Smith School of Business At Queen's University

As part of the program advisory board, I work with my fellow board members to ensure that the program is delivering learning that is current, up to date, and relevant to current business needs. Our board provides support and advice to other academic programs, assists in the development of additional content and identifies best practice standards.  We serve as ambassadors to the programs, providing a connection while facilitating the exchange of information and ideas with industry peers and multiple global bluechip companies. 
   
☛ SPECIALTIES: 
Analytics | Leadership | Strategy Executive | Machine Learning | Artificial Intelligence | AI | Digital Supply Chain | MBA | Speaker | Loyalty Program Specialist | Motivational Business Speaker | Future Technology Advocate & Speaker | Digital | Research | Speaker | Keynote Speaker

Advisory Board - Master of Management Analytics & Master of Management Artificial Intelligence

International Institute for Analytics

The International Institute for Analytics (IIA) is the authority on analytics maturity and best practices. Co-founded by Tom Davenport and CEO Jack Phillips, IIA works across a breadth of industries to uncover actionable insights from its network of analytics practitioners, industry experts and faculty.  IIA are the leading source of expertise for companies making the transition to data-driven decision-making and advanced analytics, offering four unbiased, client-oriented services designed to help organizations across the entire analytics lifecycle from ideation, through strategy and planning to operationalization.

IIA's Research and Advisory Network provides clients with access to the largest network of independent analytics expertise, allowing them to apply real practitioner insights against initiatives, projects and problems.

As a Senior Executive and Advisory Associate, I help companies and organizations in North America and Europe improve their bottom-line, building analytic presence, creating pricing, and analytic models through insights and recommendations which support decision making by integrating primary and secondary research and analysis, using multiple data sources and relevant proven methodologies. 

Through planning, analytics research, evaluation and data strategy, I assist in the creation and implementation of yearly strategic plans.  Build and sustain Business Improvement (SBI) and programs, including creating a new performance management strategy for optimum results. 

My goal is to be a trusted partner and to mentor and modernize the overall analytic environment, based on my evaluation of the client's business and technical requirements.  


☛ SPECIALTIES:
Analytics | Leadership | Strategy Executive | Machine Learning | Artificial Intelligence | AI | Digital Supply Chain | MBA | Speaker | Loyalty Program Specialist | Motivational Business Speaker | Future Technology Advocate & Speaker | Digital | Data Science | AML  | Speaker | Keynote Speaker

Faculty & Advisory 

Loblaw Companies Limited

☛ Head of Enterprise Business Information Competency Centre (BICC) @Loblaw,  Canada's largest retail company:

Responsible for enabling revenue growth, operational efficiency & risk avoidance through data innovation, data strategy, data monetization, data governance.

☛ Scope included: Customer Loyalty & Analytics, Digital Channels, Pharmacy & Healthcare, Retail Product & HR data.

Analytics Lead COE, analytics strategy, and drove culture change, executive alignment and adoption, and key technology investments.

☛ Led the creation and development of the inaugural Loblaw Analytics Certificate program, in partnership with Queens University Analytics- to-date, 70+ Loblaw colleagues have completed the program.

☛ SPECIALTIES:
Analytics | Leadership | Strategy Executive | Machine Learning | Artificial Intelligence | AI | Digital Supply Chain | MBA | Speaker | Loyalty Program Specialist | Motivational Business Speaker | Future Technology Advocate & Speaker | Digital | Supply Chain / FP&A | Speaker | Keynote Speaker

Senior Director Finance, Enterprise Business Information Competency Centre  & Loyalty

☛ Led the financial reporting, planning and analysis function for the $1B+ Loblaw Distribution and Supply Chain business

Led the creation of 5-year Financial Plan ensuring division alignment and supported the strategic plan including conducting sensitivity analyses to determine impact of changes to underlying assumptions on annual and strategic plan.

☛ Successful collaboration with Senior Operators across 20+ distribution sites for Warehouse, Transportation, and Replenishment – including external third-party providers.

Presented financial results and forecasts to Senior Management.

☛ Lead the FP&A team in providing financial support of the Total Supply Chain business through detailed, insightful commentary on financial results and forecasts; ensuring the Operations teams and Senior Management are connected to the financial processes and the division's financial performance; achievement of consistent 2-3% forecast accuracy rate.

☛Managed the long-range planning process for the Supply Chain business.

Standardized the financial reporting and forecasting process across the network; resulting in efficiencies with various financial and analytical processes.

Financial lead for network transition projects.

☛ SPECIALTIES:
Analytics | Leadership | Strategy Executive | Machine Learning | Artificial Intelligence | AI | Digital Supply Chain | MBA | Speaker | Loyalty Program Specialist | Motivational Business Speaker | Future Technology Advocate & Speaker | Digital | Supply Chain / FP&A | Speaker | Keynote Speaker

Senior Director FP&A, Supply Chain, Central Analytics, Loblaw Digtial, Customs

Support colleague engagement and development.
 
☛ Consistently achieved team engagement survey scores well above the business’ national average.

Providing members with opportunities and exposure to areas within Supply Chain that are outside their normal scope of duties.

☛ Maintain a strong relationship with all team members, through open and honest communication based on mutual respect.

Several mentoring relationships within the organization; succession planning.

Director,  Financial Planning & Analysis, Supply Chain, E-Commerce, TMS 

Supply Chain Management Inc. (a Division of DHL/Exel)

Exel is a North America 3PL leader, providing customer-focused supply chain solutions, resources and expertise. With a range of solutions that include: consulting, design, integrated information management, e-commerce support, and warehousing and distribution services. With more than 40,000 associates, 80 million square feet of warehouse space and annual revenue of over $4.5 billion, Exel is delivering value across the supply chain for several of the world’s largest and well-known brands

Senior Manager Financial Planning & Analysis

FANUC Robotics

FANUC America Corporation provides the most complete range of industrial automation equipment including robots, CNCs and motion control systems. FANUC’s innovative technologies and proven expertise help manufacturers in the Americas maximize efficiency, reliability and profitability. Markets served include: Aerospace, Agriculture, Assembly, Automation, Automotive, Construction, Education, Energy, Fabricating, Food, General Motion Control, Machine Tool, Material Handling, Material Removal, Medical, Metal Fabrication, Metalforming, Mold & Die and Woodworking.

Advisor

#1 Most Followed Voice in AI Business (1.5M) | Leading AI Entrepreneur & Advisor | Former Amazon, IBM | LinkedIn Top Voice | @alliekmiller on Instagram, X, TikTok

AI isn’t magic. It’s method.

Most executives don’t need more AI hype.
They need real-world results.
Fast.

I'm Achille Ettorre.
TEDx speaker. Advisor to C-suite leaders.
Author of The Digital Advantage.

I help senior leaders turn AI into ROI.
Not dashboards. Not prototypes.
Actual outcomes.

Where it started:
I scaled a gritty equipment rental company into a process-driven, tech-enabled business.
It taught me how transformation really works — from the inside out.

That experience grounds my approach today:
From back office to boardroom, I’ve helped Fortune 500s, retailers, universities, and governments design AI and data strategies that get traction — not just talk.

What I believe:
AI doesn’t fix broken processes. It exposes them.

Clean data beats big data.

Strategic alignment always outperforms automation.

How I help decision-makers:
✅ Keynotes on AI, analytics & transformation

✅ Board-level advisory & AI audits

✅ Workshops for C-suites and senior teams

✅ Custom training based on The Digital Advantage

Who I’ve worked with:
Clients include DHL, Walmart, Loblaw, top grad schools, and multiple government agencies.
I sit on advisory boards, speak globally, and collaborate with forward-thinking execs building the future — not chasing trends.

Let’s work together if you’re:
→ A CEO, COO, or VP ready to deploy AI for real results
→ Leading transformation and need clarity over complexity
→ Planning an event and want a speaker who simplifies the complex

🎯 Book a strategy call → www.achilleettorre.com
📘 Get The Digital Advantage on Amazon
🎤 Watch my TEDx talk in the Featured section

Achille Ettorre, MBA

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

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

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

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

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

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

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

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

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

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

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

----------

I’m Nick, and that’s exactly what we fix at Valiotti Analytics — building systems where data flows, definitions align, and dashboards actually tell the truth. If that sounds like your next step, let’s talk → https://lnkd.in/dXW44fNi

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.

“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.

Data teams speak SQL. Business teams speak panic on Google Meet.
Somewhere between the two, strategy gets lost.

And, hate to break it to you, it’s never the tool’s fault.

Most data strategies don’t fail because of bad tech. They fail because teams stop listening to each other — or worse, start talking in different languages.

You’ve got reporting on one side, fighting for “insights by Friday”; architecture on the other, building cathedrals of pipelines that no one asked for.
Different goals, different Slack channels, and a confused CEO in the middle asking, “so… what’s our ROAS again?”

That’s when strategy dies — not with a bang, but with a quiet 404.

Here’s what teams that get it right do:

1 — They don’t wire it straight to production just because someone said “real-time.” They ask why it needs to be live, realize it’s just for end-of-day sales commissions, and set a regular refresh that works perfectly and never crashes.

2 — They know beauty means nothing if you can’t compare this year’s revenue to last. So instead of chasing the cleanest schema, they plan for historical accuracy from day one — and future-proof their reports before anyone asks for them.

3 — When reporting says queries are slow and architecture says “it’s not designed for that,” they pause before blaming each other. Turns out the business is testing a new market — so they spin up a sandbox, keep production safe, and move fast without breaking things.

And that’s really the difference between chaos and clarity: not tools, not dashboards, not the number of tables in your warehouse — but connection.
The bridge between people who speak in rows and those who speak in results; someone who understands that “JOIN performance” and “Q4 targets” are part of the same story.

That’s what a Chief Data Officer is supposed to be. Not just a title on the org chart, but the translator, the referee, the bridge. The one making sure data doesn’t just look organized — it actually works for the business paying for it.

Because real data strategy isn’t a tech project. It’s a conversation.
Between priorities, people, and the pipelines that hold it all together.

♻️ Repost if you’ve ever built a “real-time” dashboard that didn’t actually need to be real-time.

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

Hot take:

You don’t need a data team.

Your nephew’s “pretty good at Excel,” right?
He can whip up a dashboard, eyeball some trends, and tell you what’s “probably working.” 

No need for data governance, definitions, or version control — just vibes and pivot tables.

Who needs analysts when you’ve got family?

The most underrated soft skill in BI isn’t SQL.

It’s sales.

You can build the cleanest dashboard in the world — pixel-perfect, automated, every metric blessed by the data gods — and still watch your stakeholder open it, blink twice, and say:

“Cool. But where’s the Excel file?”

Because here’s the truth nobody tells you early enough: every dashboard is a product, and every product needs a pitch. You don’t just “deliver a dashboard”, you sell it.

→ You walk them through the pain (old, clunky tables).
→ You show the gain (a chart that actually tells a story).
→ You demo the new workflow, make them feel the efficiency.
→ You compare the “before” and “after” like it’s a makeover show for metrics.

A dashboard without a pitch is just another tab lost in the analytics graveyard. So if you want adoption, not applause, learn to sell your data work.

Because no one buys insights they don’t understand.
Follow me Nick more painful BI truths (and how to fix them).

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!

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!

The secret to data-driven marketing?

It’s not a secret.

It’s just... not another dashboard.

A CEO of a Shopify-based store once came to me mid-campaign, slightly panicked. Budgets were spent, reports were flying, and her CMO kept telling that all marketing channels contribute to sales equally and they just need more money to reach KPIs.

She said, “We just need one dashboard to make sense of it all.”

Sure.

Because nothing says “clarity” like adding another chart on top of the chaos.
So instead of jumping into tools, I started asking questions:

→ Which marketing channels do you actively use and how do you prioritize them?
→ What’s your definition of a successful marketing campaign?
→ What are the metrics to measure that?
→ How do you calculate these metrics?
→ Is it ok to compare these channels’ efficiency or are they created for different goals and audiences?

Only after defining what’s important did I build the stack: 
Sources → BigQuery → Looker Studio.

And guess what?
Ads looked amazing… until we connected the dots 🙂

Turns out not the ads but email newsletter was driving 4× more sales and loyal customers. Meanwhile, discounts were the real villain, slowly killing profit.

And the dashboard wasn’t the hero of the story for sure.
Clarity was.

If this sounds like your marketing team on a Tuesday, you’ll enjoy the full story — process, stack, and lessons included: https://taap.it/ln-resize1

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.

I was mid-presale call when my daughter came home from kindergarten.
I’d forgotten to lock the door.

She ran in, barefoot, in her underwear, and announced with total confidence:
“I want to work too!”

My client froze.
I apologized, muted the mic, turned off the camera, and asked what kind of work she wanted.
She said, “Real work.”

So I gave her an old keyboard (Bluetooth off, of course).
She sat on the floor and started “typing,” while I kept talking through marketing metrics and data pipelines.

After two glorious minutes she sighed:
“Daddy, I’m tired. Work is boring.”
Door slam. Exit.

And that, my friends, is the shortest employee lifecycle I’ve ever seen.

Parents working remotely, what’s been your funniest “work-from-home with kids” moment?

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

Most data teams don’t need more tools.

They need to learn how to say no.

Because not every request deserves to exist and not every dashboard deserves to be built.
So if you want to keep your team productive (and not burnt out), start with impact-first planning.
The rule is simple: if it doesn’t change a decision or behavior, it’s not a priority.
But that’s where most teams fall apart. Saying no feels political, personal. Risky even.

So they keep saying yes —
to numerous new dashboards, ad-hoc requests, and endless “nice to haves.”
And then wonder why nobody uses their work.

Here’s the uncomfortable truth: a healthy data culture doesn’t measure output, it measures impact.

→ A dashboard that drives one key decision beats ten dashboards nobody opens.
→ A meeting that kills three useless requests saves a week of engineering time.
→ A “no” at the right moment is sometimes worth more than a sprint.

Good analytics isn’t about doing everything faster.
It’s about doing less, deliberately.

I’m Nick, founder of Valiotti Analytics. We help companies build data cultures that move the business — not just the backlog.

Book a call → https://lnkd.in/dg7EVEez → we’ll show you how to prioritize for impact and finally make data feel simple again.

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.

taplio

The Battle

Achille Ettorre, MBA

Nikolay Valiotti

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The Battle

Achille Ettorre, MBA

Nikolay Valiotti

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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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