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

Scott spent 3+ years on Upwork.
Only earned $100.
Sounds familiar?

It’s the same story for most freelancers.
Not because they lack skills — but because their profile hides them.

When I opened Scott’s profile, here’s what I saw:

— Headline that read like a vague job title
— First lines full of buzzwords
— His best result ($500K saved for a client) hidden halfway down
— No portfolio for proof

Clients never saw the value!!!

So I rebuilt it from the ground up:

→ Headline that says what he does (and for who)
→ Results right at the top
→ Clear call-to-action to boost invites and messages
→ Portfolio and proof front and center

Now his profile tells clients in seconds: “This is the person who can solve my problem.”
I turned this process into the 𝗨𝗽𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗙𝗶𝘅 𝗞𝗶𝘁 showing the 8 mistakes most freelancers make (and exactly how to fix them).

Want it? Drop “𝗙𝗶𝘅” in the comments and I’ll DM you the link.

♻️ PS: Know someone stuck on Upwork for years? Tag them — they’ll thank you later.

Nick Valiotti, PhD

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

When a dashboard actually works, you can feel it.
This one nails the balance between clarity, depth, and storytelling.

Overview →

Sales, profit, orders, returns — all right there, with sparklines and % change against the reference period. 

I love how quickly you can scan and get the pulse. 

The “reference vs current” logic is clear, though the screaming red exclamation marks could chill out a bit — no need to yell at me when profit’s down 2%!

Still, the visual consistency (bars vs lines) makes trends instantly obvious.

Products →

Deep dive done right. Pick a product → boom, you see sales, YoY % change, trend over time, and even a scatterplot showing its place against every other SKU. 

That’s how you give context. 

The only hiccup? Selection UX. That blue dot should toggle off automatically. Small thing, but it matters when you’re using it every day.

Customers → 

My favorite tab. RFM segmentation is not only there — it’s digestible. 

Recency, frequency, monetary visuals actually show where your customer stands inside the distribution. 

Add in demographics and lifetime value, and you’ve got a single pane of truth for customer intelligence.

𝗢𝘃𝗲𝗿𝗮𝗹𝗹: it’s one of those rare dashboards that doesn’t try to be pretty for the sake of it — it tells a story.

Yes, some details could be smoother, but the foundation is rock solid.

Big respect to the designer, Ervin Lloyd Vinzon — this is a dashboard you actually want to open every day.

Made with Tableau.

Here’s why an exec will always pick a spreadsheet over your shiny data system.

Because most dashboards are built for show, not for speed.
Executives don’t want “a single source of truth.”

They want to know:
→ What just happened?
→ Why is this number off?
→ What should we do next?

And they want to know it right now.
No loading times. No filters. No guessing if the data’s even correct.

That’s why spreadsheets still rule the room:
they feel fast, familiar, and fully under control.

I’m not saying data systems cannot provide the same experience.

But if your “data system” can’t match that?
It won’t get used — no matter how beautiful it looks.

Build for clarity, not complexity.
That’s how you get adopted.

Forget the AI bros for a second!! The most valuable content in my feed doesn’t come from hype — it comes from people who make data actually useful.

Dashboards that tell stories.
Frameworks that cut through the noise.
Takes that make you rethink how you communicate numbers.

Here are 10 creators I keep coming back to for dataviz, BI, and storytelling inspo:

Anastasiya Kuznetsova — sharp dataviz takes on how to make data genuinely insightful.
Morgan Depenbusch, PhD Depenbusch — consistently dropping thoughtful perspectives on data storytelling that stick with you.
Aurélien Vautier Vautier — clear, practical content on how to design dashboards that actually get better.
Andy Kriebel — the Tableau legend. Decades of consistency and a library of tutorials you’ll actually use.
Roman Bunin — non-trivial frameworks and smart commentary on building BI and making visuals matter.
Donabel Santos — real talk on analytics with hilarious, sharp takes about the perks (and pains) of being an analyst.
Kobe Wastiels — priceless guides on every stage of dashboard building, from requirements gathering to picking the right color palette.
Gilbert Eijkelenboom — bridging psychology and data, with advice on how to communicate numbers so people actually get it.
Kat Greenbrook — thoughtful deep dives into the craft of data storytelling, always backed by examples.
Brent Dykes — one of the originals in this space. His work basically put “data storytelling” on the map.

👉 Follow them. Steal their ideas. Let them raise your bar.
Because a dashboard without a story is just a pretty picture.

Most dashboards try to impress with charts.

This one impresses with usefulness.

→ Navigation is clean. You can jump between the funnel and a flat table view without feeling lost.
→ Filter presets are a joy — one click and you’re in the right range. (Pro tip: add a free-range date picker and you’ll cover 100% of use cases.)
→ Sorting? Genius. Those little circles above the columns that flip the order instantly — simple, but it makes the whole thing feel alive.

Even the small touches stand out:
– Sparklines that actually add signal.
– Tooltips with extra detail.
– An instruction panel that onboards new users (though I’d hide it in table mode to keep it cleaner).

Not everything is perfect (the notification bell feels like a ghost feature right now; also questionable font choiсe for the “Welcome” title), but here’s the point:

This isn’t dashboard-as-art.
It’s dashboard-as-a-tool.
And tools are meant to be used.

Follow me Nick Valiotti, PhD for more reviews like this one.

And great job, Seoyeon Jun!

When I first joined Upwork, I thought the hard part was done. PhD in Data Science. Corporate track record. Full stack of skills. And still — silence.

My proposals got ignored. and clients would always choose someone else.
The turning point for me was when I stopped treating my profile as a cv… and started treating it as a sales page.

That shift landed me the project that actually became the backbone of my agency:
We worked for over 3 years, and with just one that contract I made almost $300k.

Not because I suddenly became “more skilled”, but because I finally looked hireable at first glance.

So if I know something about Upwork, here’s the first mistake that holds most freelancers back:

If your title says “Data, BI, ML, dashboards, ads, Shopify…”, you disappear.

Because clients don’t hire toolboxes. They hire the obvious specialist for a specific problem.

My title also used to be generic af until I fixed it with this formula I see work every time:
[Role] | [Outcome/Tool] for [Who]

So let’s say you specialize in marketing data analytics, then you can go for “Marketing Data Analyst | Tableau ROI Dashboards for Ecom & SaaS”

Or you’re a PBI dashboard geek and mostly work with DTC. Then try “GA4 + Power BI Attribution for DTC Brands”

It seems like nothing but this tiny shift makes clients think: “This person gets it.” And that’s when they click “Invite.”

This is actually just one mistake I see Upworkers make. I counted 7 more and put them with actionable plan to how you can fix them into my Upwork Fix Kit. Plus the exact formulas I used to turn a silent profile into one that pulled $500K+ in revenue.

Comment "fix" and I’ll send it to you.

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
8. Three back-to-back standups
9. Breakfast at 4 PM

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

Dear founders,
Want to burn $250K this year? Build a warehouse, buy a BI platform, and hire a data scientist.

You don’t need a data warehouse.
You don’t need a BI platform.
And you definitely don’t need to build your own ETL.

As Slawomir Tulski put it when we chatted: it’s so easy to end up with overkill setups: multiple tools, millions spent, and complexity nobody needs. More than 90% of businesses could run on a single instance.

Here’s what to actually do instead:

→ At 0–10 people:
Implement Google Analytics. Track UTMs. Focus on a few key metrics, not dashboards.

→ At 10–20:
Use built-in reporting features of your SaaS tools. Make sure marketers own GA. Measure NPS.
(Still too early for SQL-based infrastructure.)

→ At 20–50:
Set up infrastructure. Depending on your business model, this could mean hiring a strong generalist (mix between DA and DE) or a data engineer first.
One source of truth is enough. Don’t write your own ETL. Don’t hire a data scientist unless ML is your product.

→ At 50–100:
Migrate from basic web analytics to event tracking (e.g. Snowplow).
Implement SQL-based modeling. Start attribution and some forecasting.
Push your warehouse + SQL as far as possible before adding complexity.

→ At 150+:
Introduce data testing. Use PRs and code review. Take documentation seriously.
Think carefully about your analytics team structure — scaling requires a motivated, talented crew.

I mapped the full analytics journey in one image:
Tools, hires, and common mistakes — stage by stage.

Comment if you’d like this in high-res — after 20 comments, I’ll add the Miro link to the post.

Follow me Nick Valiotti, PhD Valiotti for no-BS frameworks on data, growth, and scaling.

I’ve had quite a few new connections lately, so here are a few facts to help you get to know me better:

1 — I’ve been passionate about data analytics for over 15 years

2 — Back in 2014, I defended a PhD on neural networks in business

3 — I currently live and work in Cyprus — building something exciting under the sun

4 — I’m passionate about tennis, especially being on court myself

5 — While I was still in corporate, I tried on the Marketing Lead role, and quite successfully, all thanks to data

6 — I’ve tried launching a few businesses:
• A craft brewery (still operating!)
• A specialty coffee e-commerce store

7 — In 2017, I shifted focus to custom software dev — did 4 projects, learned a ton, and realized it wasn’t quite my thing

8 — That’s when a major pivot happened: I returned to what I know best — consulting in data and analytics

9 — Over the past 6 years, I’ve scaled a data consultancy — highs, lows, and all the in-between. We’ve worked with SaaS, FinTech, Ecom… but till now the main fraction of our projects is marketing analytics

10 — These days, I focus on vision, ideas, and building my own team — I’m less hands-on, but still deeply in love with the data space. 

Deeply, to the point where I analyze my 2 children’s habits in their first years of life and build a dashboard about it — let me know in the comments if you want me to share it!

With my team we deliver full-circle analytics services from data audit and consulting to setting up data infrastructure, internal data culture transformation, and advisory for AI-readiness.

Tell me about yourself in the comments — always happy to connect!

Started with Tableau — clean dashboards for C-level. 
But department heads needed something faster, cheaper, and more targeted. 

So we moved everything to Superset.

Not a big deal.

Once you’ve spent enough time aligning dashboards with how the business actually thinks, the tool stops mattering that much.

The filters, dimensions, KPIs — they don’t come from Tableau or Power BI. They come from understanding what the company is really trying to do.

And once you’ve got that, switching tools is just figuring out where the buttons live. Same logic, new interface.

That’s the part people miss.
They treat dashboarding like a technical skill — “How well do you know X?” But the real leverage comes from knowing what to track, how to structure it, and when not to show something.

Because no matter what tool you use, the hard part is the same: Getting to something that people actually trust — and use.

The rest is syntax.

Do you agree?

Follow me Nick Valiotti, PhD for all things analytics

“Let’s align on KPIs.”
Translation: we have no idea what we’re doing but let’s burn 90 minutes anyway.

Throwback to when Gilbert Eijkelenboom and I made the Data-to-Business Glossary — aka the Rosetta Stone of corporate BS.

Because let’s be honest:
Your biggest data challenge isn’t SQL.
It’s surviving the word salad.

A reminder of the classics:

Dashboard → A temporary band-aid slapped over structural chaos.
Data Warehouse → The dusty basement nobody checks until the pipes burst.
SQL → The language that ruins weekends and friendships.
KPI → A semi-arbitrary number chosen because it looked sexy in Q1.
Pipeline → A duct-taped miracle that “just works”… until it doesn’t.

Let’s stop pretending jargon is strategy.
Swipe through, laugh, cry, and tag your favorite data translator before they rage-quit Slack.

----------------
♻️ Repost if you’ve survived a 90-minute “alignment” that ended with “let’s take this offline.”
Follow me Nick Valiotti, PhD for more survival guides from the data trenches.

Data-to-Business Glossary

We helped an EdTech startup grow revenue from $50K to $1.2M in 12 months. Here’s exactly how we did it. And how you can steal the system.

At launch, they had what most companies have:

- 10+ data sources, zero trust in any of them
- Marketing, Sales, and Product data scattered across CRMs, sheets, chats, and memory
- Endless manual tables

What changed?
At Valiotti Analytics, we built a real analytics system from scratch using the same framework that now lives inside our Merketing Metrics Miro board.

What we did:

1. Identified the right metrics
Before picking KPIs, we asked: Where’s money leaking? Which audiences convert best? Which channels to scale? What’s driving refunds?
Every metric tied to a business decision — no vanity numbers.

2. Centralized the chaos
Moved 10+ sources into BigQuery, automated via Airflow. Daily sync for ad spend, CRM, LMS, payments, refunds. Clean, queryable, trusted.

3. Built dashboards for humans
Dashboards told stories: ROMI & CR by funnel, filters by region/audience, campaign AOV, lead quality, channel ROI vs. cost.

4. Scaled with the business
From 2 dashboards to a full system, evolving as new questions came up.

The analytics system grew with them — not against them.

A couple of these dashboards are now part of the board.
We built it for ourselves. Now it’s yours.

To grab the board, comment “revenue”.
DM me if you want help tailoring it to your work

5+ years. 50+ projects.
Millions in business impact.
All kicked off with the same doc.

Most analytics projects don’t fail because of “bad tools.”
They fail because of misalignment.

A vague goal → turns into a vague dashboard.
Unclear scope → turns into $50K of rework.
No structure → turns into no adoption.

Here’s how I flip that:

→ Define the goal. Not “we need a dashboard.” Say “we need to cut churn by 10%” or “find the channel with highest LTV.”
→ List the right questions. What decisions will this data support? What’s unknown today?
→ Map the source of truth. Which systems actually hold it? Is it clean, complete, and accessible?
→ Clarify roles. Who owns delivery? Who approves? Who uses it?

At Valiotti Analytics, we’ve baked this into one repeatable system — a project brief template we’ve used to launch every client engagement.

One doc. Zero guesswork.
It’s the difference between “wait… what are we building again?” and projects that actually deliver ROI.

I’ve dropped a real anonymized example in the image below so you can see how structured it gets, even from a short 30-min call.

Comment “brief” if you’d like a free copy.

The last expensive lesson I learned on Upwork cost me $475.
Here's the story:

We had a client — let’s call him John.
Task: analyze stats for his psychology clinic.

We worked 70 hours, delivered everything (even reworked models he “found somewhere”).
On the final call, John said: “I love it, thank you.”
I closed the contract.

Next day: long angry message. He decided 70 hours should’ve been 40–50.

→ Filed a refund for 3 hours — fine, I returned it.
→ Then threatened: “Refund me $1,000 more or I’ll leave negative feedback.”

That’s when I opened a dispute.
Result: I gave him $475 (~10% of the project). He didn’t leave bad feedback.

Our Job Success Score stayed clean.

Lesson:
On Upwork, feedback > money. One bad review can cost 10x more than any refund.

→ Track hours in detail.
→ If a client “renegotiates” after delivery, that’s a red flag.
→ Sometimes giving up 10% is smarter than protecting your ego.

Would I do it again? Yes — but I’d catch the red flags before 70 hours.

Follow me Nick Valiotti, PhD for more Upwork survival tips. Repost this — future you will thank you for the cheaper therapy bill.

 Problem Upwork Clients 101

Real dashboards aren’t about looking pretty.

They’re about telling the right story.

This one’s for Mentorshow — one of the fastest-growing EdTechs in Europe.

We built it together to track what actually matters for revenue:
→ Pre vs Post Refund Revenue
→ Rebill logic (often overlooked, but crucial)
→ Refunded € vs. Retained €
→ New clients, unique orders, and the full MRR/ARR ladder
→ A view that even your CFO would call “calm”

Nothing fancy. Just structure that helps teams zoom in and out — from strategy to daily ops.

We didn’t design this to impress. We designed it to work.
To make churn visible. To spotlight growth levers.
To help people ask better questions and make faster calls.

That’s what we think a dashboard should do.

Built in Tableau by our team at Valiotti Analytics.

Used daily by folks who don’t have time to dig through 12 tabs to find the right number.

If you’re scaling and your dashboards don’t feel this clear — DM me right now.

Power BI always demos well. 
Shiny visuals, endless options, “the flexibility to do anything.”
But when you start building in it, a few realities hit fast:

1 — Windows first.
If your team runs Macs or a mix of systems, Power BI becomes clunky. VM installs, workarounds, extra costs — not exactly “modern.”

2 — Microsoft lock-in.
From creation to sharing, you’re tied to Microsoft authentication. Works great if your company is 100% Office. If not, it feels like swimming upstream.

3 — Flexibility with limits.
Yes, every chart has 20 settings… but often not the ones you want. Fonts are rigid. Progress bars? Buy them in the marketplace. Want a horizontal bar with a line? Doesn’t exist.

4 — Mindset risk.
A lot of teams approach Power BI like Excel. Instead of scaling analytics, they recreate heavy spreadsheets with prettier visuals. It looks good — but doesn’t move decisions forward.

5 — Basics can be inconsistent.
Tables render differently on different monitors. Gridlines shift. Layouts break. Even the fundamentals don’t feel predictable.

6 — Hard to migrate.
The deeper you go into DAX and custom reports, the harder it is to move away. Many teams only realize they’re locked in when it’s too late.

Power BI isn’t a bad tool. It’s powerful, but also opinionated.
If you don’t have the right setup and culture, it can create as many headaches as insights.

♻️ Repost if your team has hit these walls.
→ Follow me for more no-BS takes on data, dashboards, and the tools we love to fight with.

You're a sharp data pro. Solid career, real results.
But on Upwork? Zero traction. 

This is what Scott had, just like hundreds freelancers. 

I audited his profile and immediately saw why:

- Headline read like a CV, not a service
- Overview opened with buzzwords instead of business value
- Crucial element of the profile is absent
- Best proof point (saving $500K for a client!) was buried halfway down, never seen

In short: his skills were invisible where it mattered.

I rebuilt it section by section:
→ Clear niche
→ Results up top
→ All vital sections in place
→ Call to action instead of vague “let’s connect”

The difference is night and day. His profile now tells clients in 5 seconds: “This is the person who can solve my problem.”

That revamp became the basis for the Upwork Profile Fix Kit — a guide to the 8 most common mistakes I see (and the exact fixes to apply).

Inside, you’ll find:
1. Scott’s “before/after” as a real-world case
2. My own copy-and-paste formulas for titles, descriptions, portfolios
3. A checklist so you know your profile is client-ready

I usually do this kind of profile teardown in 1:1 consulting calls.
This kit gives you the same playbook but 10 times cheaper.

How to get it:
1. Leave a comment “Fix”
2. Send me a connection request if we’re not already connected

I’ll send the link to your DMs.
Your profile shouldn’t undersell you.
Make it work as hard as you do.

It’s easy to think: “Sure, Nick can get clients — he has $500K earned and 98% Job Success.”
But it wasn’t always like that.

When I first joined Upwork, my profile killed my chances.
It read like a CV, buried results, and gave clients no reason to trust me.

Everything changed once I rebuilt it from scratch:

✅ Defined niche + tools in the headline
✅ Clear service offer right at the top of overview
✅ Actionable CTAs that led to invites and calls
✅ Detailed proof of work in portfolio

That profile setup became the foundation for everything that followed — $500K earned, 3k+ hours, and long-term clients.

I realized most freelancers still get stuck making the same mistakes I made in the beginning.

So I created the 𝗨𝗽𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗙𝗶𝘅 𝗞𝗶𝘁 showing the 8 mistakes that keep freelancers invisible (and 8 battle-tested fixes for you to apply right now).

Want it? Drop “𝗙𝗶𝘅” in the comments and I’ll DM you the link.
♻️ PS: If you know another freelancer struggling to get traction on Upwork, tag them — they’ll thank you later.

Your data stack shouldn’t be a museum of logos.

 It should be a recipe with five ingredients.

Yet I see the same story play out:
Teams buy 15 tools, burn 7 figures, and still can’t answer the simplest question — 
“Are we on track?”

Here’s the truth: 90% of businesses don’t need an “enterprise” stack.
They need something light, fast, and good enough for 80% of decisions.

If I had to launch from zero in 2025, I’d keep it brutally simple:

1. BigQuery
2. Fivetran (or Airbyte)
3. Dataform
4. Looker Studio
5. Airflow (later, when orchestration actually matters).

That’s it.

Five jars on the counter. 
Week 1: clarity. Everything else can wait.

That’s the spirit of this week’s Datapreneur. 

I also pulled in sharp takes from others who hit the same theme:
 – Dylan Anderson — on why AI fails without foundations
 – Slawomir Tulski — on why resisting SQL is like refusing to learn handwriting (and one really important insight from our latest talk)
 – Patrick Jacolenne — on why governance is the quiet backbone of data

Full breakdown + one-week blueprint via the link in the first comment. 

Because clarity beats completeness.
Numbers get noticed.
Stacks that drive action get used.

Most dashboards fail.
Not because they’re ugly — but because they don’t drive action.

I’ve lost count of how many “spaceship dashboards” I’ve seen: dozens of charts, filters, tabs… and zero real decisions being made. They look great in a demo. They make teams feel busy. But when the next budget review rolls around? Nobody opens them.

The pattern is always the same:

1 — Built backward → Teams start with data sources instead of decisions.
2 — Vanity overload → Dashboards are filled with numbers that feel thorough but don’t map to outcomes.
3 — Contradictions and gaps in spend tracking → CAC from Meta ≠ CAC from Stripe ≠ CAC from HubSpot.
4 — No linkage → Costs and revenue live in separate tabs, so ROI never makes it into the conversation.

Dashboards that don’t shift budgets are just decoration.

What works instead?

I dive deep into this in the latest issue of The Datapreneur — with a full client example and the 5 core KPIs every marketing dashboard needs. But here’s the TL;DR:

→ Frame dashboards as decision tools, not just reports. If it can’t directly tell you where to cut, scale, or double down, it’s not useful.
→ Anchor on 5 core metrics: CAC, LTV, conversion rates, CTR (as relevance proxy), and spend vs. revenue.
→ Structure with the 3-layer rule: Top-level KPIs → dynamics & trends → drilldowns.
→ Test adoption: Ask, “What decision did we make last week because of this dashboard?” If the room goes silent, it failed.

And I’m not alone in this. Some of the sharpest voices in data shared takes that hit the same nerve:

And it’s not just me saying this — some of the smartest people in the space are pushing in the same direction, so in this week's Roundup you'll find:
1 — Donabel Santos on designing for people, not for @ Dribbble.
2 — Aurélien Vautier on how adding features can’t fix weak foundations.
3 — Sebastian Hewing 🚀 on why great delivery ≠ strategic influence.
4 — Dr. Sven Balnojan on dashboards as corporate theater.
5 — John Cook on how the ugliest dashboard he ever built still runs the business 7 years later.

The punchline?
Beautiful dashboards win design awards.
Useful dashboards win budget wars.

Full breakdown in the newsletter (link in the first comment).

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