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Big Ideas! Big Results! - What do your customers see when they look for you online?

Big Ideas! Big Results!

As a business owner in today's digital age, the online presence of your company is more important than ever before. Your customers and clients are likely searching for you online before they even step foot in your store or office. That's why it's critical to put thought into how you want to represent your business across all online platforms - especially the central hub, your website.

In order to stand out and gain clients most effectively online, you need an easy to use and informative website that helps customers take action.

Andrew's mission is to help business owners refine their personal and company presence online to maximize visibility, establish authority in their industry, and attract the right leads. Andrew and Webfitters can assist with website design, content creation, relationship building, and overall marketing guidance tailored specifically to your needs.

Andrew Bressers

Valiotti Analytics

At Valiotti Analytics, I lead a team that builds powerful, modern analytics stacks for companies ranging from Fortune 500s to fast-growing startups across EdTech, FinTech, GameDev, eCom, and mobile.
We’ve delivered 40+ data-driven solutions that have:

* Cut infrastructure costs by up to 40%
* Accelerated reporting by 70%
* Boosted client revenue up to 25x

Our end-to-end services cover everything from data warehousing and pipeline engineering to BI dashboards and machine learning.

Tech Stack: AWS, GCP, Snowflake, dbt, Airflow, Kafka, ClickHouse, BigQuery, Redshift, Looker, Tableau, Power BI, and more.

What we do:
✓ Data Strategy
✓ Data Engineering
✓ Data Analytics
✓ Advanced Analytics 

Need to scale your data capabilities? Let’s build something powerful.

CEO & Founder

Founder & CEO, Valiotti Analytics | Better Data Analytics for Data-Driven Companies

🚀 Founded valiotti.com — the go-to data agency for fixing messy marketing analytics.
Top-rated on Upwork & Clutch. Trusted by growth-stage teams to build dashboards, clean up data chaos, and drive faster decisions. Partnered with AWS, Google Cloud, and Salesforce.

Ex-Head of Consumer Marketing at the largest eastern retail company.
I’ve been the one chasing targets with thousand-row tables, slow reports, and siloed data — I know the pain.

Since 2019, I’ve helped marketing and sales teams:
→ Scale revenue up to 25x
→ Save hundreds of hours on reporting
→ Get insights that actually drive action

Let’s talk: https://calendly.com/nick-valiotti

Nikolay Valiotti

Data engineering is never “done.”

If you think it is, you’re already behind.

Data stacks don’t age gracefully.
Pipelines break. Schemas drift. APIs get “versioned” into oblivion. One quiet column rename, and your executive dashboard goes dark five minutes before a board meeting.

That’s not incompetence. That’s actually normal. The mistake is treating data engineering like a one-off build instead of a living system.
The job isn’t to design something flawless and walk away. It is to stay close enough to catch problems before the business does.

Strong data teams understand this:
→ Failure is inevitable — speed of detection is the real metric
→ Models will drift — adaptability matters more than elegance
→ Requirements will change — velocity beats perfection
This is why “set it and forget it” always turns into “why does no one trust the data?”

If you want data you can rely on, you need three things:
1 — Monitoring that screams the moment something breaks
2 — Engineers who can trace, debug, and patch quickly
3 — A habit of revisiting data contracts when sources evolve

Data is infrastructure. And no serious business builds infrastructure once and never inspects it again.
—
♻️ Repost if you’ve ever been paged because a silent schema change wiped out a revenue dashboard.
Follow me Nick for more unfiltered takes on data strategy, engineering, and keeping systems alive long after launch.

Nick Valiotti, PhD

Fractional CDO for Scaling Tech Companies | Author of Your Fractional CDO | Founder @ Valiotti Analytics

Quick reality check:
If your data analyst feels like an IT intern, you’re actively destroying one of the most strategic roles in your business.

Analysts aren’t “report people.” They aren’t back-office technicians. They aren’t support staff for whatever broke today.

They are business operators.
And if they’re not sitting with marketing, product, and ops, you’re wasting their entire potential.

When analytics lives under IT, this is what you get:
→ Numbers with no story
→ Dashboards with no decisions
→ “Insights” nobody actually uses

Put an analyst in the room where decisions are made, and everything changes:

Marketing: “We’re launching a new campaign.”
Analyst: “Here’s what worked before, what failed, and where your budget will quietly bleed if you’re not careful.”

That’s the job. Not maintenance. Not admin.
IMPACT. 

If your analysts don’t understand the business, they won’t produce value.
And that’s a leadership failure — not an analytics one.

♻️ Repost if you’ve seen analytics trapped where they don’t belong.
→ Follow Nick Valiotti, PhD for hard truths on building data teams that actually move revenue.

One thing I have to say out loud:
Excel is incredible. And this is exactly the problem with it.

The better Excel works, the longer companies cling to it. The longer they cling, the harder it becomes to outgrow.

Every team hits the same phase:
→ one spreadsheet
→ turns into twelve
→ turns into forty
→ turns into a folder full of excels

At that point, no one knows what’s real anymore.
This isn’t a tooling issue. It’s infrastructure debt.
And it compounds quietly — until decisions start disagreeing with each other.

In my book, Your Fractional CDO, I break down why spreadsheets become dangerous at scale — and what to fix before dashboards, KPIs, and leadership conversations drift out of sync.

Because the answer isn’t “ditch Excel.”
It’s: stop using it as your entire data strategy.

What actually works instead:

1 — Centralize your data.
Stop exporting CSVs like confetti. One shared source—Sheets, a small warehouse, anything—is better than ten “perfect” reports.

2 — Assign ownership.
If everyone owns the data, no one does. Quality only scales when accountability exists.

3 — Automate the pipes, not the panic.
Connect systems directly. Excel is fine as a front end—if the plumbing behind it isn’t chaos.

4 — Measure less. Decide more.
Fifty KPIs don’t make you data-driven. A handful of metrics that guide real decisions do.

Excel belongs in modern data stacks. Just not at the center of them.
Tools don’t create clarity. Frameworks do.

Fix the foundation before it fails you. And grab Your Fractional CDO — unless you genuinely enjoy living one broken formula away from disaster → https://lnkd.in/d--GjMpr

First day at work in 2026. Fresh priorities. Same first request:
“I need a dashboard.”

Every analyst hears this. Regularly. Confidently. With zero additional context. Because 9 times out of 10, when a stakeholder says “I need a dashboard,” what they actually mean is:

“Something feels off and I don’t know why.”
The dashboard is just the physical form their confusion takes.

The moment you accept the request as is, you’ve already lost. You’re no longer solving a problem — you’re redecorating anxiety.

It usually goes like this: “Can we get a dashboard to track campaign performance?”

Sure. You build it. Then come the sequels:
→ “Can we slice it by region?”
→ “Actually, over time?”
→ “What if we try a different chart?”
→ “Can we add just one more KPI?” (famous last words)

Congrats. You’ve entered the Dashboard Cinematic Universe. This happens when analysts become BUILDERS instead of PARTNERS.

A good analyst doesn’t just execute. They investigate. They pause and ask the most dangerous question in analytics:

“What decision will this help you make?”

That one sentence ends most bad dashboards on the spot. Because sometimes the answer isn’t a dashboard at all. It’s:
– a short memo
– a mini-report
– or a five-minute conversation that saves 6 weeks of work

If you want to be seen as a partner, not a chart factory, this is the shift that matters.

And this is exactly why I built a simple dashboard discovery framework, the Dashboard Canvas.
No endless meetings. No vague requests. No dashboards that get ignored.
If you build dashboards for a living, it’ll save you hours → https://lnkd.in/djYMn3Fp

Repost it so others can use it too. Use it on your next project.

Hot take: “Natural language to SQL” won’t replace analysts.
Not now. Probably not ever.

For years, the pitch has been the same: “Let business users just ask questions in plain English.”
Sounds great — until you remember what actually makes analytics hard: thinking, framing the problem, deciding what’s worth measuring, understanding why a number moved before asking how to query it.

LLMs are good at translating language into syntax, but they’re bad at judgment. And we’ve already seen this pattern play out.

2025 was supposed to be “the year of AI agents.” Autonomous systems joining the workforce. Replacing chunks of human labor.
Instead, what we got was a reality check.

Agents struggled with multi-step reasoning, context, and basic real-world constraints. They worked well in narrow, text-based environments (like code and terminals) — and fell apart the moment tasks became open-ended, ambiguous, or required common sense. Even the people closest to the tech quietly admitted the hype got ahead of the substance. 

Natural language → SQL is the same story, just smaller.
Yes, the model can write a query.
No, it doesn’t know which question matters.
And it definitely doesn’t know which metric is misleading, incomplete, or politically explosive inside your org.

Business users don’t actually want to “talk to data.” They want answers — and they want someone else to decide what the right question is.
When you remove SQL, you don’t automate analytics, you expose how rare analytical thinking really is.

To be clear: this isn’t anti-AI.
We should and will continue use AI in 2026 to:
→ draft queries
→ explore data faster
→ automate repetitive work
→ reduce the cost of iteration

What we shouldn’t do is pretend AI agents are about to wipe out data teams.
That requires:
 – more reliable reasoning
 – better context understanding
 – cleaner data foundations
 – and a real mindset shift inside organizations

This isn’t the year of the agent.
It’s the decade — at best.

So LLMs won’t kill analysts. They’ll kill lazy thinking and the illusion that SQL was the job.
And honestly? That’s a good thing.

→ Follow me Nick for no-BS analytics takes that separate real thinking from AI hype.
♻️ Repost if you’ve ever seen a “natural language to SQL” demo and thought: cool toy — wrong problem.

I’ve seen a lot of dashboards.
This one gets more right than most I’ve seen in the wild.

I’m starting a new series where I’ll be highlighting dashboards we didn’t build — but wish more teams learned from.

Found this gem on Tableau Public (author – Maureen Okonkwo) — and while it’s not built for campaign tracking, the structure is a lesson in usability:

- Smart layout that guides attention
- Filters and toggles are right where users expect them
- Clean visual hierarchy and labeled outliers
- Easy to explore without feeling overwhelmed

Good dashboards don’t overwhelm. They invite action.

This one? Your next marketing dashboard could learn a lot from it.

Follow Nick Valiotti, PhD for more on making data work.

A common question I hear:
“What does a CDO actually do? And why can’t our senior data analyst just do that?”

It’s a fair question. The thing is:
A data analyst’s job is to answer questions.
A CDO’s job is to decide which questions matter — and which ones shouldn’t be asked at all.

Analysts work inside the system.
CDOs are responsible for the system itself.

That means:
→ deciding what data the company should trust
→ setting priorities when everything feels important
→ choosing what to build, buy, or ignore
→ clarifying ownership so decisions don’t stall in debate
None of this is about SQL, dashboards, or tools. It’s about judgment.

At Series A–C, this gap becomes visible. Data exists. Dashboards exist. Talent exists. But nobody is accountable for direction.

That’s usually when founders start feeling the drag: slow decisions, duplicated work, quiet confusion.

This is where a fractional CDO helps — not by replacing analysts, but by giving them direction.

I wrote a short guide explaining how this actually works in practice: what a CDO does, when the fractional model makes sense, and what typically changes in the first 90 days.

Feel free to grab it in my Featured or via the link → https://lnkd.in/d8TDg3DJ

----

👋 I’m Nick, Fractional CDO. If you’re looking for this kind of data leadership, DM me.

The secret to being data-driven?
It’s not a secret.

It’s 3 steps and zero BS.

Step 1: ETL (aka “just move the damn data”)
Fivetran — Free up to 500K rows. Unless you’re Google, you’re fine.
Python script — For people who are either clever… or broke.
Goal: one place for your data. Not duct tape. Not Excel exports.

Step 2: Data Storage
BigQuery. That’s it.
Cheap. Scales forever. Doesn’t cry when you throw SQL at it.
Storage = pocket change.
Queries = fine unless you go full goblin mode.

Step 3: Dashboards That Don’t Suck
Looker Studio. Tableau. Whatever.
A good analyst makes any BI tool sing.
Make it visual. Make it clear. Make it clickable.
That’s how you get decisions, not headaches.

Bonus: Real-Time?
Use Segment if you want click-by-click product tracking.
Just don’t stream straight into BigQuery unless you enjoy surprise CFO meltdowns.

That’s the starter pack.

No enterprise fluff. No shiny $10K toys.
Just a stack that works.

----------

♻️ Repost if you’re still drowning in CSVs with a dream.
Follow Nick Valiotti, for stacks that don’t suck.

Author of Your Fractional CDO — Order on Amazon | Founder @ Valiotti Analytics | Data Strategy & Consulting for Modern Teams | PhD & MA in Analytics

Nothing fuels overengineering like a decision nobody wants to make.
I’ve seen this pattern more times than I can count.

When a team can’t agree on what actually matters, the solution quietly becomes: “Build everything.”

So data engineering turns into an insurance policy.
Extra pipelines “just in case.” Abstracted models “for future flexibility.” Another orchestration layer because maybe we’ll need it later. A schema that can support ten businesses when you barely run one.

It looks sophisticated. Feels responsible. And it’s almost always a symptom of avoidance.

Because here’s the uncomfortable truth:
Most data complexity doesn’t come from scale.
It comes from indecision.

When no one wants to commit to:
→ which metric actually drives the business
→ which questions deserve fast answers
→ which use cases can wait or die
…the platform absorbs the uncertainty.

And the platform always pays interest.

Overengineered systems don’t fail loudly.
They rot slowly:
– longer lead times
– fragile dependencies
– higher cognitive load
– teams afraid to touch anything
All in service of optionality no one ever exercises.

Good data engineering starts much earlier than tools and architecture.
It starts when someone is willing to say: “This is the decision we care about now.”

So if your data stack feels heavier every quarter, don’t ask “what tool are we missing?” Ask: “What decision are we still avoiding?”

👋 Hi, I’m Nick, fractional CDO. I help teams replace “future-proof” complexity with clarity, ownership, and decisions. If your data stack feels heavier every quarter, let’s talk.
Not ready to talk but still need help? This book might be a place to start → https://lnkd.in/dxS6W92D

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Founder @ Valiotti Analytics | Marketing & Sales Dashboards for Growth-Ready Companies | PhD & MA in Data Analytics

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Data stacks are quietly splitting into two camps.

One camp is getting bigger, louder, more “enterprise.”
The other is getting leaner, closer to the warehouse, and suspiciously calm.

The loudest signal in 2025 was the big consolidation around ELT with Fivetran and dbt moving pipelines and transformations under the same roof.

From a distance, it looks clean. Efficient. Inevitable.
2026 will show whether it actually stays that way.

What we’re seeing on the ground is different. Alongside dbt-style workflows, we’ve been actively using Dataform in real client projects. Not as an experiment. As a default choice in several cases.

And here’s the truth: for warehouse-native SQL transformations, the results are not worse.
Same idea. Same logic. Same outcomes.
Models live in the warehouse. Transformations are written in SQL. Dependencies are explicit. Everything is versioned. The analytics layer gets exactly what it needs — and nothing extra.

So the question teams are starting to ask in 2026 isn’t “what’s the standard tool?”
It’s: what am I locking myself into?

To be clear, Fivetran remains a very strong market standard for ingestion. No debate there. And dbt earned its place by defining analytics engineering as a discipline.

But when transformations can live closer to the warehouse — with fewer licenses, fewer dependencies, and fewer points of no return — people notice.
What’s emerging right now isn’t a rejection of modern data tooling. It’s a preference for leverage.

Less platform gravity. More warehouse ownership. Same results.
That feels like a very 2026 direction.

What do you think?

Read this book before you start your next analytics project.
Because it teaches you one lesson that will save you hundreds of hours and hundreds of thousands of dollars:

Your first question matters more than your first dataset.

Most failed analytics projects start long before the dashboards do.
They start with vague, unanswerable questions:
“How do we grow?”
“What’s working in marketing?”
“Can we be more data-driven?”
These aren’t questions.

They’re weather forecasts with a KPI attached.

A real analytics question is specific, falsifiable, and tied to a decision:
→ “Which acquisition channel produces customers with the highest 90-day LTV?”
→ “Which step in the funnel creates the biggest revenue leak?”
→ “Which product segment is worth prioritizing next quarter?”

Ask better questions → get better insights → make better decisions.
Every transformation starts there.

This principle is so fundamental that it’s the backbone of my new book, Your Fractional CDO.
Not tools, not architectures, not AI dashboards with glowing neon charts.
Just decision-making clarity.

In the book, I break down how to rewrite messy business questions into clean, analytics-ready ones — the exact process I use with founders, CMOs, and product teams.

Once you do this, everything else gets easier:
You know what data you actually need, you stop building dashboards “just in case”, you avoid 3-month detours into perfect ETLs that solve nothing, and you finally get insights that drive action instead of confusion.

Check out Your Fractional CDO on Amazon → https://lnkd.in/dU-kiEUf

You hired a CDO.

Nothing changed. Why?
Because one person can’t fix an 8-person problem.

Most data disasters don’t come from bad talent.
They come from asking one human to be:
a strategist, an architect, an engineer, an analyst, a BI developer, an AI owner,a translator to execs, a firefighter on Slack. 

That’s not leadership. That’s organizational confusion.
So what happens? Your “data scientist” is building pipelines.
Your analyst suddenly owns AI because “they’re good with numbers.”
Your BI dev is trying to architect a warehouse.

Every team has dashboards. None of them agree.
Everyone is busy. Nothing is reliable.

This is what should be happening instead:
→ Data Engineers move data — clean, automated, invisible.
→ Data Architects design how everything fits and scales.
→ BI Developers turn tables into usable interfaces.
→ Analysts explain why numbers move and what to do next.
→ ML / AI roles only exist after the foundations are solid.

It’s a relay, not a solo marathon. Each role carries the baton at the right moment.

Now here’s the uncomfortable truth:
A full-time CDO is expensive. And most companies don’t actually need one sitting on payroll.

What they need is the CDO thinking:
→ role clarity
→ sequencing
→ ownership
→ alignment to revenue and growth

That’s why I wrote Your Fractional CDO. Not a leadership memoir. Not AI hype.
A practical playbook for building a data system that works — without hiring an army or burning budget. Available on Amazon → https://taap.it/Yt0nMRT

Santa dropped by data-LinkedIn this year. 
Not with mugs or hoodies. With very on-brand gifts 🎅📊

Or let’s be honest — there are no kids here. Everyone made these gifts for themselves by putting out genuinely useful content.

1. José Siles unwrapped simpler, saner data stacks, because his engineering advice helped people stop overbuilding.

2. Clare Kitching earned governance that teams actually respect, clear rules, shared language, real buy-in.

3. Slawomir Tulski built pipelines that hold up under pressure, even while moving countries. No small feat.

4. Morgan Depenbusch, PhD ended the year with dashboards that just work, because teams actually followed her chart playbook.

5. Sebastian Hewing received data strategies that survive contact with reality, because they’re built around decisions, not slides.

6. 🏴‍☠️ Bill Yost got meetings that feel lighter and more human, thanks to humor that makes data less intimidating.

7. John Cook got teams that trust their data leaders, the natural outcome of clear, thoughtful leadership content.

8. Arthur Feriotti earned leaders who show up better for their teams, not louder — better.

9. Ali Šifrar analytics that answer real questions, because his content keeps things practical.

10. Marcel Dybalski received clarity across the analytics stack, from exec views to hands-on work.

11. John Wernfeldt ended up with foundations done right, so everything else has something solid to stand on.

12. Shachar Meir earned careers that move with intention, fewer panic moves, better decisions.

13. Dmitry Nekrasov got metrics and driver trees that point to action, not just observation.

14. Nevada Pascolini earned timelines with a sense of humor, dry, sharp, and very needed.

15. Donabel Santos received analysts who advocate for themselves earlier, because her career insights stick.

16. Tris J Burns 🔥 got analytics professionals who see a path forward, not just their next task.

17. Fabian Werkmeister built ETL pipelines that run quietly, the highest compliment in engineering.

18. Anastasiya Kuznetsova earned reports that explain themselves, no walkthrough required.

19. Andy Kriebel ended the year with Tableau dashboards people actually use, daily. Voluntarily.

And me… tomorrow morning I’ll be looking for founders and execs who understand why work with data and how to do it right from day one.

Your turn 👇
What gift would you like for yourself? Share in the comments!
And tag someone new I should follow going into 2026.

Most dashboards don’t fail because of bad data or bad charts.

They fail because they were built too early.

Someone asks for “a dashboard.”
The team opens the BI tool.
SQL gets written. Charts get polished.

And only at the end does anyone ask the uncomfortable question:
“Wait… what decision is this supposed to support?”

That’s why prototyping is non-negotiable if you want dashboards that actually get used.

Prototyping forces the hard thinking before the build:
→ What questions are we really trying to answer?
→ Who is this for, and what do they need to decide?
→ Which metrics matter, and which ones are just noise?
→ What level of detail is useful vs overwhelming?

A simple low-fidelity prototype (even boxes and labels) does two critical things:
1 — It exposes vague thinking immediately.
2 — It lets stakeholders react to structure and logic, not colors and charts.
By the time you open your BI tool, 80% of the decisions should already be made.

That’s exactly why I put together this practical, no-nonsense cheatsheet for dashboard discovery and prototyping.

It’s a repeatable framework to gather clean, complete dashboard requirements — without endless meetings, vague requests, or dashboards that get ignored.

The guide walks you through:
– Running focused user interviews
– Defining business questions first
– Aligning on metrics and breakdowns
– Mapping data sources
– Translating all of that into a clear, stakeholder-friendly layout

If your dashboards aren’t getting used, don’t add more charts.
Prototype better → https://lnkd.in/dxyFYdUy

Hot take:

You don’t need a data team.

Your nephew’s “pretty good at Excel,” right?
He can whip up a dashboard, eyeball some trends, and tell you what’s “probably working.” No need for data governance, definitions, or version control — just vibes and pivot tables.

Who needs analysts when you’ve got family?

A lot of companies think they’re “behind” on data.

In reality, they’re just out of sequence.

I wrote Your Fractional CDO for leaders who feel pressure to “level up” analytics, but aren’t sure what level they’re actually on. The book walks through a simple but uncomfortable truth:

Most teams don’t fail because they lack data. They fail because they try to operate like a Stage 5 company while still living in Stage 2 or 3.

That leads to:
→ overbuilt dashboards
→ premature tooling
→ stalled initiatives
→ and a constant sense of “why isn’t this working yet?”

The goal isn’t to be “data-driven.” It’s to build the next layer that actually fits your business right now.

Normally, Your Fractional CDO is available on Amazon. But I’m giving free copies to a small number of founders / execs who feel stuck between “we know data matters” and “we don’t know what to do next.”

If that’s you, comment “CDO” and tell me what stage you think you’re in — or where things feel forced. If the book can help you move forward cleanly, I’ll send you a copy.

If your Christmas wish is to get promoted,

I’ll be your Santa.

Here’s the uncomfortable truth:
Promotions don’t go to the person who worked the hardest. They go to the person whose work was understood.

Clear reporting does a few unfair things: it makes your thinking visible, your decisions legible, and you — senior before your title catches up.

When leadership sees:
→ clean charts
→ obvious takeaways
→ numbers that answer questions instead of creating new ones
they don’t think “nice dashboard.”
They think: “this person gets the business.”

While messy reporting quietly caps your ceiling: unclear charts → unclear judgment, unclear judgment → no trust, no trust → no promotion.

That’s why I built The Data Visualization Handbook for Managers. 
Not to make your charts prettier, but to make your work easier to read, harder to ignore, and impossible to misinterpret.

If you want your reports to speak for you when you’re not in the room, this is for you → https://lnkd.in/gdypQkDb

2025 was… a lot.

In a good way.

This year I: 

→ published a book (still wild to say that out loud). Dropping the link in case you need a ast-minute New Year’s present → https://lnkd.in/dGJgV9Xj

→ celebrated 6 years of Valiotti Analytics and expanded the team even more 

→ shipped 10+ practical products helping Upworkers and managers grow without burning out 

→ grew LinkedIn from ~1.5k to 15k followers 

→ hit ~4 million post impressions (still don’t fully process this one)

Together with the Valiotti Analytics Team we delivered over 20 projects across non-profits, HVAC, accident management, e-commerce — very different worlds, same goal: make data actually useful for real decisions.

I also launched a newsletter focused on real cases. No theory. Just “here’s what broke, here’s what we fixed, here’s what changed for the business.” In case you missed it → https://lnkd.in/dYSzvs4U

And honestly? One of the best parts: meeting dozens of genuinely smart, generous, sharp people here on LinkedIn. The comment sections, DMs, disagreements, ideas — all of it made the work better.

No big “new year, new me” resolution. Just more clarity, more usefulness, fewer buzzwords, better work.

Happy New Year 🎉

Most companies don’t waste money on data tools.
They waste it on the wrong data hires.

Last year (had to do it!) I released a book called Your Fractional CDO — and if you’re a founder, CxO, or anyone involved in decision-making, it can save you a lot of time, money, and frustration.

It’s a practical 101 to data analytics, written in plain language. But one section matters more than most: data roles.

Your first data hire can either accelerate your business or quietly burn months of runway. Not because they’re bad — but because each role solves a different layer of the system, and there’s a natural order to building a data team.

Here’s the short version:

Data Engineer — pipelines, integrations, storage, identity stitching. They build the foundation everything else depends on.

Data Analyst — investigations, insight generation, decision support. They tell you what changed — and why it matters.

Analytics Engineer — transformations, models, metric definitions. This is where CAC, ROAS, LTV, and retention become trustworthy.

Data Scientist — forecasts, experiments, advanced modeling. Powerful, but only once the basics are stable.

CDO / Head of Data — strategy, architecture, prioritization. The role that ensures data work drives ROI, not noise.

In the book, I break down each role in detail: what they actually do, when you need them, what problems they solve, and the risks of hiring them too early or too late.

I also share a simple framework for assembling your first data team based on your company stage and the decisions you’re trying to make.

If you’re hiring your first (or next) data person, read this first.
Your Fractional CDO is out now on Amazon: https://lnkd.in/dczU_G34

This is the only book you should read before your first data hire.

It will save you months of trial and error, tens (or hundreds) of thousands in wrong hires, and a lot of internal frustration you won’t see coming until it’s too late.

Here’s what usually happens instead:
A company “decides to do data.”
→ They post a vague job description.
→ They hire an analyst.
→ Two months later: dashboards everywhere, answers nowhere.

Not because the hire was bad — but because leadership never defined what the analyst was supposed to change.

In Your Fractional CDO, I make one thing painfully clear:
Data roles don’t create value. Decisions do.

This book helps you:
→ know who to hire (and who not to hire yet)
→ understand what analytics can realistically deliver at your stage
→ avoid building systems that look impressive but solve nothing
→ turn data work into business outcomes, not reporting theater

No tech deep-dives. No vendor bias. No “AI will fix it” fantasies.
Just a clear mental model for leaders who want to get data right the first time — instead of paying twice to fix it later.

If you’re about to hire your first analyst, engineer, or “data person” this book is cheaper than getting it wrong → https://lnkd.in/gAfvcYn9

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Nikolay Valiotti

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Nikolay Valiotti's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreNikolay Valiotti's LinkedIn Performance