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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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.

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!

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

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

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 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.

----------

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!

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

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

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

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 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.

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.

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.

AI on top of Excel.

What could possibly go wrong?

→ One source of truth (per tab)
→ KPIs defined emotionally
→ “Don’t touch that formula”
→ AI confidently explaining wrong numbers

✨ Innovation ✨

When was the last time a dashboard helped you make a decision without someone saying, “let me double-check that”?

If the answer isn’t “recently,” you’re not alone.
I see this all the time:
– the dashboard is open
– the numbers look reasonable
– and yet the decision stalls

Someone asks for another cut. Someone pulls up a spreadsheet. The meeting ends with “let’s come back to this.”

Usually, nothing is technically broken. What’s missing is clarity around three things:
→ who owns the decision
→ which metrics actually matter
→ whether the numbers are trusted enough to act on

So I put together a short, practical check to make this visible. It’s called the CDO Healthcheck and takes under 5 minutes.

No tools. No vendors. No buzzwords. You just answer a few questions based on how things work today — not how they’re supposed to work. At the end, you get a clear read on where friction is coming from and what to fix first.

If you want the link, comment "CDO" and I’ll send it over.
If dashboards feel heavy instead of helpful, this will tell you why.

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.

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👋 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.

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

🏄‍♂️ Dmitry Anoshin

Surfalytics

Surfalytics = Surfing + Analytics. Inspired by West Coast surfing spots and Pacific ocean vibes. Created with goal to help you start a new career in data analytics space, develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus to deliver significant value to businesses in the analytics realm as well as help to get fair compensation for the work you're passionate about.

There are several key producs:

1. Membership in a community with the goal of landing the job, preparing for the interview and getting the best offer. Every month, we do a Data Engineering and Analytics project and discuss our progress on the journey.

2. Community support to
2.1. Land 1st job in data
2.2. Move from analyst to engineer
2.3. Promote from middle to senior and beyond

3. Free course based on my 14 years of experience in Data Engineering and Data Analytics

Data Engineering and Analytics Mentor

University of Victoria

Instructor | Cloud Computing, Analytics and Data Engineering

Rock Your Data

- Data Team building and solving bottlenecks in team delivery
- Building Data Engineering Solutions with Modern Data Stack
- Data Infrastructure and Data Ops (DevOps)
- Data Security and Compliance

Advisory Board Member

Microsoft

- Building Delta Lake on top Databricks and Azure for Microsoft Gaming
- Structured Streaming
- Self-Service BI

Data Engineering & ML

Amazon

- Designed and Implemented Cloud Data Platform for Alexa Quality Metrics
- Designed and Implemented ML platform into production for Customer Perception, Onsite Experience and Churn
- Analytics Modernization with AWS
- Data privacy and Security
- Tracked and Optimized solution cost

Locations: Vancouver, Cambridge, Seattle

Communities:
- Lead Amazon Tableau User Group with 2k+ users.
- Lead Amazon BI Tech Talk community with 100+ BI teams globally.

Packt

•	Author of "Azure Data Factory Cookbook" - Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service. Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory.
•	Author of "Tableau Desktop Certified Associate: Exam Guide" - This book helps to develop your Tableau skills and prepare for Tableau certification with tips from industry experts.

•	Author of "Tableau Cookbook 2019.x" - This book has over 600 pages and includes over 115 recipes that help you to master Tableau Desktop, Prep, and Server.
•	Author of "Mastering Business Intelligence with Microstrategy" - This book is intended to master the craft of Business Intelligence using the best BI platform and discover the full potential of Microstrategy 10 in order to increase performance and improve the user experience.

•	Author of "SAP Lumira Essentials" - Learn data discovery techniques, build amazing visualizations, create fantastic stories and share these visualizations through an electronic medium with one of the most powerful tools – SAP Lumira. 

•	Author of "Learning Hunk"​ - Explore, analyze and visualize Big Data in Hadoop or NoSQL data stores with powerful full-featured big data analytics platform Hunk. 

•	Reviewer of "Learning Tableau 2019"​
•	Reviewer of "Mastering Tableau 2019.1"​
•	Reviewer of "SAP BusinessObjects Reporting Cookbook"​
•	Reviewer of "Creating Universes with SAP Business Objects"​
•	Reviewer of "Learning SAP Business Objects Dashboards"
•	Reviewer of "Splunk 7 Essentials 3rd Edition"

Author & Technical Reviewer

Apress

Jumpstart Snowflake A Step-by-Step Guide to Modern Cloud Analytics. This book is about modern analytics in cloud data warehouse age. It covers cloud analytics fundamentals and highlights modern analytics requirements. The book helps you to start with Snowflake and explore unique features and architecture. and learn more about the modern analytics ecosystem. You will review the analytics solution architectures and migration/modernization scenarios.

Author

Wawanesa Insurance

Toolbox: Tableau, Microstrategy, Oracle, Erwin Data Modeller, Informatica PowerCenter

Data Engineering & BI

Forex Club

DI toolbox: Tableau, Amazon Redshift, Postgresql, Pentaho DI, Tealium, DMP, Klipfolio, Adobe SiteCatalyst, Google Analytics, Adjust

Data Engineer

Teradata

• Delivered technical concepts, designed & developed of BI/ETL/DWH, Splunk, Web Analytics, Campaign Management, Data Science, BigData Analytics
• Designed and Implemented BI architecture and developed the full life cycle of the Business Intelligence part of the corporate BI solution for 1,000+ end users

Data Engineering Leader with 14 years of experience working in Business Intelligence, Data Warehouse & Data Integration, BigData, Cloud, and ML space across North America and Europe.

Leading Data Engineering initiatives, working on a petabyte size data platform built using cloud and big data technologies for supporting machine learning experiments, data science models, business intelligence reporting, and data exchange with internal and external partners. Handling privacy compliance and security-critical data sets.

Apart from work, I am teaching a Cloud Computing course at the University of Victoria, mentoring high school students at CS faculty, and volunteering my time for coaching people with analytics engineering skills in the CIS region. Moreover, I am the author of analytics books and speaker at data related conferences and user groups.

Data Engineering and Analytics Chief Instructor

Surfalytics = Surfing + Analytics. Inspired by West Coast surfing spots and Pacific ocean vibes. Created with goal to help you start a new career in data analytics space, develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus to deliver significant value to businesses in the analytics realm as well as help to get fair compensation for the work you're passionate about.

There are several key producs:

1. Membership in community with the goal to land the job, prepare for the interview and get best offer. Every Saturday we do a Data Engineering and Analytics projects and discuss our progress on the journey.

2. Career consultations from Dmitry with focus on
2.1. Land 1st job in data
2.2. Move from analyst to engineer
2.3. Promote from middle to senior and beyond

3. Free course based on my 14 years experience in Data Engineering and Data Analytics

🚀 Apache Spark 4.1 is Here!

The latest Spark release focused on faster streaming, smarter pipelines, and enhanced PySpark! Here are the highlights:

Key Features ⚡

🧰 Spark Declarative Pipelines (SDP)
⚙️ Define what you want, let Spark handle the how. No more manual dependency management, parallelism, or retry logic. Write in Python or SQL and let the framework do the heavy lifting.

🚄 Real-Time Mode for Structured Streaming
⚙️ Sub-second latency is now official! Achieve single-digit millisecond p99  ⚙️ latency for stateless queries. Just flip a config—no code rewrite needed.

🐍PySpark Improvements 
⚙️ Arrow-native UDFs/UDTFs for zero-copy execution
⚙️ Built-in Python worker logging for easier debugging
⚙️ Filter pushdown for Python Data Sources

🛂 Spark Connect Enhancements
⚙️ Spark ML is now GA with intelligent model caching, compressed execution plans, and support for larger datasets.

📊SQL Maturity 
⚙️ SQL Scripting is GA with loops, error handlers, and cleaner syntax
⚙️ VARIANT data type with shredding: 8-30x faster reads on semi-structured data
⚙️ Recursive CTEs for hierarchical queries
⚙️ New KLL and Theta sketches for approximate aggregations

Available now on spark.apache.org and Databricks Runtime 18.0!

Staff AI Data Engineer | Data Infrastructure for AI | Data Engineering & Analytics System Design Community | 8x tech book author

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