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

Founders don’t lack data.
They lack confidence to make decisions with it.

I work as a Fractional Chief Data Officer for scaling tech companies, when analytics becomes business-critical — but hiring a full-time CDO is too early or too risky.

I’m the founder of Valiotti Analytics, a data consultancy trusted by 50+ teams globally. I work with companies across the US and Europe.

I have a PhD in data science, which helps when problems are complex — but my work is always grounded in business outcomes.

What I typically change:
• Data & analytics strategy aligned with business goals
• Clear metrics and decision frameworks
• Analytics org design (people, tools, processes)
• Systems that teams actually use, not just maintain

Before consulting, I was an in-house operator. I’ve lived through messy data, slow reports, and dashboards nobody trusts — and I know why “just build another report” never works.

Since 2019, I’ve helped marketing, sales, and product teams:
→ Scale revenue up to 25x
→ Eliminate manual reporting and data chaos
→ Turn analytics into a decision-making system, not a cost center

I usually work in a fractional format (3–12 months), directly with CEOs, CPOs, and CMOs — with optional execution support from my team.

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

Fractional CDO | Helping Scaling Tech founders turn data into faster decisions | Founder @ Valiotti Analytics

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

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ClickHouse Developer

Tableau Desktop Certified Associate

A Course for Executives

Digital Transformation Academy

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

🚀 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

Let's discuss your project and grow you business with the power of data

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Ivan C.

A data strategy that looks perfect on paper

usually dies in real life.

I’ve seen this pattern across companies of every size.
There’s always a polished deck, five-year vision, clean architecture, immaculate governance, dashboards that could win design awards.

And then you look at how decisions are actually made.
→ Sales still lives in spreadsheets
→ Marketing trusts the ads manager more than internal reports
→ Finance keeps a “shadow model” just in case
And that's not because people hate data, but simply because the strategy never met reality.

From a Fractional CDO standpoint, this is the core mistake:
teams confuse designing a system with changing behavior.

The thing is, data strategy is an operating model. So if adoption isn’t happening, the problem is rarely tooling. It’s usually one of these:
1. No clear decision owner
2. No agreed definition of “good enough” data
3. No incentive to switch from existing workflows
4. No tolerance for messiness during transition

A strategy that only works when everything is clean, aligned, and fully implemented is just a concept. Real data leadership starts smaller and rougher:

– Which decisions actually matter this quarter
– Who owns them
– What data is “good enough” to act
– Where the workflow already lives
If the answer doesn’t fit into how people already work, it won’t be used, no matter how elegant it looks.

Strategy isn’t supposed to be beautiful.
It’s supposed to survive contact with reality.

P.S. Had this convo with Marcel Dybalski a while ago, thought more people need to hear it!
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👋 Hi, I’m Nick — Fractional CDO. I help teams turn “data vision” into decisions people actually use. If your dashboards look great but nobody trusts them, that’s usually where I start.

AI & Data Platform Engineer | GCP & Fabric Certified

“Data-driven” — easy to say, brutal to explain.

I turned it into my elevator pitch.

Step 1 — Get all your data in one place
Your numbers live everywhere — website, ads, CRM, spreadsheets.
First, you just need to collect them.
→ Fivetran: connects the tools for you (free up to 500K rows).
→ Python: for people who like to build things themselves.
Goal: one single place where all your data lives.

Step 2 — Store it safely
Once you’ve collected it, you need to keep it somewhere reliable.
That’s BigQuery.
Think of it as a big digital storage room for all your numbers.
It’s cheap, fast, and handles anything you throw at it.

Step 3 — Make it visual
Turn all that data into something you can actually use.
That’s your dashboard.
It doesn’t really matter which tool you go for. Looker Studio, Tableau, whatever.
A good dashboard shows what matters — sales, traffic, growth —
without making your eyes hurt.

Bonus — real-time?
Want to see what users do on your site right now?
Use Segment.
Just don’t send every click straight into BigQuery — your CFO won’t like the bill.

That’s it.
Three steps. Zero $10K toys.
Just a simple stack that works.

♻️ Repost if this has been the most insightful elevator ride this quarter.
Follow me Nick to learn how to explain it to your grandma next.

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.

Nick Valiotti, PhD

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

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

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.

As a CEO and founder — this dashboard is just great. Simply because it respects how execs actually think.
They don’t open a dashboard to analyze. They open it to confirm or challenge a belief.

This one starts with the only question that matters:
Did we hit quota?
101.3%. Answered. No interpretation required.

Immediately after, it answers the second executive question:
Is this short-term noise or a real trend?
MTD, YTD, last month, last year — side by side, same visual language. 
The user doesn’t have to remember numbers. They just register direction.
Green means “we’re fine.” Red means “ask why.” That’s executive UX.

What’s also strong here is sequencing. Cumulative → period → comparison → distribution over time. That mirrors how leadership conversations actually happen in a room.

The total sales number isn’t there to impress anyone. It’s there to ground the conversation. Execs anchor on scale before they talk variance. This dashboard knows that.

The monthly chart at the bottom is doing quiet but serious work. It shows where execution deviated from expectation and how often that happens.
That’s a performance management view, not an analytics one.

Most importantly:
There is no moment where an exec would have to ask, “So what should I do with this?”
They can already tell:
– whether to push the team
– whether to stay hands-off
– whether to zoom into a specific month
– whether this is a sales issue or a planning issue

That’s why this is an executive dashboard.
Not because it has fewer charts. But because it collapses complexity into decisions, instead of pushing complexity onto the reader.

That’s the difference most teams miss. Bravo, Sebastine Amede!

----

👋 Hi, I’m Nick — fractional CDO. I break down what actually makes dashboards useful to leadership. Follow for reviews like this!

Tableau Developer | Business Intelligence Developer | Data Analyst |Tableau Ambassador’25

My feed and comment section keep panicking over “the death of the data analyst.” 

Which tells me most people still don’t understand what the job was in the first place.

A data analyst was never meant to be a SQL code generator. Not before. Not now. SQL was the easiest part of the job — so we turned it into the job.

For years, we hired analysts to write queries, ship exports, and keep the dashboard lights on. But that role always had an expiration date. AI just made it obvious.

Fast-forward to 2026.
→ LLMs write SQL in seconds.
→ Warehouses scale automatically.
→ “Can you pull this data?” is no longer a skill.

But here’s what didn’t change:
CMOs still don’t trust the numbers. CEOs still ask, “Wait — what’s included here?” Meetings still end with, “Let’s double-check that.” Because the business didn’t suddenly become analytical!!

No one is validating assumptions. No one is pressure-testing metric logic. No one is responsible when AI output sounds right but is wrong.
That responsibility moved to the analyst.

A strong analyst today:
— Lives inside the business, not the warehouse.
— Translates messy questions into precise metrics.
— Knows where models hallucinate and why.
— Owns conclusions, not queries.

SQL is cheap now. Dashboards are everywhere.
Judgment is still rare. That’s the job.

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

If you think AI will “solve analytics” in 2026, you’re focusing on the wrong problems. Here are my predictions. 3 things I’m confident about.

1 — People trust people. Not models.
This hasn’t changed. It didn’t change before AI, it won’t change after.

When you sent a report to a director, they trusted it because you owned the numbers. You could recheck them. You were responsible.
No one wants to blindly trust LLM output. There is always a human who validates, verifies, and stands behind the result.

That’s why a new role emerging this year: AI-enabled analyst.
Not “AI replaces analyst”. Analyst who uses AI and owns the outcome.

2 — Data is still the foundation.
No matter how good LLMs get, everything still depends on data quality: clean structure, clear logic, well-designed models.

Yes, LLMs can help design schemas and even suggest warehouse structures. But you can’t just say:
“Hey AI, fix my data.”

You still have to explain:
→ what the business actually does
→ how metrics should behave
→ which decisions depend on them
Good prompting still requires a brain, context, real business understanding. AI doesn’t remove thinking. It exposes the lack of it.

3 — The chat interface is already annoying.
Let’s be honest: business leaders don’t want to chat with data. One question leads to ten more. No CXO is spending their day interrogating a chat window about metrics.

Chat is great for exploration, not for decisions.
What’s coming instead:
→ You see a chart.
→ A metric drops.
→ You click it.
→ AI runs the analysis in the background.
→ Returns 2–3 hypotheses actually worth checking.

And then — the crucial part — someone still owns the conclusion.
Same role as point #1.

The boring truth: AI accelerates analytics. Responsibility doesn’t scale automatically.

What changes do you think we’ll see?

--------

👋 Hi, I’m Nick — Fractional CDO. I write about building analytics teams that actually work, not just demo well. Follow for practical takes on data, AI, and decision-making.

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

If your data work mostly happens with IT, you’re probably not doing strategy. You’re doing infrastructure.

I always say that and my recent conversation with Mike Drake was another proof to its rlevance across teams. We were talking about fractional CDOs, when they actually add value, and why so much data work quietly underdelivers. Here’s the pattern we kept coming back to:

Strategy doesn’t live where tickets live, it lives where outcomes live.
CEO. CFO. CMO. Revenue. Risk. Growth. Allocation.

When data is treated as an IT function, a few things happen almost automatically:
→ The work optimizes for systems, not decisions
→ Dashboards pile up, but ownership stays fuzzy
→ Teams argue about numbers instead of acting on them

That’s not a tooling, but a positioning problem.
The real job of a fractional CDO isn’t “better dashboards” or “one more model.” It’s turning data into a shared business asset:
– Owned by leadership
– Aligned to decisions
– Designed to compound over time

A few things Mike and I aligned on quickly:
→ Execution is getting commoditized. AI and cost pressure made sure of that.
→ SQL is cheap now. Thinking isn’t.
→ Dashboards should be disposable. Foundations shouldn’t.
And if you don’t historize from day one, you don’t get a second chance later.

The path I keep seeing work looks like this:
IT-centric data → shared business capability → revenue leverage. That’s where strategy actually lives. And that’s the only place it gets paid for.

If you’re a business leader trying to understand where your data work is helping — and where it’s quietly holding you back — comment CDO below.

I’ll send you a link to a quick quiz I built that helps identify data bottlenecks, growth zones, and gives you a concrete 7-day action plan to move things forward.

And Mike, thanks again. Always a good sign when a call reorganizes a few neurons instead of just filling a calendar.

Fractional Data Leader | From Chaos to Clarity | Uncomfortable Conversationalist | Liberator of Hostage Data

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?

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

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.

Most dashboards fail for a simple reason:

They answer questions nobody asked.

We jump straight into charts, colors, and layouts… but skip the one step that actually makes a dashboard useful:
Understanding how people will use it.

Here’s the part so many teams miss:
A useful dashboard isn’t built around metrics. It’s built around decisions.
Before opening Figma, Tableau, Power BI, or anything else — ask your users:

→ What decision do you need to make faster?
→ What is the pain with your current process?
→ What do you consider a “good” or “bad” week?
→ Which slices, filters, or comparisons do you need every time?
→ Which metrics do you actually trust?

If you can get clear answers to these five things, the dashboard basically designs itself.
No more 12-widget monsters. No more “Can we add one more chart?” No more reports that look great… and get ignored.

Because the truth is simple:
Dashboards fail when they’re built for the tool, not the user. Dashboards succeed when they’re built for the decision.

If you want a shortcut:
I put together a Dashboard Interview Cheatsheet — the exact framework I use with clients to turn vague requests into dashboards that actually get used.

Happy to share it: https://lnkd.in/dNSPbAzx
Let’s make dashboards people actually use.

Nothing says “data-driven company” like KPIs updated by hand every Friday.
But sure, let’s talk about machine learning next quarter.

And yet, every quarter (if you’re lucky, if not — every month), someone asks for a “cool dashboard.”
As if design can fix dysfunction. Surpise-surprise, it won’t.

Here’s the quick sanity checklist every BI analyst to run before saying yes:

1 — Data foundation
If your inputs aren’t automated, every chart is a ticking time bomb. Dashboards built on manual data rot faster than milk in the sun.

2 — Metric alignment
If “Revenue” means one thing to Marketing, another to Finance, and a third to the CEO — you’re not visualizing insights, you’re hosting a family argument.

3 — Decision logic
Every chart should answer a question someone actually asks. If it doesn’t change behavior, it’s just decoration.

So next time someone says, “We just need a dashboard,” maybe ask: “Do we have data… or do we have Excel?”

Because there’s a big difference between building analytics and making PowerPoint interactive.

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I’m Nick, and that’s exactly what we fix at Valiotti Analytics — building systems where data flows, definitions align, and dashboards actually tell the truth. If that sounds like your next step, let’s talk!

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

If I had to give just one piece of data advice, it would be this:
Build the Boston Public Library.

This idea clicked for me over the holiday break, spending a few quiet hours there. No rush. No noise. Just space designed for thinking.

A good library does a few things quietly and exceptionally well.
Books are easy to store — because there’s a system. Knowledge is easy to access — because things are labeled. Learning feels natural — because the space removes friction.
The room itself helps you think.

Now look at most data environments.
Files stacked like books on the floor. Queries written once and forgotten. Sheets that “kind of” match, but never fully agree. Dashboards that exist, but no one sits down with them.
That’s what a library looks like without shelves.

In a real library:
– every book has a place
– similar topics live near each other
– you know where to look before you start searching
– history is preserved, not overwritten

In good analytics:
– every metric has a definition
– related data lives together
– you know where truth lives
– past decisions stay explainable

Libraries don’t optimize for storage. They optimize for understanding.
Great analytics works the same way. It’s not about having more data. It’s about making learning feel inevitable. Because mess spreads quickly. But order compounds quietly.

So if your dashboards feel heavy, your metrics feel fragile, or your team keeps asking the same questions again and again — you don’t need a new tool.
You need shelves, labels, and silence.
Build the library.

------

👋 I’m Nick, Fractional CDO. I write about building calm, decision-ready analytics systems. Follow if this is the kind of data thinking you want more of.

✨Bhavana Jaiswal

topmate.io

Hi, I’ve started my Topmate to help freshers — free of cost!

Data Engineer | Google Certified | Mentor @Beau Roi
I didn’t have a mentor when I started in IT, so I became one.

✅ Resume Review
✅ LinkedIn + Branding
✅ Role Clarity (DA, DE, BI…)
✅ Project Help + Mock Qs
✅ Interview Communication

📞 Book a free 1:1 — let’s build your career!

Topmate Mentor

Vrahad Analytics

Built and optimized end-to-end ETL pipelines using Databricks for structured (CSV/XLS) and semi-structured (JSON) datasets.

Designed ER diagrams and relational data models to ensure efficient database architecture.

Connected and ingested data from Azure, AWS, and GCP via Databricks integration connectors.

Automated workflows for data ingestion, transformation, and validation, improving efficiency and scalability.

Applied PySpark, SparkSQL, and SQL to handle large-scale data processing and analytics.

Skills: Databricks · PySpark · SparkSQL · SQL · ETL Pipelines · Data Modeling · ER Diagrams · JSON/CSV/XLS · Azure · AWS · GCP

Databricks Engineer

Beau Roi

Stepping into my new role as a Cloud Data Engineer, I'm excited to work at the intersection of data, cloud, and innovation. I specialize in building scalable ETL pipelines using Databricks, PySpark, and cloud-native services across GCP and AWS. From ingesting raw data to transforming it into actionable insights, I ensure every pipeline is optimized for performance and reliability. Over the past years, I've also had the opportunity to mentor and guide 10+ interns, helping them transition from foundational learners to confident contributors. This role empowers me to blend hands-on technical delivery with leadership and cross-functional collaboration, driving real business value through cloud-powered data solutions.

Cloud Data Engineer

As an Associate Consultant, I play a key role in designing, building, architecting, deploying, and testing demo application products across various domains such as Inventory Management, Healthcare, Education, Logistics, Pet Care, E-commerce, and Medicare. I focus on translating complex requirements into efficient, sector-specific software solutions.

Beyond my technical responsibilities, I lead a dedicated team, ensuring that projects are executed with precision and efficiency. I am committed to creating a collaborative environment where team members feel supported and empowered to contribute their best work. I provide guidance when challenges arise, helping the team overcome obstacles to achieve project goals.

In my current role as Associate Consultant, I am responsible for designing and implementing solutions, leading client calls, conducting demo presentations, and overseeing project enhancements. I also bring new ideas and plans to the table for continuous improvement, while managing reports for both employees and senior leadership. My role involves ensuring seamless project execution and maintaining strong client relationships.

Associate Consultant

As a Software Analyst, I play a key role in the end-to-end process of designing, building, architecting, deploying, and testing demo application products across various domains, including Inventory Management, Healthcare, Education, Logistics, Pet Care, E-commerce, and Medicare. My work involves translating complex requirements into effective software solutions that meet the specific needs of each sector.

Beyond my technical responsibilities, I also lead a dedicated team, ensuring that our projects are executed with precision and efficiency. I am committed to fostering a collaborative environment where team members feel supported and empowered to contribute their best. Whenever challenges arise, I provide the necessary guidance and assistance to help my team overcome obstacles and successfully achieve our project goals.

Software Analyst

Intern

Upwork

Azure Data Engineer

Built and optimized end-to-end ETL pipelines using Databricks for structured (CSV/XLS) and semi-structured (JSON) datasets.

Designed ER diagrams and relational data models to ensure efficient database architecture.

Connected and ingested data from Azure, AWS, and GCP via Databricks integration connectors.

Automated workflows for data ingestion, transformation, and validation, improving efficiency and scalability.

Applied PySpark, SparkSQL, and SQL to handle large-scale data processing and analytics.

Databricks Data Engineer

Bharat Intern

Stock Prediction with LSTM:
Embark on a fascinating journey into the realm of stock market dynamics by building a predictive model using LSTM (Long Short-Term Memory). This project revolves around forecasting the future stock prices of a chosen company. By analyzing historical stock price data.

Titanic Survival Classification:
Delve into the intriguing world of data analysis and classification as you tackle the Titanic Survival project. Through a systematic examination of factors like socio-economic status, age, and gender, this project seeks to discern patterns that influenced passengers' chances of survival during the infamous Titanic sinking. By employing machine learning techniques, this endeavor contributes to our understanding of historical events and their underlying dynamics.

Handwritten Number Recognition with Neural Networks:
Embark on an exploration of image recognition and artificial intelligence with the Handwritten Number Recognition project. Powered by Neural Networks and utilizing the MNIST dataset, this project showcases the capability of technology to decipher and categorize scanned images of handwritten digits. Through a series of intricate computations, this endeavor underscores the potential of machine learning in accurately identifying and interpreting handwritten content.

Data Science Virtual Internship 

Forage

During my internship at KPMG, I was actively involved in performing a Data Quality Assessment to ensure the accuracy and completeness of the data for subsequent analysis. This hands-on experience in evaluating data quality equipped me with valuable skills in Data Quality Analysis and the creation of analytical dashboards.

Another significant aspect of my internship was centered around identifying high-value customers through a comprehensive analysis of customer demographics and attributes. This engaging task provided insights into Data Analytics and Customer Segmentation, empowering me to contribute to data-driven strategies for targeting specific customer segments effectively.

Furthermore, I had the opportunity to leverage my data insights in crafting impactful presentations. Utilizing various data visualizations, I presented key findings and insights in a compelling manner. This experience strengthened my expertise in Data Dashboards, Data Visualizations, and creating presentations that effectively communicate complex data-driven insights.

Overall, my internship at KPMG enriched my analytical abilities, data-driven decision-making skills, and proficiency in presenting data insights to facilitate informed business decisions.

Short Term Virtual Internship  in Data Analytics  via Forage

Accenture

During my internship at Accenture, I gained valuable skills in Excel and Power BI, with a primary focus on data cleaning, data integration using VLOOKUP, and extracting meaningful insights. I was responsible for connecting datasets across multiple sheets and applying data cleaning techniques to ensure data accuracy and consistency. Through this experience, I developed a strong understanding of data analysis and visualization, enabling me to present valuable insights to the team. Overall, my internship at Accenture provided me with a solid foundation in data management and analysis, contributing to my professional growth and proficiency in handling complex datasets.

Short Term  Virtual Internship  in Data Analyst and Visualization via Forage

Data Engineer ⭐5 SQL & 3⭐ Python Badges @ Hackerrank || Azure Synapse Analytics | Azure Databricks | Azure Data Factory | Azure Purview | Azure Cosmos DB | Azure Data Lake Storage

👋 Hi! I'm Bhavana Jaiswal, a 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 with 3+ 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐫𝐚𝐰 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬. I thrive at the intersection of data, technology, and innovation, leveraging my expertise in 𝐜𝐥𝐨𝐮𝐝 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬, 𝐛𝐢𝐠 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬, 𝐚𝐧𝐝 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 to deliver impactful solutions.  

🏆 Key Achievements & Certifications:
✅ 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
✅ 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
✅ 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐒𝐭𝐮𝐝𝐞𝐧𝐭 𝐀𝐦𝐛𝐚𝐬𝐬𝐚𝐝𝐨𝐫 𝐢𝐧 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠
✅ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞
✅ 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜

🎯 Goals:
🔹 Google Professional Cloud Data Engineer Certification
🔹 Earning the AWS Golden Jacket 🧥

💡 Core Skills & Expertise:
🔹 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 & 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐒𝐐𝐋, 𝐏𝐲𝐭𝐡𝐨𝐧 (𝐍𝐮𝐦𝐏𝐲, 𝐏𝐚𝐧𝐝𝐚𝐬, 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛, 𝐒𝐞𝐚𝐛𝐨𝐫𝐧)
🔹 𝐂𝐥𝐨𝐮𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬:
• 𝐀𝐳𝐮𝐫𝐞: 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲, 𝐀𝐃𝐋𝐒, 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬, 𝐒𝐲𝐧𝐚𝐩𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
• 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 (𝐆𝐂𝐏): 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲, 𝐃𝐚𝐭𝐚𝐟𝐥𝐨𝐰, 𝐃𝐚𝐭𝐚𝐩𝐫𝐞𝐩, 𝐃𝐚𝐭𝐚𝐩𝐫𝐨𝐜, 𝐂𝐥𝐨𝐮𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞, 𝐏𝐮𝐛/𝐒𝐮𝐛, 𝐂𝐥𝐨𝐮𝐝 𝐒𝐩𝐚𝐧𝐧𝐞𝐫, 𝐂𝐥𝐨𝐮𝐝 𝐒𝐐𝐋, 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 𝐄𝐧𝐠𝐢𝐧𝐞, 𝐈𝐀𝐌
 🔹 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: 𝐏𝐲𝐒𝐩𝐚𝐫𝐤
🔹 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: 𝐆𝐢𝐭 & 𝐆𝐢𝐭𝐇𝐮𝐛
🔹 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈

🚀 Current Role & Experience:
As an Associate Consultant at BeauRoi Technology Pvt Ltd, I specialize in designing and deploying cutting-edge data solutions across industries such as Inventory Management, Healthcare, and E-commerce. I enjoy leading teams, solving technical challenges, and ensuring project success through collaboration and innovation.

🌍 Mission & Vision:
I aim to empower businesses with scalable, data-driven solutions that enhance efficiency and drive growth. My philosophy revolves around continuous learning, innovation, and leveraging data to unlock new opportunities.

📈 Let’s Connect!
Looking for a detail-oriented, dedicated, and innovative Data Engineer to transform your data into meaningful business insights? Let’s connect and explore opportunities to collaborate! 🚀

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Nikolay Valiotti

✨Bhavana Jaiswal

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✨Bhavana Jaiswal

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