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

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 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

Fractional CDO | Helping Scaling Tech founders turn data into faster decisions | Founder @ Valiotti 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/dWqxidfk) 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.

Most dashboards show data.

This one supports decisions. 

From a business lens, it works because it ties volume, monetization, and engagement together in one place. A leader can quickly understand:

→ Where demand sits 
→ Where revenue concentrates 
→ Where content investment goes 
→ Whether engagement backs up growth

The subject breakdown isn’t decorative. It helps with portfolio focus. 
The pricing view supports monetization strategy. 
Students vs Reviews and Students vs Lectures help justify where to invest next. 
Paid vs Free anchors the revenue model conversation.

Nothing here feels academic. It’s built for operators who need to decide where to double down — not admire charts.

Great job, Maureen Okonkwo!

If you want to build dashboards people actually use, start before you open Tableau or whatever tool you’re using. Grab my free Dashboard Canvas interview template — it helps you design around decisions, not visuals: https://lnkd.in/dcJGqeWj

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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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Tableau Ambassador || Tableau Featured Author 2024 || 4x #VOTD || Tableau Most Notable Newbie Winner || Tableau Community Highlight || Business Inteligence Analyst || Data Visualization Developer

A lot of funnel dashboards are just progress reports.

This one by Varun J. is a control panel.

From a business perspective, what makes this strong is that it separates volume from efficiency, and shows both at the same time.

You can see how many users enter the system.
You can see how many convert.
And you can see how long it takes.

That matters because growth problems are rarely just “not enough traffic.” Sometimes it’s friction. Sometimes it’s delay. Sometimes it’s a specific step bleeding value.

The time-to-account-opening section is especially smart. Median time, percentiles, distribution — that’s operational insight. A leader can immediately ask:

→ Are we losing people because the process is slow?
→ Is the long tail hurting us?
→ Is speed a competitive advantage?

The time-limited conversion blocks are even more practical. Conversions within 5 minutes vs 10 minutes? That’s product design territory. That’s where small UX changes can move revenue.

And the drill-down by state and age group turns this from a generic funnel into a segmentation tool. Now it’s not just “conversion dropped.” It’s: where and for whom?
That’s how you run experiments with intent instead of guessing.

This dashboard doesn’t just describe the onboarding journey.
It exposes where to intervene.

If you’re building funnel dashboards, start with the questions the business needs answered before you start placing charts.

Data and BI @ Ledger Analytics Engineer and Data Product Developer | 2xTableau Ambassador | IITK ’21 | ESSEC MiM

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

It’s 3 steps and zero BS.

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

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

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

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

That’s the starter pack.

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

----------

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

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

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

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

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

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

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

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

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

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

Hot take:

Data doesn't create value.
Decisions do. Data just makes decisions less wrong.

Most companies I audit have dashboards, a warehouse, analysts, sometimes a full data team. And every meaningful decision still gets made on gut feeling in a boardroom.

The chain isn't broken. It's snapping at the same point every time.
Data → Insight → Action → Measurement

Here's where each link fails:
1. Data → Insight 
Nobody's asking the right questions.  Analysts spend 80% of their time cleaning, 20% analyzing. Stakeholders request "one more cut" of the same report. 
Dashboards show what happened. Never why. Never what to do.

2. Insight → Action 
This is where most companies die. The weekly analytics review happens. Everyone nods. Someone says "interesting." The Slack message with the chart gets read once and forgotten. Nothing changes. This isn't a data problem, it's an org problem.

3. Action → Measurement 
You made the decision. Did it work? Nobody checked. Six months later the same problem resurfaces in a different dashboard. There's no loop back.

The fix for all three is the same: every analysis ends with a specific recommendation, a named owner, and a deadline.

Not "churn is up 15%."
But "churn is up 15% in enterprise due to onboarding failures, restructure onboarding for accounts >$50K, owner: Head of CS, deadline: 2 weeks."

If your data team can't name 3 decisions they influenced this quarter, that’s a decision-making problem, not a data one.

💬 Where does your chain break?
📘 If this hit close to home, I wrote a whole book for exactly this situation. "Your Fractional CDO" is for leaders who know they have a data problem but aren't sure where to start → https://lnkd.in/gaC23sz2

On International Women’s Day, I wanted to highlight a few women in data whose work I always enjoy seeing in my feed.

People who consistently share thoughtful ideas, useful insights, and perspectives that push the field forward.

Morgan Depenbusch, PhD — great examples of how design and narrative turn analysis into something people actually understand.
Clare Kitching — sharp, grounded perspectives on AI and how organizations actually adopt it.
Charlotte Ledoux — one of the voices reminding the industry that governance isn’t bureaucracy, it’s what makes trusted data possible.
Kat Greenbrook — constantly showing how insight only matters if the story behind it is clear and actionable.
Anastasiya Kuznetsova — always clear data visualizations that make complex information accessible.
Mariska van Kan — consistently strong dataviz work and perspectives on visual communication.
Donabel Santos — an incredible voice supporting people building careers in data.
Dawn Choo — thoughtful content on the intersection of data science and career growth.
Hannah Rounds — practical insights from the day-to-day world of data analytics.
Alex Wang — smart perspectives on the evolving AI & data science landscape.

One of the best things about the data community is how much knowledge people openly share. These are just a few voices I’m always happy to learn from.

If there are women in data whose work you enjoy, feel free to add them below so more people can discover them.

Happy International Women’s Day 👩‍💻 👩‍🎓 👩‍🏫 🌿

HR Data Storytelling & Influence → Turn people data into recommendations leaders trust • Corporate trainer & Keynote speaker • Snowflake, ex-Google

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Data & AI Governance Expert 🔎 | Entrepreneur | Author of The Data Governance Playbook | Speaker | LinkedIn™️ Top Voice in AI 🇫🇷

Author of The Data Storyteller's Handbook 📘 Founder of Rogue Penguin 🐧

BI Lead | Tableau Ambassador | DataViz Consultant

Dashboard product designer • I audit, rescue and educate dashboard products • Good Design Award Winner • Power BI & UX

Empowering Data Professionals Through Education | Teacher, Data Leader, Author, YouTube Educator | teachdatawithai.substack.com

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Data, Analytics & AI Leader | Data Governance | Manufacturing & CPG | Get Value From Your Data

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!

Nobody wants to own "data governance".

Why would they?
It's a parade of boring checklists, meetings, and policy docs that nobody reads.

Here's the truth:
Most data governance programs fail because nobody actually uses them. They look tidy in Confluence, but out in the wild, chaos rules. Broken fields linger for months. Data is "owned" by committees where deliverables go to die. Everyone has an opinion, nobody takes action.

If you want traction, skip the dog-and-pony show. Screw the frameworks. Fix the basics.

Start with 3 hard questions:
→ Who touches this data daily, and do they know they're supposed to care?
→ Can anyone remember the last time a policy was enforced, or did it get waved off 'as a favor'?
→ Are you actually tracking outcomes, or just activity for a quarterly slide deck?

Pull one ugly thread. See what breaks. That's where the real problems hide.
Teams don't need another dashboard for governance. They need someone who will show up, call the mess, and fix it when it's right in front of them.

Drop the ceremony, ship actual ownership with clear consequences.
That's governance that works. Otherwise, enjoy your next "policy refresh" meeting.

🔔 Follow me Nick Valiotti for fewer frameworks and more outcomes. 
📘 And for the full approach, check out Your Fractional CDO → https://lnkd.in/ddySGAjR

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.

-------

👋 Hi, I’m Nick Valiotti — 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.

This is what an executive dashboard is supposed to feel like. 

Here’s why this works:

First, the top row does exactly what an executive needs in 15 seconds.
Total Spend. Spend per Vendor. Active Vendors. New Vendors.
You immediately see magnitude and direction. $6.0M total. Down 4.6% vs last year. Vendor count slightly down. No new vendors.

That alone tells a story: consolidation, mild cost compression, no aggressive expansion. You don’t need to hunt for it.

Second, the month-over-month bar chart is clean and comparative.
Current period vs reference period, side by side.
No rainbow colors. No 3D nonsense.

You can instantly spot May’s spike. That’s where a CFO’s brain goes:
“What happened in May?”
That’s the right kind of friction, it pushes conversation.

Third, spend by category is ranked.
Wages at the top. People expenses next. Marketing. Raw material.
This is prioritization.

The business conversation naturally becomes:
Are wages aligned with revenue?
Is marketing spend justified by growth?
Are raw material costs creeping?
Ranking forces trade-offs. That’s strategic.

Fourth, spend by vendor exposes concentration risk.
One vendor at $2.7M.
The next two under $1M.
That’s real leverage.
If I’m running this company, I now know where renegotiation power matters most.

And then there’s the vendor location map.
It’s not there for decoration. It hints at geographic concentration. Supply chain exposure. Regional dependency.
If a disruption hits one of those clusters, what happens to us?

Now, what makes this dashboard strong isn’t just what’s on it.
It’s what’s not.

No vanity KPIs. No metric inflation. No 40 tabs of “just in case.”
This is a spending control cockpit.

If I’m a CEO in this company, I can walk into a board meeting with this and have an intelligent discussion.
If I’m a procurement lead, I know where to focus tomorrow morning.
If I’m a CFO, I see risk, trend, and leverage in one screen.
That’s the standard.

Always great work from Pradeep Kumar G

Crafting data-driven insights and powerful visual narratives. Data Viz Lead | Tableau Ambassador | IronViz Finalist | IIBAwards | 2nd Most Fav Author on Tableau Public

HR data usually answers "what happened." 

This dashboard answers "what do we do next.

Alice Rooney built this Employment Hero dashboard, Tableau's Viz of the Day, and from a business decision-maker standpoint, it earns that recognition.

Here's why it works:

The 288% YoY headcount growth isn't just a vanity metric sitting at the top. It creates a question the rest of the dashboard actually answers: are we scaling well, or just scaling fast?

A leader can immediately check: 
→ Where salary gaps live (avg $68K vs median $55K. That spread matters) 
→ Which departments are carrying performance vs collecting headcount 
→ Whether satisfaction scores hold up as the org grows 
→ If manager layers are earning their comp premium

That last one is subtle but sharp. Sales has the highest median salary. Also the lowest satisfaction score. Operations is the biggest department. Also the lowest performance score. Those two tensions alone are worth a board conversation.

The role breakdown isn't decorative either. When Associates make up 29% of headcount at $40K median and score 5.2 on performance — you've got a workforce composition story hiding in plain sight.

Nothing here feels like it was built to impress a data team. It's built for operators who need to act.

Great work, Alice!

If you want to build dashboards that drive decisions instead of just displaying data, start before you open Tableau. Grab my free Dashboard Canvas interview template: it helps you design around decisions, not visuals → https://lnkd.in/gHBiWM7m

SQL | Data Visualization | Tableau | Data Analytics and Insights

This is what a dashboard looks like when it's designed around decisions, not charts.

Andreea Scintei built this help desk view and the moment you look at it, you stop reporting and start managing.
Here's what it surfaces immediately.

71.7% of tickets are still open. Average 19.3 days without resolution. 32% of tickets have no severity assigned.
That last number is the one that should make leadership uncomfortable. When a third of your workload hasn't been triaged, response time becomes a guessing game and you're dealing with a prioritization process that isn't working.

Then look at satisfaction. 43% unknown.
Nearly half of all support interactions close without capturing a single feedback signal. You think you're managing customer experience. You're actually managing a process gap you never noticed because your dashboard never showed it.

That's what separates this from a reporting screen.
It doesn't just measure activity. It exposes where the system itself is incomplete.

The standard worth holding every dashboard to isn't "does it show data?" It's "does it force a decision?"
If yours doesn't, it's an expensive screenshot.

-----

I built the Dashboard Canvas to help teams design around that question from the start: decision first, then data → https://lnkd.in/du789A95

You can be the best data storyteller in the company and still never be heard.
Hot take: storytelling isn’t the bottleneck anymore. 
Organizational readiness is.

I’ve seen analysts deliver sharp narratives. Clear problem. Clear stakes. Clear next step. No jargon. No fluff.

Everyone nods.
Nothing changes.

For years we blamed this on “poor communication.” So we trained people to tell better stories. And to be fair, they did. The bar is higher now.

But as Brent Dykes pointed out in his piece on narrative tension, resistance isn’t random. It happens when your data collides with the story already living in someone’s head.

If your insight confirms what they believe, you’re a genius. If it challenges a public bet, a KPI they own, or a strategy they championed… you’re suddenly “too simplistic” or “missing context.”

Same data. Different room. Different outcome.

And here’s the uncomfortable part:
Even perfect storytelling can’t override bad incentives.
If bonuses depend on a metric, people will defend the metric. If leaders are punished for bad news, they’ll resist “gut punch” insights. If no one owns the decision, your story becomes an interesting slide deck.

We’ve upgraded storytelling skills. We haven’t upgraded decision culture.
So yes, learn to frame. Learn to read the room. Understand narrative tension.

But also ask a harder question:
Is this organization built to act on truth — or just to listen to it?

-------

👋 I’m Nick. I write about the messy space between insight and action. If you care about making data actually move decisions, follow along.

Author of Effective Data Storytelling | Founder + Chief Data Storyteller at AnalyticsHero, LLC | Forbes Contributor

Everybody talks about "governance" like it's some magic safety net.
But here's the unpleasant reality: if nobody owns the business impact, your governance is hollow.

You can craft all the policies and perform all the reviews you like. Approve the data tables, sign off the ML models, tick every compliance box.
Still doesn't matter.

Because when it comes down to the actual decision, pulling the trigger on something that changes revenue, costs, or risk, that's where responsibility lives or dies.

Slick governance frameworks lull leadership into a false sense of control. Meanwhile, the hard part stays unsolved: 
Who actually holds the bag when the outcomes are messy? 
Who answers for the fire drill, not just the ticket?

This is where most orgs slip. They confuse process for ownership. Accountability gets spread so thin that nothing sticks. A dozen signoffs. Zero accountability. Everybody involved, nobody responsible.

If you want real governance, stop focusing on artifacts and enforcement. Start asking one brutal question: 

who owns this decision when the stakes are high?

Mature companies bake that into muscle memory. Early-stage ones just play paperwork roulette.
Your models are inputs, not absolution. People run the business, people own the risk. Anything else? Corporate fantasy.
Trust comes from knowing whose name is on the line when it matters, not from a template or a checklist.

Keep your framework. But if it doesn't make ownership explicit, you're not protected. You're exposed.
That's the layer most people never build.

------

I’m Nick, Fractional CDO. Follow for practical data strategy that holds up when revenue, cost, and risk are on the line.

Hot take: your company doesn't need a data warehouse.
It needs someone who knows what questions to ask.

I've worked with Series B companies running the full modern stack. Snowflake. dbt. Looker. Fivetran. The works.
Their founders still couldn't tell me what decision the last dashboard actually changed.

Here's the pattern I keep seeing:

Someone sells leadership on "modernizing the data infrastructure" → The team spends a quarter building pipelines → Dashboards multiply → A Monday morning report gets scheduled that nobody reads by Tuesday.

Meanwhile, the actual business questions - should we kill this product line, which customer segment is worth doubling down on, where is the retention leak - stay unanswered.

Because tooling doesn't answer questions. People do.

Signs your data problem is actually a questions problem:

→ You have dashboards but no standing decisions tied to them
→ Your data team gets pulled into requests, not strategy conversations
→ Leadership asks for reports; nobody asks for recommendations
→ You've onboarded three tools in 18 months and clarity hasn't improved
→ Your analysts are busy but you can't point to what changed because of their work

Most companies at Series A/B don't have a data infrastructure problem. They have a prioritization problem dressed up as a tooling problem.

The infrastructure comes second. Always.

The highest-leverage move at that stage is someone who can sit in a leadership meeting, understand what decisions are actually on the table, and build backwards from there. That's what changes the ROI on everything that follows.

More data flowing through better pipes doesn't fix a room that doesn't know what it's trying to decide.

---

💬 Founders: what's the last decision your data team actually influenced? Not reported on. Influenced.

Most dashboards fail for the same reason meetings do: people think they’re talking about the same thing.

Building dashboards is really about building a shared language. Because the dashboard is the sentence everyone reads.
The data stack is the grammar, dictionary, and context that make that sentence mean the same thing to everyone.

Most teams start with charts, filters, or BI tools. That’s backwards.
Here’s the analytics stack, translated into language — in the order it should be built:

0. Question level — intent
What decision are we trying to make? Approve budget, change pricing, pause a campaign?
If you can’t answer that, every chart that follows is cosmetic. Language exists to trigger action, not to sound correct.

1. Source systems — letters
The smallest units of truth.
Users clicked. Money moved. Tickets were opened.
No interpretation yet, just symbols.

2. Ingestion layer — words
How letters become something recognizable: tracking, events, APIs.
If this layer is sloppy, you just get fluent gibberish.

3. Storage layer — the written record
Where words are preserved over time. Warehouses that keep history consistent.
If yesterday’s facts change today, trust disappears.

4. Transformation layer — grammar
The rules for combining words. Joins, aggregations, business logic.
Bad grammar doesn’t just look wrong, it changes meaning.

5. Semantic layer — the dictionary
What the words officially mean: “Revenue”, “Active user”, “Conversion”, “Churn.”
If definitions differ, agreement is an illusion.

6. Analytics layer — the sentence
Dashboards, drilldowns, ad-hoc analysis. This is what people actually read.
A good sentence reduces thinking at the moment of decision. A bad one creates false confidence.

7. Decision layer — action
Where language turns into movement. Operating reviews, alerts, scorecards. If no behavior changes, the sentence didn’t matter.

When teams skip a layer, they don’t get alignment, they get something closer to the Tower of Babel: everyone talking, everyone nodding, nobody truly understanding each other.

Good companies build dashboards.
Great companies build shared language, and make decisions in it.

-------

If this sounds familiar, I turned the framework into a simple dashboard discovery cheatsheet. It’s a ready-to-use Figma canvas you can grab for free → https://lnkd.in/dY4TnMXi

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/dxkdm3nq

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

Attrition isn’t an HR metric. It’s an operating risk.
This dashboard by Anastasiya Kuznetsova treats it that way.

16% attrition. 237 people gone.
No fluff. No engagement score theater. Just the number that matters.

→ Then it breaks the problem down into levers.
Work-life balance. Income. Tenure.
You immediately see where churn concentrates. Lower balance scores. Lower income bands. Early years at the company.

→ Then the role breakdown.
Sales Representatives at 40%. Lab Technicians at 24%. HR at 23%.
That’s revenue risk. Capability risk. Continuity risk.

And the employee table matters more than people admit.
Because when leadership says, “Show me who,” you can.
No hiding behind averages.

What makes this dashboard strong is alignment.
Every visual ties back to one outcome: retention stability.
So it doesn’t try to be clever. It forces the right conversation.

Before you build another dashboard, map the decision. Use my free Dashboard Canvas to get it right → https://lnkd.in/dnRsTHGx

Hot take: dashboards are how data teams avoid executives, not communicate with them.

Building a beautiful dashboard feels like work. It has colors. It has filters. It has a date range selector nobody asked for. You spent three weeks on it. An exec glanced at it for 11 seconds and went back to their emails.

And this is a comfort zone problem.

Data teams are brilliant at building things that let them never have to be in a room defending a number. Dashboards are the perfect alibi.

"It's all in the report" is the data equivalent of "I sent an email"

The leaders who actually move decisions don't hide behind reports, they show up. Here's a quick gut check on where you actually stand:

How exec-ready is your communication strategy?

→ Do you have a recurring slot with at least one C-suite stakeholder — not to report, to think together?
→ Can you explain any metric on your dashboard in terms of a business decision, without looking at the dashboard?
→ Did you shape the question before the last board meeting — or just answer it?
→ When something shifts in the data, do you call someone or update the report?

If you're mostly updating reports, you're dashboard-first.
Dashboards have their place. But if that's your primary channel with the C-suite, you're not being strategic. You're hiding.

Hat tip to Tris J Burns 🔥 for sparking this one.

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♻️ Repost to help someone confusing dashboards with strategy. 
🔔 Follow Nick Valiotti for no-nonsense takes on data leadership and the CDO agenda.

МТС

Data Engineer

СБЕР

Data Analyst

Подружки

Data Analyst / Data Engineer / BI Developer

Мир Авторемонта (ГК Интерколор)

Менеджер по  работе с дилерами

karpov.courses

Обучение по программе Аналитик данных (успешно завершил обучение в Июне 2022 года)
За время обучения овладел следующими знаниями и навыками:
- Python для анализа данных (Pandas, Numpy, SciPy, Matplotlib, Seaborn, Plotly), работаю в Jupyter Notebook и Google Colab
- сложные запросы в PostgreSQL и ClickHouse (SELECT'ы, JOIN'ы, группировка и агрегирование данных, подзапросы, оконные функции, создание/удаление/редактирование таблиц)
- создание дашбордов в Tableau и Redash (создаю дашборды в Power BI и Yandex DataLens)
- продуктовая аналитика (юнит-экономика, сегментация клиентов, метрики DAU, MAU, ARPU, ARPPU, Churn, Retention, CTR, CAC, LTV и др.)
- принятие решений на основании результатов A/B тестирования и проверка гипотез с помощью статистических тестов
- основные понятия теории вероятностей
- базовые принципы работы моделей машинного обучения
- Git и Airflow

За время обучения выполнил два проекта:
1) В промежуточном 'E-Сommerce' проекте провел анализ и ответил на следующие вопросы:
- Количество пользователей, которые совершили покупку только один раз?
- Какое количество заказов в месяц в среднем не доставляется?
- По каждому товару определить, в какой день недели товар чаще всего покупается.
- Среднее количество покупок у каждого из пользователей в неделю
- Когортный анализ пользователей
- RFM-сегментация

2) Цели финального проекта были следующие:
- Написать на Python функцию Retention
- Рассчитать продуктовые метрики, провести анализ результатов А/В теста и сделать выводы
- Предложить метрики на основании которых можно оценить результаты определенных тематических событий в игре

Ссылка на проекты: https://github.com/kovalenko-maksim/karpov_courses_project

Ссылка на один из моих дашбордов в Tableau:
https://public.tableau.com/app/profile/maksim2241/viz/lesson_2_part_2/Dashboard2

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