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

The hill I’ll die on:
“Natural language → SQL” will never — I said NEVER — replace analysts.

Every few months someone announces the same miracle:
“We cracked it — business users can query data in plain English now!”

And every time I think:
Great. We solved the part of analytics that was never the problem.

SQL was never the bottleneck, and typing isn’t the job — thinking is.
Most companies don’t struggle because nobody knows how to write a JOIN.
They struggle because nobody knows what question actually matters, or how to connect it to the business reality they’re living in.

Give someone a “natural language → SQL” tool and here’s what happens:

→ They ask vague questions.
→ The model confidently answers the wrong thing.
→ And everyone walks away thinking the data is broken.

The truth is that if analytics fails, it fails long before the query — at the decision, the framing, the context, AKA the part AI can’t do for you.

This tech won’t replace analysts. It just exposes how much of analytics depends on real judgment, not syntax. If anything, AI is about to make analysts more valuable — because someone still has to think clearly.

→ Follow Nick Valiotti, PhD for the data takes that won’t waste your time.
♻️ Repost if you’re tired of seeing “natural language SQL” demos pretending to solve a thinking problem with a typing feature.

Nick Valiotti, PhD

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

Hard pill to swallow:

Excel was never the problem.
Dependence on Excel was.

Excel is brilliant.
It’s flexible, familiar, and absurdly powerful for its size.
But here’s the trap: the better Excel works, the more you rely on it. And the more you rely on it, the harder it is to grow beyond it.

Every company reaches that moment:
→ one file becomes 12
→ 12 becomes 40
→ 40 becomes a folder called “final_v7_really_final.xlsx”
and suddenly nobody knows which version is the truth

This isn’t a tooling problem. This is an infrastructure debt problem. And it compounds silently.

In my book “Your Fractional CDO” (Amazon: https://lnkd.in/dtvr7Y9Y) I break down exactly why spreadsheets become dangerous at scale — and what to do before your dashboards, KPIs, and decisions start contradicting each other.

Because the solution isn’t to “ditch Excel.” The solution is to stop using it as your entire data strategy.

Here’s where companies should shift their focus:

1 — Centralize your sources. Stop exporting CSVs like you’re throwing confetti. Put your data in one place — Sheets, a small warehouse, whatever. One source of truth beats ten perfect reports.

2 — Assign ownership. If everyone is responsible for the data, no one is responsible for the data. Quality only scales when accountability does.

3 — Automate the pipes, not the panic. Connect tools directly. Even Excel works beautifully as the front end if the plumbing behind it isn’t a disaster.

4 — Measure less, decide more. 50 KPIs don’t make you data-driven. A small set of metrics that the business actually uses — does. Excel absolutely has a place in a modern stack. Just not as the backbone of an entire company.

Tools don’t create clarity. Frameworks do. Excel should absolutely stay in your stack — just not at the center of it.

So fix foundations before foundations fail you.
And get your copy of Your Fractional CDO — unless you truly enjoy living dangerously with formulas that break when a butterfly sneezes.

Hot take we need to normalize in 2026:

Data engineering is not a one-time project.

Because here’s the truth nobody wants to admit:
Pipelines will break.
Schemas will drift.
APIs will betray you.

That’s not incompetence, that’s the nature of living, breathing systems.
The job isn’t to build a perfect stack and vanish into a Jira ticket sunset.
The job is to own it, watch it, and fix it before the business even knows a fire existed.

Great data engineers understand:
→ Code will fail — your detection speed is the real skill.
→ Models will drift — your adaptability is the differentiator.
→ Requirements will change — your shipping velocity decides your career.

This is why “set and forget” always becomes “set and regret.”

If you want trustworthy data, you need:
1 — Monitoring that screams before Slack does
2 — Engineers who debug like surgeons
3 — A real process for data contracts, not a Notion wish

Because data is infrastructure.
You’d never build a bridge and say, “ok, good forever 👍”
Don’t do that with your pipelines either.

Repost if a schema change has personally victimized you at 2am.
Follow me Nick for unfiltered takes on data engineering, data strategy, and keeping your pipelines from spontaneously combusting.

This is the best dashboard I’ve seen this week.
Not because it’s perfect — it’s not.

But because someone clearly gave a damn.
Most dashboards feel like spreadsheets that went to design school and dropped out.

This one by Alice McKnight, MPH one actually tries to communicate.

1 — The layout breathes
No clutter. No panic. Every KPI has space to exist — to be seen, not shouted.
That’s rare. Most dashboards feel like PowerPoints after a bender.

2 — The hierarchy makes sense
Big picture up top. Details below. You can tell Alice actually thought about how someone reads, not just what they read.

3 — The numbers talk, not mumble
They’re big enough, clean enough, and balanced enough to be noticed without screaming “LOOK AT ME.” That’s confidence.

4 — Context lives where it should.
There’s a data source, update date, and even the developer’s name.
That’s how trust is built — through quiet transparency, not endless disclaimers.

5 — It’s not flawless — and that’s fine.
Some visuals take a second to decode.
A table assumes you know what you’re looking at.
But that’s the good kind of friction — the kind that says “almost there,” not “run away.”

6 — It tells a story, not a status.
You don’t stare at numbers — you read them.
Every element works toward one message: clarity.

Great BI design isn’t about decoration. It’s about earning trust.
Every chart, number, and title whispering the same story, in the same tone, to the same person.

If your dashboards can’t explain themselves to a stranger, they’re not finished yet.
Save this for your next design review

Fractional CDO | Founder @ Valiotti Analytics | Data Strategy & Consulting for Modern Teams | PhD & MA in Analytics

Best dashboard I’ve seen this week.

For one simple reason — it doesn’t scream, it breathes.

This one by Anastasiya Kuznetsova is one of those rare Tableau projects that feels considered instead of stitched together at 2AM.
Here’s what stood out to me:

1 — The layout is clean. Ruthlessly so.
KPI block on top, two neatly separated tables at the bottom — nothing fights for attention.
There’s air. Space. Respect for the reader’s eyes.
(You’d be surprised how rare that is.)

2 — The first KPI section is genuinely clever.
The distribution visual is tidy, intuitive, and lands the message fast.
Only gripe: red numbers on a red background = Where’s Waldo: Dashboard Edition.
Easy fix for an otherwise strong idea.

3 — The remaining KPIs? Thoughtful as hell.
Side-by-side visuals make the comparisons stupidly easy.

4 — The left table is the star of the show.
Not your typical range chart — more informative, more intentional.
Switchable dimension, clean values, no useless axes cluttering the view.
Honestly: love it.

5 — The right table is simple, pretty, and functional.
Bars inside rows always look good when done right.
Switchers add flexibility without blowing up the layout.

And here’s why dashboards like this actually matter:

A while ago, inside one company, we ran a deep-dive attrition study. Months of surveys, HRIS exports, interviews, features, models… everything. And the biggest predictor of someone quitting?

A sudden, unusually long vacation.

Not salary. Not performance. Not even manager relationships. Just a long break — the classic “I need to clear my head” PTO that HR loves to ignore.

Dashboards like this help you see those signals before they turn into resignations on your desk.

Overall? It’s polished, structured, and built with care.
A dashboard that respects logic, not just decoration.
Every element feels like it belongs — and that’s the kind of design that scales, teaches, and actually gets used.

→ Follow me for more breakdwons like this.

A very important message — and some of you need to hear it twice:
Stop downgrading data analysts into IT interns.

If your analyst is fixing printers, resetting passwords, or babysitting dashboards nobody opens… congratulations: you’ve just burned the most strategic hire in your business. Because analysts aren’t “report people”, neither are they back-office technicians, nor 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 analysts are isolated under IT, here’s what you get:
→ Numbers with no story
→ Dashboards with no decisions
→ “Insights” nobody uses

But put an analyst in the room where decisions are made, and the whole machine shifts:

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

That’s what an analyst is for: not maintenance, not admin — but IMPACT.
If your analysts don’t understand the business, they don’t produce value. And that’s on leadership, not them.

—

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

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

“Data analytics is so difficult”

No it’s not, you just need to know these basics:

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.

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.

3 — Dashboards That Don’t Suck
Make it visual. Make it clear. Make it clickable.
Tool doesn’t matter.
That’s how you get decisions, not headaches.

That’s the starter pack.
No enterprise fluff. No shiny $10K toys.
Just a stack that works.

Now, this has been a mega short rundown. If you want details, foundations, and basics, my book “Your Fractional CDO” is for you.

Order on Amazon: https://lnkd.in/dDzQC3MJ

It shows you how to:
→ choose metrics that won’t betray you 
→ build a stack you won’t rebuild in 6 months 
→ think about data the way operators think about business

If this post was the appetizer, the book is the full meal — without the enterprise-priced bill.

Most “dashboard problems” I get hired to fix,

have nothing to do with dashboards.

They start with a CEO wondering why Stripe says one thing, their BI tool says another, and the cohort table sits in the corner pretending nothing is wrong.
Then someone quietly admits they’ve been “double-checking everything in Excel… just to be safe.”

That was exactly the case with one of our clients at Valiotti Analytics. They were not fighting the dashboard. They were fighting the system underneath it.

Broken foundations.
KPIs built on guesswork.
Two databases that didn’t speak the same language.
Refund logic that erased churn.
ETL scripts nobody remembered writing.

By the time dashboards started disagreeing, the real damage already happened months earlier — inside the pipelines, definitions, and identity logic that nobody had time to untangle.

This week’s Datapreneur is about how we fixed it and why it mattered.
If you’ve ever found yourself reconciling metrics manually… or trusting your gut more than your dashboards… you’ll want to read this one.

→ Issue 24 — “What Really Happens When You Skip Data Foundations”: https://lnkd.in/dSm8Ydud

I’ve just released a book that will save you a lot of money.
It’s called Your Fractional CDO.

It’s a sort of 101 to data analytics for founders, CXOs, and anyone who participates in decision-making.

And while it explains technical concepts in plain language, the book also includes a separate section on data roles — for a reason.

Your first data hire can either accelerate your business or quietly waste months and money. Not because they lack skill, but because every role solves a different layer of the system — and there’s a natural order to building your team.

So here’s a quick rundown:

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

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

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

Data Scientist — forecasts, experiments, advanced modeling. Only useful when the basics above 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 they’re needed, what problems they solve, and the risks of hiring them too early or too late.

And I also share a simple formula for assembling your first data team, based on the stage of your business and the kind of decisions you’re trying to make.

So if you’re hiring your first (or next) data person, read this book first.
Your Fractional CDO is out now on Amazon: https://lnkd.in/dh7xP34x

You hired a CDO and nothing changed,

because one person can’t fix an 8-person problem.

Most data disasters don’t come from bad talent.
They come from asking one person to do eight different jobs.

Here’s what it looks like when roles are wrong:
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.

Each department has its own dashboards, none of which reconcile.
Everything is busy… and nothing is reliable.

Here’s what it should look like:

Data Engineers move data from your sources into your database/warehouse — clean, accurate, automated. If they do their job well, you never think about pipelines again.

Data Architects decide how all systems connect, where data lives, and how everything scales.

BI Developers build the interfaces: dashboards, reports, visuals — they’re the ones who turn tables into something teams can actually use.

Data Analysts provide meaning. They look at the numbers, explain what’s driving them, and tell you what decisions those numbers support.

ML Engineers turn patterns into predictions. Churn risk, lead scoring, recommendations — anything that forecasts the future lives here.

This is the basic setup. AI adds a couple more roles in the mix like AI Engineers for LLM integrations, Prompt/LLM Engineers for optimizing the use if AI, and AI Product Minds to decide where to add AI at all, but that’s a whole new level.

Think of it like a relay:

One role prepares the data, another structures it, another presents it, another explains it, another automates it, another embeds it into product.

When each team carries the right baton at the right time, everything works.

And this is exactly where a Chief Data Officer comes in. A CDO creates the system: who owns what, what gets built first, and how every part ladders up to revenue, product, and growth.

Without that leadership, you get activity. With it, you get outcomes.
If your team is scaling and you want this system built properly, let’s talk → https://lnkd.in/dzGjJgWM

This is how you do dark mode —

without turning your dashboard into a rave.

Most “dark mode dashboards” just go black = done and call it innovation.
This one? Actually built for low-light decision-making.

Here’s why it works:

→ Strong visual hierarchy — clean cards, deliberate contrast
→ Red as the accent — great for night use (yes, your eyes love red light)
→ KPI cards feel alive — that legend toggle is chef’s kiss
→ Filters + toggles speak the same UI language — no drift, no noise
→ Waterfall on tab two — intuitive, readable, fast
→ Tooltips — finally doing something useful instead of restating labels

In short: dark theme done right.
Subtle. Functional. Designed to be used — not just admired.

Amazing work, Pradeep Kumar G!

Nobody’s using your dashboards.

And it’s your fault.

Here’s why it happens — and how to fix it fast (read on for a free cheatsheet):

1 — Trust isn’t there (your numbers ≠ their numbers)

They’ve been making calls off a hand-rolled sheet for years. You ship a shiny dashboard, skip real QA/reconciliation, and it doesn’t match their file. Trust drops to zero.

Fix: reverse-engineer their sheet. Document every calc, filter, and caveat. Run the dashboard side-by-side for a few cycles, show a reconciliation tab, and agree on a single source of truth. If their sheet is wrong, prove it with traceable logic — politely.

2 — It’s usable… for you, not for them

Excel/Sheets let them scribble notes, add helper columns, and drop quick charts. Your dashboard stripped those workflows away.

Fix: interview them. What questions do they answer daily? What shortcuts do they use? Recreate the flow: default filters that mirror how they think, prebuilt “answers,” and fast paths for the 3 decisions they make most.
And if they want a table... just do the damn table. 

3 — You ignored the stakeholder journey

That report doesn’t live in a vacuum. It’s forwarded to investors, pasted into decks, compared with finance, exported for ops.

Fix: map the journey (inputs → decisions → outputs). Who touches it, when, and why? Build for the full loop: export formats that survive copy/paste, drill paths for board slides, and snapshots that audit clean.

Do these three and adoption stops being a prayer and starts being process. Otherwise, you didn’t build a tool, you shipped art.

And if you want the exact template I use to run dashboard discovery sessions with clients — it’s yours, free → https://gum.co/u/nrnjlnqa
🤠 Save now, thank me later!

Data isn’t magic.
But if LinkedIn is Hogwarts… a lot of wizards walk among us.

Every day on LinkedIn, someone breaks down a concept, simplifies a workflow, or explains a tool in a way that suddenly makes everything click.

It’s collaborative, generous, and strangely close to what a modern Hogwarts would look like — minus the robes and floating candles.

Here’s how I see today’s Hogwarts Faculty of Data:

Transfiguration — Data Engineering
The ancient art of turning raw chaos into clean, structured data.
Professor: José Siles
He’s been posting DE spells for so long that half of our pipeline discipline is basically his homework.

Flying — Pipeline Optimization
Airflow, but make it aerodynamic.
Professor: Benjamin Rogojan
His content is basically: “Here’s how to keep your broom from crashing mid-air.”

Charms — Data Analytics
SQL, dashboards, insights — the everyday spells.
Professor: Donabel Santos
Her posts are the closest thing we have to Wingardium Levio-SQL.

Defense Against the Dark Arts — Data Governance
Keeping your company safe from metric inconsistencies, rogue definitions, and shadow spreadsheets.
Professors: Charlotte Ledoux & John Wernfeldt
Blocking curses like:
→ KPIs that contradict each other
→ Definitions that shapeshift every quarter
→ “We thought Finance owned this metric?”

Care of Magical Creatures — Data Leadership
Where analysts learn how to survive messy data, stranger decisions, unclear ownership… and the occasional fire-breathing timeline.
Professor: John Cook
His content is basically a field guide to keeping analysts alive:
how to ask the hard questions, push back gently, spot bad logic early, and protect your team from the kinds of “creatures” that wander into meetings pretending to be business requirements.

Muggle Studies — Explaining Data to Non-Data People
The noble work of translating dashboards into human language.
Professors: Morgan Depenbusch, PhD & Andy Kriebel
Their content is the bridge between:
“I don’t get this chart” → “Oh… THAT’S what it means.”
The Muggles absolutely love them for it.

If you were hiring at Hogwarts…
Who else belongs on the list? Tag them in the comments!

I wrote a book.

Unlike AI, it might actually replace me.

A few years ago I realized something obvious but uncomfortable:
There wasn’t a single book that explains analytics to business people in a way that actually makes sense to business people.

There were tons of technical books — great ones, even. But for a founder, a CMO, a COO, someone running a real business, most of that material was unusable.

So I wrote “Your Fractional CDO”.

It’s not a technical manual. It’s a decision-making manual.
Because most people still think a CDO is the person who “knows how to count things correctly.” While CDO is actually the one who:
→ Knows when a metric is lying
→ Understands the cost of fixing vs. the cost of ignoring
→ Picks the system that gets you to ROI fastest — not someday
→ Connects marketing, product, ops, finance, and sales into one coherent story
→ Prevents decisions that look smart in dashboards but kill you in reality

And this is exactly what this book helps you see: context, judgement, trade-offs; how business reality collides with data, and how to actually make decisions with the thing you worked so hard to measure.

It won’t promise you a “data culture.” It will help you solve the one problem slowing your business down right now. And honestly, that’s what most leaders actually want.

If you want to support me:

→ Grab the Kindle edition (it’s free if you’re on Kindle Unlimited): https://lnkd.in/dKbMg8fp
→ Or just repost this so more founders, CXOs and whoever might need it see it

And a quick note: print-on-demand edition goes live soon — hardcover + paperback, with reviews on the back from some of your favorite data creators: José Siles and John Cook. 

Designed to be highlighted, dog-eared, and carried around like a field manual.

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/d_tq_kGb
Let’s make dashboards people actually use.

There’s just one difference between an AI product that fails and one that grows.
And it’s not the AI.

Right now we’re living through the noisiest tech bubble since crypto and dot-com.

Investment dollars are flying into AI companies not because they’ve built something groundbreaking, but because everyone hopes they might someday.

And in this environment, every founder eventually thinks: “Maybe I should build an AI product too.” So the market fills up with thousands of tools that all look identical. 

Some raise millions before they have a single real user. But here’s the part no one wants to admit: if you add AI without adding product discipline, your AI becomes a liability, not an advantage.

The products that survive this hype wave won’t be the ones shouting “AI” the loudest. They’ll be the ones that:

→ measure what actually drives acquisition & retention
→ understand real user behavior
→ iterate based on data, not vibes
→ know where AI helps — and where it breaks
→ build a feedback loop around the model, not treat it as a miracle

So AI doesn’t fix a weak product. AI exposes it.

This week’s Datapreneur issue dives into a real case where analytics didn’t just analyze an AI platform — it reshaped how the AI sells, behaves, and improves over time.

If you’re building AI right now (or funding it), this one is worth your time.
Full story → https://lnkd.in/dcNF3TWi

The hill I’ll die on:
Your company doesn’t need to be “data-driven”.

You need the one fix that removes today’s bottleneck. And make sure it doesn’t occur again.
Might be a report you rebuild every week, a KPI no one can calculate twice the same way, a funnel that breaks every time you need it, or a dashboard that looks great but tells you nothing. You name it.

You just want one thing: an exact solution to the exact problem setting everything on fire as you read this.

That’s why I wrote Your Fractional CDO. Not as a manifesto, but as a practical first step for founders who need clarity fast — and don’t have six months to waste wondering what they actually need.

Because once you peel back the noise, most “data problems” come down to a few simple truths: → Metrics break when no one agrees on their definitions. → Dashboards lie when systems don’t tell the same story. → Your first data hire can quietly save you a fortune — or quietly burn it.

The book exists to help you spot these traps before they cost you time, money, and momentum.

→ Get the book: https://lnkd.in/drHQsQgK
Because you don’t need a data revolution.
You just need your next decision to be right.

They say being a data analyst is about SQL and dashboards.
In reality, it looks more like this:

1. Drink a giant mug of coffee
2. Management asks to “fix something” in the dashboard
3. Spend 40 minutes decoding what they actually mean
4. Drink another coffee
5. Fix it
6. Hear: “It looked better before”
7. Inhale deeply, consider moving to a monastery
8. Three back-to-back standups that could’ve been one email
9. Breakfast at 4 PM — the most spiritual meal of the day

Data skills matter.
But caffeine and patience are the real tech stack.

Smash Repost if you’ve ever whispered “SELECT * FROM sanity” into your mug.

Some dashboards fight you.

This one actually wants you to understand it.

I spent a good half-hour playing with this insurance portfolio dashboard — and honestly, it’s one of the rare cases where you can feel the designer thinking.

Here’s what stood out immediately:

1 — The layout breathes.
Clear spacing, clean separators, nothing screaming for attention.
Just calm, intentional design — the kind that makes a CFO exhale.

2 — A tiny detail that changes everything:
The settings-style legend on the left. It’s subtle, elegant, and genuinely helpful.

And the wild part? Every chart using that arrow icon supports cross-filtering — and it takes exactly two seconds to figure out.
That’s good UX.

3 — KPIs with personality.
Each metric comes with its own deviation indicator — maybe slightly duplicated across the board, but it works.

Feels neat, approachable, almost “mobile-app clean.”

4 — Tooltips, source labels, developer credits.
This is what “designing for trust” looks like.
People forget that credibility is a UI element too.

5 — Benchmarking page? Thoughtful, but not perfect.
Some spacing inconsistency.
Sparklines don’t need Y-axes — they’re meant to whisper, not lecture.

But the idea of darkening the selected quarter? Love it. Not 100% intuitive, but intuitive enough.

6 — And then the BROKERS view hits you.
Super-light bars with subtle borders — aesthetically beautiful.
A different stylistic choice from the first sheet… but honestly, it works. Clean, airy, almost Scandinavian.

Overall? This dashboard has a soul. Simply because someone cared enough to make every interaction just a bit smoother than you expect.

If more dashboards were built this way, we'd have fewer “what am I looking at?” meetings and more actual decisions.

Great work, Ellen Blackburn!

Let’s be honest: running a marketing agency is like always being on trial. 
Every action and every dollar has to prove it wasn’t in vain. 

Good reporting can do that.
Bad reporting makes everybody look confused.

At Valiotti Analytics we worked with a marketing agency that was managing reports for 70 clients — all updated by hand.
Download. Paste. Fix. Repeat. By the end of each month, nobody trusted the numbers anymore.

They weren’t maintaining 70 reports, they were repeating one broken process 70 times.

So we tore it down and rebuilt it from the ground up:
→ automated data collection
→ standardized logic and definitions
→ one dashboard with secure, trackable client views

That’s when the magic happened: reports refreshed automatically, formulas never broke, and the team got 80% of their time back to actually think — not just maintain.

They didn’t just improve reporting — they improved how the whole agency learns.

If you’ve ever looked at a dashboard and thought,
“Cool… but what am I supposed to do with this?”
you’ll want to read this one.

Dive into the full story → https://lnkd.in/dyPVMC2M

DataShip

Saved 56 hours per week in dashboard manual updates by building an automated ETL pipeline in Python that synced data from multiple Google Sheets into structured Looker Studio dashboards.

Saved thousands in accounting fees with a real-time Looker Studio dashboard on GCP that automated daily bonus payouts.

Identified $3,000 verified in bonus payouts surfaced by a conversion tracking framework that connected mentee milestones to incentive rules, improving payout accuracy and leadership confidence in program data.

Reduced average time-to-interview for job-seeking clients by 1 week by optimizing funnel tracking, which expanded sales team capacity to enroll new clients and scale revenue.

Improved attribution accuracy by deduplicating events across systems, ensuring mentor bonuses and client progress metrics were tied to verified, business-critical outcomes.

Marketing Data Analyst

LendingClub

- Used Python to automate data cleaning for misspelled text-based user-input for a 2 million row dataset, cutting data-cleaning time and increasing data accuracy by 24%

- Used SQL to create reports on financial KPIs like Debt-to-Income Ratio, Default Rate / Charge-off Charge-offs by Loan Grade / Risk Tier, and Weighted Average APR, enabling leadership to assess business health in real time.

- Performed Customer segmentation analysis to identify borrowers with positive CLV to optimize marketing team optimize ad targeting.

- Created interactive data visualizations with Python's Pyplot & Bokeh for stakeholder assess exposure to risk.

- Performed Credit Risk analysis to show Borrowers to reveal which income buckets had the most and least chance of defaulting.

- Diagnosed inefficiencies in the underwriting pipeline and flagged missing predictive variables using statistical correlation analysis.

- Recommended reallocating marketing spend towards these high-density states to improve customer acquisition efficiency and lower CAC.

Growth Analyst - Freelance

School of Public Policy & Administration, Carleton University

Used Excel and R to process, clean, and analyze a dataset of over 1,600 observations from the 2011 New York Housing Vacancy Survey.

Designed and executed a study to assess the impact of affordable housing policies in New York City, leveraging the 2011 New York Housing Vacancy Survey.

Used Partial Linear Machine Learning Model to show a 59% decrease in rents in rent-controlled housing compared to non-rent-controlled housing, highlighting the impact of rent-control policed in New York.

Delivered insights to stakeholders on rent stabilization, highlighting its benefits and trade-offs to guide housing policy decisions.

Tech Stack: Excel, Stata, R, Data Visualization, Quantitative Analysis, Double Debiased Linear Regression, Statistical Analysis, Statistical Inference, Supervised Machine Learning for Research.

Quantitative Analyst

Used Excel & Stata to create data visualizations to help analyze the relationship between unemployment and inflation in the United States spanning over 50 years (1970-2023).

Demonstrated the flattening of the Phillips Curve, indicating a weakened relationship between inflation and unemployment, thus limiting the effectiveness of expansionary fiscal and monetary policies to reduce unemployment during recessions.

Presented findings to professors and peers in the economics faculty, contributing to discussions on the evolution of monetary policy and its impact on unemployment and inflation.

Research Analyst

Elite Traders University

Performed A/B test for email subject lines for Financial education program resulting in an increase in Click Thru Rates and open rates by 30-50% leading to an increase in booked calls for sales team.

Designed and executed a marketing strategy in 10 days for a new financial education digital product that generated $1988 in revenue in 15 hours after performing market research on our ideal customer avatar.

Developed a re-engagement email sequence campaign to boost open rates. After implementing re-engagement email sequence open rates went from 10% to 17%

Non-Skiils: Copywriting, Email copywriting, Content writing, Sales, story telling, presenting insights.

Abubakar Al-faki

Helping Digital Marketers increase their Bottom Line, by focusing on closing inbound leads on Sales calls on High Ticket Offer. Allowing them to focus more on improving and running their business.

Further Qualifying leads to see if they are a good fit for your offer so you get more committed clients and higher retention

High Ticket Closer™

Scale your ad ROI by at least 10% using Google's Conversion Tracking system. (GT4 + GTM + stape.io)

I help you match sales from their CRM/payments processor to your ads managers data using Google's Conversion Tracking System, so you know which ads to scale.

Connect & DM me to book a free consult.

𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗱𝗼 𝗳𝗼𝗿 𝘆𝗼𝘂

𝗕𝘂𝗶𝗹𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗵𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺
→ Daily refreshed Looker Studio reports pulling data from Ads Manager, Shopify, or CRMs automatically without manually exports or imports.

𝗗𝗲𝗳𝗶𝗻𝗲 & 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗸𝗲𝘆 𝗲𝘃𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝘀 𝗶𝗻 𝗚𝗧𝗠 + 𝗚𝗔𝟰 (e.g., Product Page view, page scroll, add to cart, checkout, purchase).

𝗦𝗲𝘁 𝘂𝗽 𝗮 𝗦𝗲𝗿𝘃𝗲𝗿-𝗦𝗶𝗱𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗚𝗧𝗠 + 𝗚𝗔𝟰 𝘄𝗶𝘁𝗵 𝗦𝘁𝗮𝗽𝗲.𝗶𝗼  
→ Match sales across platforms, deduplicate, and apply clear attribution rules so you know which channels are driving the most profit.

𝗨𝗻𝗶𝗳𝘆/𝗯𝗹𝗲𝗻𝗱 𝗮𝗱 𝗺𝗮𝗻𝗮𝗴𝗲𝗿 + 𝘀𝗮𝗹𝗲𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗢𝗻𝗲 𝗟𝗼𝗼𝗸𝗲𝗿 𝗦𝘁𝘂𝗱𝗶𝗼 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱  
→ Combine Google Ads, Meta Ads, and Shopify/Stripe data for accurate ROAS, CAC, and LTV in your dashboard, so you know which ads and channels are actually driving profitable Customer acquisition.

𝗙𝗶𝘅 𝗯𝗿𝗼𝗸𝗲𝗻 𝗼𝗿 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴  
→ Troubleshoot GA4, GTM, and ad platform mismatch in KPIs so you can trust your numbers.

The post iOS14 has made marketing data across platforms inaccurate because people don’t accept cookies.

When you work with me, we can 
- Fix Mismatch in sales & conversions across ad platforms.
- Lower your CAC by stopping ads that aren't profitable.
- Increase your ad ROI by at least 10% using Google Conversion Tracking System.

𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗜 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵
- E-Commerce & DTC Brands
- Digital Products & SaaS
- Info Products

𝗦𝗸𝗶𝗹𝗹𝘀
- Marketing Data Analytics
- Data Visualization in Looker Studio
- ETL/ELT with Python
- Conversion Tracking & Attribution
- GTM + GA4
- Server-side tracking with Stape.io

𝗧𝗼𝗼𝗹𝘀
Python • Looker Studio • Google Sheets • Excel • SQL  • GA4 • GTM • Google Cloud Platform

Most Linkedin Info product creators don't track these metrics

The last metric costs them sales and higher conversions

1. Free-to-Paid Conversion Rate
     -- How many of your free tier customers becoming paying customers
     ----- This allows you to how many Free tier customers u need to get 1 paying customer.

2.  Sales Page Conversion Rate (Landing Page %)
% of visitors who land on your sales page and buy.

3. Linkedin Post Conversion rate
    -- How many people that follow your CTA to your sales page, booking page convert to Paying customers, email subscribers etc

There is currently no easy way to track Point Number 3

And I would argue it is one of the most important metrics to track and optimize.

LinkedIn is your Top of Funnel.

it's the Engine that feed tour business with leads and customers.

Not optimizing this is costing you money.

- Not all posts will generate the same revenue and conversions.
- More views doesn't necessary equal More Money.

In some cases posts with less views make you more money.
Because it speaks more to your ideal customer than "viral" posts.

I am currently thinking of building a Guide on "How to Track Linkedin Posts that make you the most money"

If a 100 creators comment "Track",  I will make the guide.

Hate updating client data because it means losing the old version?

I was working with a client that runs a data mentoring program who wanted to build a Bonus Payout System that processes upto $42,000 a month.

When a new client is Onboarded the client can have more than one mentor
    - One mentor to help with Technical Skills
    - One Mentor to help with Interview Skills

This is a One-to-Many relationship
    - One client can have multiple Mentors.

To compound the problem, a client can request a different mentor at any point in time.

These changes are not recorded and backed up. They are just overwritten.

This means no historical record of changes to mentors of any client.

There is a bonus called "Placement Bonus", which is given to the Mentor who works which the client till they land a job.

If both mentors worked with the client till placed, then we give the bonus to the mentor who worked with the client the longest.

Without keeping a historical record of changes to a clients mentor, we won't know who worked with the client the longest.

The Solution is to build an SCD Type 2 (Slowly Changing Dataset)

When a clients' mentors are changed we keep the old row and create a new row with the updated mentor columns.

This allows us to see the full history of changes in mentors for client.

So we can see which mentor worked with the client the longest and give payout the bonus automatically.

This process is All Automated.

Automated using an ETL pipeline hosted on Google Cloud Platform.

Google Cloud Platform allows you essentially rent a computer to run an ETL or anything based on a schedule or real time.

The use cases for building SCD Type 2 are numerous

- Detect when a user’s engagement starts to drop → trigger reactivation campaigns.
- Detect when customers become “inactive” and trigger reactivation campaigns.
 - Build customer lifecycle models: New → Engaged → Buyer → Promoter.
See how long it takes for a Customer to move from your Free to Paid Products.

I love building these type of automated Data Models for clients.

It saves so much time and you get a historical record of behaviours or statuses of your clients you want to track.

Costs depend on the Size of your Dataset.

For my Client costs less than $2 a month.

If you want a system like this implemented for you 
Comment "Update" and I will reach out to you.

taplio

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The Battle

Nikolay Valiotti

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Posts

Engagements

Likes

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

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