semesters studying computer engineering. My path then took me into the United States Air Force, where I served for four years as a signals intelligence analyst.
That experience fundamentally shaped how I think about information.
Each day, I analyzed, identified, decrypted, interpreted, and reported highly sensitive information. The technology mattered, but the real work was turning signals into information that someone else could understand, trust, and use. The work demanded accuracy, security, context, provenance, and judgment because the information could inform consequential decisions.
Years later, I recognized the same pattern in information technology and data science: collect something, understand what it represents, connect it to other information, establish confidence in it, and turn it into something useful.
Learning the System from the Ground Up
When I began working at the Eastern Upper Peninsula Intermediate School District, I started as a computer client technician. I was close to the users and close to their problems. A computer that would not work was not simply a technical problem. For a teacher or student, it was an interruption to whatever they were trying to accomplish.
I later became a network engineer, and my perspective had to expand. Instead of thinking primarily about individual computers, I became responsible for regional infrastructure connecting nearly 6,500 students to the systems and resources they needed for learning. The question was no longer simply whether a device worked. It was whether the network worked, whether the systems behind it worked, whether they were secure and resilient, and whether thousands of people could depend on them.
When I transitioned into the IT director role, my perspective expanded again. I became responsible for the broader IT services supporting K-12 school districts across three counties. At that level, technology was no longer primarily about servers, switches, computers, applications, or circuits. It was about people, budgets, organizations, vendors, policies, cybersecurity, infrastructure, planning, and ultimately the mission of the schools we supported.
I eventually completed a bachelor’s degree in information technology and a master’s degree in data science. Data science added another layer to my experience. It gave me formal tools for understanding how organizations can use data, statistics, computation, modeling, and machine learning to learn from the information they already generate.
But I would not describe myself as someone who simply “does data science.” What I bring to an organization is broader than that.
Seeing the Whole System
I understand enough of the technology stack to see how its layers fit together. I understand the physical infrastructure beneath digital systems. I understand networks and communications, computing infrastructure, information systems, data, cybersecurity, and the analytical and computational capabilities built on top of them. I also understand that the boundary between information technology and operational technology is becoming increasingly important as buildings, energy systems, communications, automation, sensors, and computing converge.
Because of my background in data science, I also understand what becomes possible when those layers are connected intentionally.
An organization may want to build a predictive model, deploy an artificial-intelligence application, create a knowledge system, automate a process, or give employees better access to organizational information. Those capabilities do not begin with AI. Underneath them are networks, computing, storage, identities, applications, databases, APIs, data pipelines, security controls, governance, and people.
A model has to be trained somewhere. Data has to come from somewhere. It has to move across something and be stored somewhere. Someone has to determine whether it is accurate, appropriate, secure, and trustworthy. The model has to run somewhere, and an application has to place its capabilities into the hands of the people who need them.
Ultimately, the result must save someone time, improve a decision, reduce risk, create new capacity, or help the organization accomplish its mission.
That last part is what matters to me.
Putting Computers to Work for People
I am not interested in technology for technology’s sake. I am interested in putting computers to work for people.
That might mean using automation to eliminate repetitive work that consumes staff time every year. It might mean connecting information that currently exists in separate systems so employees do not spend their days searching for it. It could mean using data science to identify patterns that would otherwise remain invisible or building an AI application on top of trusted organizational knowledge so employees can ask questions instead of hunting through folders, email, spreadsheets, and databases.
Sometimes it means something more fundamental: improving the infrastructure beneath those capabilities so the organization can reliably and securely do what it is already trying to do.
That is where I believe I bring unusual value as an advisor. I can work across layers that are often separated inside organizations. I can talk with leadership about mission, strategy, risk, workforce, investment, and organizational capacity. I can talk with IT and operational teams about networks, systems, cybersecurity, integration, architecture, automation, and infrastructure. I can talk with analysts and data teams about data quality, statistics, models, knowledge graphs, information architecture, and provenance.
I can also talk with the people doing the actual work and begin with perhaps the most important question:
What are you trying to accomplish?
From there, I can help the organization work backward. What information do we need? What do we already have? Where does it live? Can we trust it? How do the systems connect? What infrastructure is required? What can we automate? Where could data science or AI genuinely help? What should remain a human decision? What is the simplest architecture that can move the organization from where it is today to where it needs to go?
Understanding the Business First
That is fundamentally how I see advisory work.
I am not coming into an organization with a technology looking for somewhere to install it. I want to understand the business of the organization first.
If you manufacture something, the business is manufacturing. If you run a hospital, the business is caring for people. If you operate a school district, the business is teaching and learning. If you are a utility, the business is delivering reliable services. If you are a nonprofit, the business is accomplishing the mission that brought the organization into existence.
Technology, information, data, automation, infrastructure, and artificial intelligence are supporting systems around that mission. My role is to understand those systems well enough to determine where they can remove friction, increase capacity, reveal useful knowledge, reduce risk, and accelerate the work the organization already exists to do.
In some ways, that brings me back to the farm.
A farmer does not buy a tractor because owning a tractor is the objective. The tractor exists because there is work to be done. You choose the equipment, understand its capabilities, maintain it, and put it to work where it creates the greatest value.
Organizations should approach technology the same way.
The tools available to organizations are becoming extraordinarily powerful. Data science, distributed computing, cloud infrastructure, automation, machine learning, and artificial intelligence give even relatively small organizations capabilities that would have been unimaginable not long ago.
But capability alone does not create value.
Someone still has to understand the work. Someone still has to connect the pieces. Someone still has to ask the right questions. And someone has to be able to move from the mission of the organization, through the information and technology architecture beneath it, and back to the people who ultimately have to use it.
I help organizations understand how people, information, technology, infrastructure, and data fit together so we can put computers to work for people and accelerate the work the organization actually exists to do.
The technology is not the objective. The work is the objective. That is what I bring to the table.