I had a great chat with Nick Valiotti. Nick built his fractional CDO practice around one bet: growing companies need senior data leadership long before most of them can afford it full time.
He wrote the playbook down in his book Your Fractional CDO, after watching the same failure pattern repeat across years of client work.
A data team builds perfectly modeled tables, clean naming conventions, a finished warehouse, and still fails at the one job that matters.
The failure starts in the hiring order, months before anyone touches SQL.
The Pattern Behind Almost Every Broken Data Team
A company grows, raises money or bootstraps itself, and builds product and marketing first. Data waits. By the time anyone notices, the gaps have already turned into blind spots in real decisions.
The fix usually starts with a data analyst, hired to both analyze the data and build the warehouse underneath it. He patches things together with whatever is closest: ad hoc imports from spreadsheets, quick pipelines wired through whatever tool is available. It works until it doesn’t.
So the company hires a data engineer to rebuild the plumbing properly. Pipelines get rebuilt, structure improves, the technical layer finally looks right.
It still doesn’t work. Nobody ever agreed on what the numbers mean.
A clean warehouse built on an unresolved disagreement only makes the disagreement faster.
This is the gap a fractional Chief Data Officer gets hired to close. The job is the translation layer between what the business is trying to decide and what the data team is building. Head of Data, VP of Data, Chief Data Officer, the title changes. The job underneath stays the same: turn business priorities into a data roadmap, then close the distance between the two.
Three People, Three Numbers, One Metric
When the role gets done right, the first move rarely touches the warehouse.
On one project, the opening round of conversations with C-level stakeholders surfaced a single metric, active subscriber, calculated three different ways by three different executives. Each one trusted their own version. Nobody had flagged it as a problem, because nobody had compared notes.
The fix starts with a written catalog of every metric that matters: what it counts, what it excludes, who owns the definition. It lives somewhere simple, a Notion page, an Obsidian vault, plain markdown files, and every stakeholder signs off on a definition before a single pipeline gets touched.
Skip that step and the technical work inherits the argument anyway. A warehouse with perfect naming conventions and a properly modeled transformation layer still produces three different numbers for active subscriber, because the disagreement never lived in the tables. It lived in the room full of people who never sat down to agree on what the word meant.
Your career is not stuck because you lack technical skills.
It is stuck because nobody taught you how to operate. Stakeholder management. Business translation. Career positioning. I write about all of it every week
What AI Changed
The Audit That Used To Take A Month
When Nick onboarded onto a new client’s stack, he inherited a Metabase instance holding around 2,000 saved questions, each one a SQL query sitting on top of the warehouse. Reading through that by hand to figure out which tables and metrics mattered used to eat him three to four weeks.
He connected an LLM to Metabase’s API and turned the same task into a question-asking exercise: which tables get used most, which metrics get calculated more than one way. The 2,000 saved queries referenced around 90 tables in the warehouse. Only 12 of them turned out to matter.
The same pass produced a first list of conflicting metric definitions, the kind of list that used to take weeks of stakeholder interviews to assemble by hand. It became the opening line of the metric catalog conversation. The conversation with stakeholders still had to happen.
A Personal Operating System
Outside of any single audit, Nick’s daily workflow runs through a stack of connected tools: email, Slack, Telegram, WhatsApp, Jira, Google Calendar, a meeting note-taker, a task planner, a personal fitness tracker. Each one is wired in through its own API, feeding into folders organized by client, by teammate, by personal project, written up as markdown files an AI assistant reads for context.
A new lead from the website used to mean reading an email and booking a call. Research on the prospect now happens automatically, the call transcript gets analyzed for next steps, and a draft proposal and CRM update follow without him typing either one by hand.
He still types every prompt himself. Roughly 90 percent of his working day still happens inside an editor, reading something, asking a question about it, deciding what happens next.
The Rules That Don’t Bend
Speed doesn’t extend to trust. A short list of rules stays fixed regardless of how good the tools get:
Every SQL statement an AI writes gets verified by a human before it touches anything live or anything tied to budget.
Raw client data never goes into a public AI tool. Only metadata about the data does, table names and query structure, never the rows underneath.
Sensitive processing happens through local models inside the client’s own environment instead of commercial platforms.
Delivery got faster. The job grew, because speed opens room to take on more work.
Judgment Became The Scarce Skill
As AI absorbs more of the execution, headcount on this kind of team is shrinking on purpose, driven by a bet: enable each remaining data analyst and engineer with AI rather than hiring more people to do routine work that AI now does faster, the documentation nobody wanted to write, the boilerplate code, the first draft of a pipeline.
Not everyone takes that bet. Some experienced data engineers distrust the output outright or keep working the old way out of habit.
That resistance is becoming the dividing line. Once anyone is able to write a working SQL query or stand up a dashboard with AI’s help, calculation stops being the skill that earns a senior title. Reasoning about why a stakeholder cares about a specific number takes its place. The valuable hire thinks strategically about the business and catches the cases where the fast answer is the wrong one.
I built the resource library I wish existed when I was 25 years old.
Career scripts. Business translation templates. Stakeholder playbooks. Meeting frameworks.
Every single one came from real situations, real mistakes, and real results. Paid members get the whole thing.
Reporting Lines Decide Whether The Job Works
Where this role sits on the org chart changes what it’s able to do, regardless of the title on the business card.
Put the data function inside engineering and it drifts toward whatever a chief technical officer recognizes as good work: pipelines, infrastructure, technical correctness, thinly connected to what the business is trying to decide.
Put it inside finance and a different bias shows up. Finance’s own questions get solved first simply because of who the team reports to, while marketing or product priorities wait their turn even when they matter more to the business right now.
Neither placement is wrong on paper. Both pick a winner without saying so.
The function works best as a peer to product, marketing, and finance rather than a report inside any one of them, sitting at whatever level makes the final call on what the company builds next: a CEO in most structures, whoever arbitrates between general managers in flatter ones. The job stays the same either way. Listen across every department, then build the data foundation that unblocks all of them instead of only the one holding the budget.
None of this requires the word “Chief” in the title. It requires someone willing to do the unglamorous work of getting people to agree on what a number means before anyone touches a pipeline, and the discipline to use AI as a way to do that work faster rather than as a reason to skip it.
Find Nick Online
You can find Nick on a number of places. I strongly recommend following Nick on LinkedIn and subscribing to his Substack.
Valiotti Data, the agency Nick runs as a fractional CDO for growing companies.
LinkedIn, where most of this thinking shows up first.
Substack, where Nick writes long-form content.
Your Fractional CDO, the book written for executives building a data function for the first time.
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Yordan
