Valiotti Analytics

At Valiotti Analytics, I lead a team that builds powerful, modern analytics stacks for companies ranging from Fortune 500s to fast-growing startups across EdTech, FinTech, GameDev, eCom, and mobile.
We’ve delivered 40+ data-driven solutions that have:

* Cut infrastructure costs by up to 40%
* Accelerated reporting by 70%
* Boosted client revenue up to 25x

Our end-to-end services cover everything from data warehousing and pipeline engineering to BI dashboards and machine learning.

Tech Stack: AWS, GCP, Snowflake, dbt, Airflow, Kafka, ClickHouse, BigQuery, Redshift, Looker, Tableau, Power BI, and more.

What we do:
✓ Data Strategy
✓ Data Engineering
✓ Data Analytics
✓ Advanced Analytics 

Need to scale your data capabilities? Let’s build something powerful.

CEO & Founder

Founder & CEO, Valiotti Analytics | Better Data Analytics for Data-Driven Companies

🚀 Founded valiotti.com — the go-to data agency for fixing messy marketing analytics.
Top-rated on Upwork & Clutch. Trusted by growth-stage teams to build dashboards, clean up data chaos, and drive faster decisions. Partnered with AWS, Google Cloud, and Salesforce.

Ex-Head of Consumer Marketing at the largest eastern retail company.
I’ve been the one chasing targets with thousand-row tables, slow reports, and siloed data — I know the pain.

Since 2019, I’ve helped marketing and sales teams:
→ Scale revenue up to 25x
→ Save hundreds of hours on reporting
→ Get insights that actually drive action

Let’s talk: https://calendly.com/nick-valiotti

Nikolay Valiotti

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

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

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

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

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

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

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

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

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

Nick Valiotti, PhD

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

Hard pill to swallow:

Excel was never the problem.
Dependence on Excel was.

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

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

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

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

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

Here’s where companies should shift their focus:

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

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

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

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

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

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

Hot take we need to normalize in 2026:

Data engineering is not a one-time project.

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

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

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

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

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

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

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

Best dashboard I’ve seen this week.

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

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

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

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

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

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

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

And here’s why dashboards like this actually matter:

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

A sudden, unusually long vacation.

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

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

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

→ Follow me for more breakdwons like this.

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

If your analyst is fixing printers, resetting passwords, or babysitting dashboards nobody opens… congratulations: you’ve just burned the most strategic hire in your business. Because analysts aren’t “report people”, neither are they back-office technicians, nor support staff for whatever broke today.

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

When analysts are isolated under IT, here’s what you get:
→ Numbers with no story
→ Dashboards with no decisions
→ “Insights” nobody uses

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

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

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

—

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

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

“Data analytics is so difficult”

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

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

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

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

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

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

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

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

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

Most “dashboard problems” I get hired to fix,

have nothing to do with dashboards.

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

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

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

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

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

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

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

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

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

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

So here’s a quick rundown:

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

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

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

Data Scientist — forecasts, experiments, advanced modeling. Only useful when the basics above are stable.

CDO / Head of Data — strategy, architecture, prioritization. The role that ensures data work drives ROI, not noise.

In the book, I break down each role in detail — what they actually do, when they’re needed, what problems they solve, and the risks of hiring them too early or too late.

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

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

You hired a CDO and nothing changed,

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

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

Here’s what it looks like when roles are wrong:
Your “data scientist” is building pipelines. Your analyst suddenly owns AI because “they’re good with numbers.” Your BI dev is trying to architect a warehouse.

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

Here’s what it should look like:

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

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

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

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

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

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

Think of it like a relay:

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

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

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

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

This is how you do dark mode —

without turning your dashboard into a rave.

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

Here’s why it works:

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

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

Amazing work, Pradeep Kumar G!

Nobody’s using your dashboards.

And it’s your fault.

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

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

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

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

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

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

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

3 — You ignored the stakeholder journey

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I wrote a book.

Unlike AI, it might actually replace me.

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

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

So I wrote “Your Fractional CDO”.

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

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

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

If you want to support me:

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

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

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

Most dashboards fail for a simple reason:

They answer questions nobody asked.

We jump straight into charts, colors, and layouts… but skip the one step that actually makes a dashboard useful:
Understanding how people will use it.

Here’s the part so many teams miss:
A useful dashboard isn’t built around metrics. It’s built around decisions.
Before opening Figma, Tableau, Power BI, or anything else — ask your users:

→ What decision do you need to make faster?
→ What is the pain with your current process?
→ What do you consider a “good” or “bad” week?
→ Which slices, filters, or comparisons do you need every time?
→ Which metrics do you actually trust?

If you can get clear answers to these five things, the dashboard basically designs itself.
No more 12-widget monsters. No more “Can we add one more chart?” No more reports that look great… and get ignored.

Because the truth is simple:
Dashboards fail when they’re built for the tool, not the user. Dashboards succeed when they’re built for the decision.

If you want a shortcut:
I put together a Dashboard Interview Cheatsheet — the exact framework I use with clients to turn vague requests into dashboards that actually get used.

Happy to share it: https://lnkd.in/d_tq_kGb
Let’s make dashboards people actually use.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some dashboards fight you.

This one actually wants you to understand it.

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

Here’s what stood out immediately:

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

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

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

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

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

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

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

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

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

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

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

Great work, Ellen Blackburn!

The most expensive problem in marketing isn’t bad ads.

It’s bad data. Here’s how you fix it:

1 — Connect your data sources
Ads, CRM, website, payments, product.
Whether you use Fivetran or Python, the goal is simple: one source of truth.

2 — Store it in a proper warehouse
BigQuery is fast, scalable, and cost-efficient.
Stop asking spreadsheets to behave like infrastructure.

3 — Turn it into insight
Looker Studio, Tableau — doesn’t matter.
A good dashboard answers business questions instantly and clearly.

Three steps. One system.
This is exactly what we build, end-to-end, in under 90 days so you get:

→ Core marketing dashboards — funnel, channel, and executive views built for real decisions.
→ A clean, connected data stack — with all key sources integrated and automated pipelines replacing manual reporting
→ Tools to scale insights — metrics glossary, system map + documentation

And just like that, reporting time drops from 28 hours → 5 minutes, teams stop arguing, leaders get clarity they needed, execution becomes faster and smarter.

See how we do it in detail: https://lnkd.in/dE_c2DcF

This is a quietly confident dashboard. 

And I mean that as a compliment.

First thing you notice: contrast and hierarchy are doing the heavy lifting.
Everything is readable. Your eyes go exactly where they should — KPIs first, then structure, then detail. No visual panic. No “where do I look?” moment.

1. The KPI layer — clean, familiar, useful.
Plan vs actual, deltas vs previous month — nothing exotic, and that’s the point. This is what operators and managers actually need.

One small nuance (and this is a designer-level nitpick):
That plan/actual bar with the target line — the empty space between the bar and the target line made me pause. My brain instinctively expects something there:
→ a faint translucent bar
→ a shaded “to target” zone
→ or a clearer explanation of what that gap represents

Right now, the gap asks a question it doesn’t answer. Not a dealbreaker — just a micro-clarity tweak.

2. The sectioned layout (Hubs / Drivers / Vehicles) — this is where the dashboard really shines.
Big, clear thematic blocks. Each one tells a story, not just numbers:
→ capacity vs throughput
→ experience vs rating
→ active vs maintenance vehicles

And importantly: the “See more” affordance is obvious and honest.
You immediately understand: “I can go deeper here.” No hidden drilldowns, no Easter egg UX.

One basic but important thing:
Left-align the bar labels consistently. That’s table-stakes UX, and fixing it would make the whole section feel even more polished.

Overall verdict — this isn’t a flashy dashboard.
It’s a trustworthy one.

It feels like something a real logistics team could use daily — not just admire in a portfolio. Strong hierarchy, sensible KPIs, clear navigation, and only minor polish left to turn it from “very good” into “great.”

Subtle. Functional. Built for decisions — not demos.
Exactly how operational dashboards should be built.

Great job, Jennifer Eneh!

Let’s be honest: AI and data can feel overwhelming. 
I’m here to make them feel useful, human and possible.

I turn AI and data from buzzwords into capabilities your organisation can actually use.  

Whether you’re starting from scratch or knee-deep in a tricky program, I help you get clarity and momentum.

What I do:

1. Help you set your AI ambition and turn it into practical strategy.
2. Build AI & data governance that’s actually usable.
3. Get teams confident so they actually want to use AI and data.
4. Fix or launch programs so they deliver results, not just dashboards.

My background blends McKinsey consulting, senior leadership roles and strong technical foundations, which means I’m comfortable in boardrooms, war rooms and everything in between.

If you’d like someone who’s pragmatic, creative and energising to work with, let’s chat.
Reach me at hello@cambiq.com

Clare Kitching

Everyone wants AI magic.
Few want to invest in the plumbing.

We live in a moment where the promise of AI
is louder than the reality of data.

I see it every week with teams.

The excitement is real. The budgets are flowing. The pilots look impressive.
But when you lift the lid a different story shows up.

Data that lives in twelve places. 
Metrics that mean one thing in finance and another in operations. 
Ownership that sounds like “we think it sits with them”. 
Governance that feels optional.

And still we keep sprinting toward AI, hoping it will smooth over the cracks.
It never does. It makes them more expensive.

Here is the part no one markets:
AI built on weak data creates more rework, more delays and more organisational friction than leaders expect.

If you want real value, start with the boring foundation:
→ Clear definitions 
→ Reliable data 
→ Agreed owners 
→ Confidence in the outputs

When these are in place AI finally becomes what everyone wants it to be: simple 
scalable 
repeatable.

So here is my question for every exec team: 
What is the data truth you have been quietly avoiding?

♻️ Repost to help someone get their data AI-ready.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Transform your AI & data ambition into action | xQuantumBlack, xMcKinsey | Global top 100 Innovators in Data & Analytics ’24 | Founder & Director, Cambiq

Overlooking good data.
Overpromising on AI.

Agentic AI is moving faster than most teams can learn it.
Use this reading path to shape your 2026 roadmap:

Every report below is from the last six months, and together they form a clean & current path from “What are agents?” to “What will my organisation look like in 2026?”

Take a read:

1️⃣ QuantumBlack / McKinsey – The state of AI in 2025
→ Start here for a data-driven snapshot of how far enterprises have actually gone with AI and agents.
🔗 https://lnkd.in/gMHgSVys

2️⃣ AWS – The rise of autonomous agents
→ Ground yourself in what agentic systems actually are and the main use cases.
🔗 https://lnkd.in/gDBDqh9D

3️⃣ PwC – Agentic AI – the new frontier in GenAI
→ A clean C suite lens. Risks, decisions, moves.
🔗 https://lnkd.in/gwgXUDJi

4️⃣ McKinsey – The Agentic AI Opportunity 
→ A five minute shareable framing of the size of the shift.
🔗 https://lnkd.in/gx-i4Ykb

5️⃣ Accenture – Six key insights for C-suite Executives to maximise the ROI of Agentic AI
→ The part most leaders skip. What drives financial value.
🔗 https://lnkd.in/gZh4FvfP

6️⃣ Bain – Building the Foundation for Agentic AI
→ Get practical on the underlying data, technology and platform foundations  to make any of this real.
🔗 https://lnkd.in/g4sTnHB4

7️⃣ IBM – Agentic AI’s strategic ascent
→ Broadens into operating model, governance and enterprise mechanics.
🔗 https://lnkd.in/g2BY4uXx

8️⃣ BCG – The Emerging Agentic Enterprise
→ What a “machines that manage themselves” future starts to look like.
🔗 https://lnkd.in/gTAvW3a2

9️⃣ McKinsey – The Agentic Organization
→ A deeper dive on org design and ways of working in an agentic era.
🔗 https://lnkd.in/grCJChZe

🔟 Deloitte – Agentic enterprise 2028
Finish with a 2028 horizon-scan to stress test your 2026 roadmap.
🔗 https://lnkd.in/g6BEY-qe

If you want to walk into Q4 reviews and January planning with a clear point of view, this is the reading path that will actually get you there.

Anything you’d add to this reading path?

♻️ Repost to help someone with their agentic AI strategy.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Agentic AI Strategy Pack: 10 must reads from the last 6 months

Behind every confident decision
is invisible governance doing the heavy lifting.

Data governance often gets dismissed as red tape. 
In reality, it’s the backbone of trust, speed, and better decisions.

What people think it is:
→ Restricting access to data
→ Slowing projects with rules
→ Complicated policies no one reads

What it’s actually about:
→ Making data accessible and usable (not buried in systems only one person understands)
→ Building trust in data quality (so your dashboards stop arguing)
→ Protecting sensitive information without drowning in jargon
→ Creating shared definitions (“customer” means the same thing everywhere)
→ Balancing compliance with delivery (rules and results)
→ Enabling faster, more confident decisions (“which number is right?” solved)
→ Turning policies into real outcomes (less paperwork, more wins)

At its core, governance is about making ‘yes’ possible safely, consistently, and with confidence.

👉 What do you think is the most overlooked aspect of data governance?
♻️ Repost to help data governance gets the recognition it deserves.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What people think data governance is about vs. what it is actually about.

The fastest way to understand AI governance?
Stop thinking about data. Start thinking about decisions.

There's a lot of noise around governance in the age of AI.
And leaders cut through some of that noise by understanding the split between inputs and outcomes.

Data governance protects the inputs.
It’s about accuracy, privacy, lineage, access and compliance.
It answers: Can we trust the data going into our systems?

AI governance protects the outcomes.
It deals with fairness, explainability, drift and human oversight.
It answers: Can we trust the decisions coming out?

Both matter.
Both are essential.
But they serve different purposes.

What I’ve seen inside organisations this year is that AI governance doesn’t sit neatly with one owner.
Legal, risk, product, ethics, engineering and the business all hold a piece.

That’s normal, because the outcomes affect so many parts of the organisation.

The companies making real progress connect the two:
They build strong data governance first. Clean foundations mean AI systems fail predictably, not catastrophically.

Then they layer AI governance on top as the mechanism that gives business leaders confidence to actually use what's been built.

Have a look at the two side by side to see where each plays a role.

What's your experience? Where does AI governance live in your organisation, and is it working?

♻️ Repost to help someone get their governance layers set.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Data governance vs. AI governance: Inputs vs. outcomes

AI isn’t a one-size-fits-all solution.
This confusion is quietly costing businesses millions.

Executives keep asking, “How do we use AI?” 
But everyone in the room is picturing something different.

Some imagine predictive models. 
Some imagine ChatGPT. 
Some imagine automated workflows. 
And they don’t realise they are talking past each other.

AI is not one thing.
It’s many things, and here are three flavours to know right now.

1. Traditional AI helps you decide. 
2. Generative AI helps you think and communicate. 
3. Agentic AI helps you act.

My view is that until companies name & understand these differences, they’ll keep approving the wrong investments, overlooking solid opportunities and wondering why impact is slow.

When leaders finally see the landscape, the conversation changes from tools to outcomes. Instead of “We need AI”, it becomes: 
→ Do we need clearer predictions? 
→ How can we create content and insights more quickly? 
→ Or how do we benefit from tasks automated end to end?

That’s when real progress starts.
If you were explaining AI to a non technical exec team, what’s the first distinction you’d make?

♻️ Repost to help teams agree on what they mean by AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

3 Roles of AI Every Leader Should Understand

The biggest lie about AI? 
That it’s one technology.

People talk about “AI” like it’s just one technology.
One system. One capability. One silver bullet.

In reality, AI is a toolbox.
And just like any toolbox, using the wrong tool gives you a very expensive mess.

Here’s the simple version executives need:

Machine Learning
↳ Finds patterns in data so you can make smarter decisions.
↳ Think: spotting customer trends or predicting equipment failure.

Natural Language Processing
↳ Lets software read and write like a human.
↳ Think: chatbots that actually help customers.

Generative AI
↳ Creates new content.
↳ Think: drafting documents, creative ideas, first pass analysis.

Computer Vision
↳ Understands images.
↳ Think: monitoring shelves, defects, safety incidents.

Predictive Analytics
↳ Forecasts outcomes so you can plan with less guesswork.
↳ Think: finance forecast and demand planning.

AI Agents
↳ Do tasks for you end to end.
↳ Think: running routine processes automatically.

Agentic AI
↳ Makes decisions, takes actions and coordinates tools toward a goal.
↳ Think: autonomous operations, complex workflow orchestration.

This is why AI feels confusing.

People expect a magic wand, but what they actually get is a multipurpose toolkit that still requires judgement, structure and good leadership.

Its value comes from leaders who know which tool to use, when to use it, and why it matters.

The companies winning with AI know it’s not magic.
They’re using AI to build better decision making systems.

Which part of AI are people still confusing most in your circles?

♻️ Repost to help someone unpack their AI toolkit.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What AI can feel like vs.
What AI actually is

Some companies make AI look effortless.
Others are exhausted.
And it’s not luck.

The heartbreak of AI isn’t failure. It's the wasted potential you can see coming from a mile away.

I walk into some organisations and the energy is electric. AI is helping people work smarter, faster, and with more confidence. 

Then I walk into others and everything feels heavy, stuck, uphill.

And the difference has nothing to do with the tool.

The places that win put in the groundwork long before the platform goes live.
→ Clear measures of impact 
→ Workflows redesigned around humans 
→ Data foundations that stop chaos 
→ People feeling safe, not replaceable 
→ Humans and AI building on each other’s strengths

When these pieces are missing, the tech never stands a chance.

Exec teams have more influence over this than they realise. 
Get the foundations right and momentum builds fast. 
Ignore them and frustration becomes the norm.

What foundation do you think leaders skip most often?

♻️ Repost to help a team meet their AI ambitions.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What people think AI adoption is about vs. What AI adoption actually takes

CEO: “Why aren’t we using AI yet?”
Data team: “We’ve been using it for years.”

Both are right. 
Both are talking about completely different things.

The exec sees ChatGPT writing marketing copy.
The data scientist sees a churn prediction model in production.

The difference matters, because each brings new opportunities, new data needs, and new risks.

AI isn’t one thing.
It’s a spectrum:
From predicting → to creating → to acting.

Before investing in the next AI pilot:
→ Start by naming which AI you're discussing: Traditional, Generative, or Agentic.
→ Then ask: where are we today, and where should we invest next?

Because when everyone understands what kind of AI you’re actually doing, expectations align and investment decisions make sense.

Check out the infographic below: “Different Types of AI Explained to a Non-Tech Exec.”

Five minutes now could save you months of misaligned pilots and budget debates.

How does your organisation currently perceive AI?

♻️ Repost to help teams agree on what they mean by AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

How to explain different types of AI to a non-tech exec

Every company I’ve worked with has confessed the same thing:
“Our data is a mess.”

And here’s the funny part… 
They still think they’re the only ones.

Most companies are messy on the inside. 
Most companies carry legacy decisions, old systems, and data that has lived too many lives.
And yet… that’s still where every AI journey begins.

Every AI ambition begins with a data decision. 
Not a perfect dataset. 
Not a shiny platform. 
A decision to start cleaning, shaping and using what you already have.

The transformation comes from small, consistent steps: 
→ Identifying the critical fields. 
→ Fixing the definitions that cause arguments. 
→ Agreeing on what “good enough” looks like so the work can actually begin.

You won’t have the polished robot version of your data on day one. 
But have the courage to look at the messy version and take ownership of it.

Start somewhere. 
The finish line only appears once you do.

What’s been your biggest challenge with messy data, and how did you tackle it?

♻️ Repost to help someone turn their messy data into polished outcomes.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Start Somewhere: Every AI ambition begins with a data decision

You can’t build an AI powered future
when the middle is trapped between big expectations 
and broken foundations.

The execs paint an exciting AI future.
People get energised.
The slides look incredible.

Then the middle gets told to “make it happen”
with tools they barely understand
and systems that barely hold together.

No shared priorities.
No real training.
Leaders encouraging AI use, but not using the tools themselves.

And underneath it all sits the stuff nobody wants to own:
decades of tech debt, tangled systems, and data scattered everywhere.

This is where the AI strategy quietly collapses.

Not because people don’t care.
But because the foundations were never part of the plan.

If leaders want real AI impact, they:
→ treat the foundations as part of the AI program, not an afterthought
→ make the path clear
→ equip people with the skills to use the tools
→ role model AI use in their own work

It’s not glamorous.
But it’s what works.

If you’re honest about your organisation, where does the story break?

♻️ Repost to help someone take a look at how AI lands across their org.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Hopeful AI vision meets the messy middle and a reality check.

2026 will be a defining year for AI advantage.
Here’s what the leaders are doing now so you can map your next moves.

If you’re deciding where AI fits in your business next year, 
learn from those already doing it.

These resources give you a practical sequence:
Understand where AI makes a difference → get specific on use cases → prioritise for impact

Start the new year with clarity and momentum.

1️⃣ Understand where AI has made a difference in 2025:

1/ McKinsey / QuantumBlack – The state of AI in 2025 
→ Your reality check. What enterprises are actually doing today.
🔗 https://lnkd.in/gMHgSVys

2/ Capgemini - Harnessing the value of AI
→ How organisations are moving from pilots to scaled gen AI use cases
🔗 https://lnkd.in/gB7fF2za

3/ Google - ROI from AI 2025
→ Shows how firms using AI agents see real payoff
🔗 https://lnkd.in/gh4N7pf4

2️⃣ Get inspiration from these lists that show use cases that work:
1/ Google – Real-World GenAI Cases / 1001 Use Cases
🔗 https://lnkd.in/gGAuCFx7
PLUS: Google – 101 GenAI cases with architectural blueprints
🔗 https://lnkd.in/gmYrKNee

2/ Amazon – GenAI Customer Stories / 230 Use Cases
🔗 https://lnkd.in/g-RgmxBN

3/ Microsoft – AI Customer Stories / 1000 Use Cases
🔗 https://lnkd.in/gb-RnCKV

4/ PwC – Applied AI Compass / 200 Use Cases
🔗 https://lnkd.in/g75ZsSUX

5/ SAP – Business AI per department / 200 Use Cases
🔗 https://lnkd.in/gvh4GhqJ

3️⃣Prioritise your use cases:
1/ Gartner – AI Opportunity Radar
Great for setting your AI ambition.
🔗 https://lnkd.in/gG_tsSqB

2/ Google – Value, Feasibility & Actionability
Bridges data readiness and business impact.
🔗 https://lnkd.in/ggkJnTm9

3/ Capgemini – Potential vs Ease Matrix
Perfect for workshops.
🔗 https://lnkd.in/gPZx8jYe

4/ Deloitte – Impact, Ease & Differentiability
🔗 Highlights where AI creates unique value.
https://lnkd.in/g_7iD4Cs [chapter 1]

What excites you the most about AI in 2026?

♻️ Repost to show someone the art of the possible with AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Here’s the advice boards are getting on AI.
Relevant whether you sit in the boardroom or answer to it.

McKinsey, BCG, AICD and similar reports released over the last quarter are shaping how boards think about AI.

Even if you're not on a board, they still matter.
They influence the questions boards ask management, often before management has had time to build real context.

If you're on a board, they matter even more.
The message is consistent and increasingly explicit.
Directors are expected to lift their AI fluency.
This isn't something to delegate.

The sequence is clear:
Understanding obligations → setting direction and demanding value → using AI at the board table.

1/ KPMG Australia – Boardroom view on GenAI adoption
A grounded view of what's happening in boardrooms, with a practical checklist of questions boards should be asking.
🔗https://lnkd.in/gqk36hXv

2/ Institute of Directors (UK) – AI Governance in the Boardroom
Principle-led, practical guidance on overseeing AI strategy, ethics and risk at board level.
🔗https://lnkd.in/g-in7Cir

3/ McKinsey – The AI reckoning: How boards can evolve
A clear argument for why AI is reshaping competitive advantage and governance expectations, and why directors must lift their own AI literacy.
🔗https://lnkd.in/geG-Xtfz

4/ BCG – Targets Over Tools: The Mandate for AI Transformation
Pushes boards to move management away from experimentation and towards measurable outcomes. Strong on value creation over shiny toys.
🔗https://lnkd.in/gqaW4wCt

5/ Australian Institute of Company Directors – AI use by directors and boards: Early insights
Practical guidance on how boards and directors can start using AI in their day-to-day work now, not in theory.
🔗https://lnkd.in/guXwqJ4A

6/ EY – What is the agenda for reimagining more strategic, future-looking boards?
A broader perspective on board evolution, with useful insights on how AI can support better governance and decision-making.
🔗https://lnkd.in/g-TxhiHx

The quiet message running underneath all of this:
AI fluency is becoming part of baseline good governance.

How prepared do you think most boards really are for that shift?

💁‍♀️ If you want help lifting AI capability at board level, get in touch.

♻️ Repost to help board come to grips with AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.
👉 I’m building somewhere to go deeper, sign up here: https://lnkd.in/giQ3b6Fi

The AI Board Pack: 6 guides from the last 3 months

Your team wants to learn AI but most don’t know where to start.
Here’s everything you need to give them direction.

If you lead people, you are already feeling the shift.
By 2026, AI fluency will sit beside email and Excel as a basic skill.
Your team will expect guidance, not hype.

The good news is you do not need a paid course. 
Some of the best training comes from Google, Microsoft, IBM, Anthropic and OpenAI. 
Free, self paced and trusted.

Here is a simple starter list you can share with your team.

General overview of AI

1. Google AI Essentials: 
→ Helps people across roles and industries get AI skills to boost their productivity 
https://lnkd.in/gJz_zN8S

2. Generative AI for Everyone: 
→ Learn how generative AI works, and how to use it in your life and at work
https://lnkd.in/gv9XyTVR

3. IBM AI for Everyone: 
→ Introduction to core AI, ethics, and hands-on use of Gen AI tools.
https://lnkd.in/g8GAYM4M

4. General prompting essentials: 
→ Learn from Google experts how to use AI effectively by writing clear and specific prompts.
https://lnkd.in/gZgSxW37

5. Generative AI Leader
→ Get a broad overview of Gen AI as well as how to leverage it to transform work
https://lnkd.in/g-wEa5NY

6. Elements of AI by The University of Helsinki
→ Learn what AI is, what it can (and can’t) do AI. The courses combine theory with practical exercises
https://lnkd.in/g7J-kECe

For ChatGPT users
7. Prompting for ChatGPT: 
→ Learn how to chat with ChatGPT to give you the best results
https://lnkd.in/gduXrQft

8. Further resources for ChatGPT at work:
→ Find content to harness AI as a partner in your daily work
https://lnkd.in/gMj2h3nS

For Copilot users
9. Get started with Microsoft 365 Copilot: 
→ Explore how Microsoft 365 Copilot can streamline communication and power up your content creation.
https://lnkd.in/gQhP4EXd

10. Microsoft 365 Copilot Personal Productivity for all: 
→ Learn which Microsoft 365 Copilot tool to use for which task and when to switch
https://lnkd.in/g-Fh5hH2

11. Want more Copilot? Browse here:
https://lnkd.in/g7SNaBTf

For Claude users
12. Claude for Work:
→ Learn to us Claude across your organisation & max team productivity
https://lnkd.in/gzbwWpMv

For Gemini users
13. Google Skills Hub
→ Build AI skills for tomorrow
https://lnkd.in/gQkfhTVE

Teams want to learn, but they don't want noise, they need a simple, trusted list like this gives them the confidence to begin.

What will you point your team to first?

♻️ Repost to help a team learn AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Intro to AI: Essential Courses to get your team started

There's a quiet assumption in some workplaces that AI is a “tech thing”.
It isn’t.

AI is a confidence thing.
A courage thing.
And now a career thing.

But right now, too many brilliant individuals are sitting on the sidelines because they feel they need to know everything before they begin.

My friend Georgie Hubbard has just written a book, The Bold Move, that flips that script.
It was great to catch up with her this week and see once again her energy and passion for helping women pivot and succeed in their careers.

She’s done it for over a decade and her enthusiasm hasn’t waned.

It really reminded me that the future belongs to the people who are willing to learn out loud.
To experiment.
To ask for help.
To back themselves even when they feel unsure.

We've heard it before, but it needs saying again:
AI isn't here to replace your creativity, empathy or judgement.
It’s here to amplify them.

If more people felt confident using these tools, imagine the products we would build.
Imagine the workplaces we would redesign.
Imagine the conversations we would shape.

You don't need to be an expert.
You just need to start.

What has been your biggest barrier to experimenting with AI?

6 old career rules no longer relevant in the age of AI

Make efficiency your competitive advantage in 2026 with these 100 Copilot shortcuts.

Huge thank you to Zsolt Kovacs for pulling this together. Follow him for practical Copilot tips.

I’ve worked with several companies rolling out Copilot this year and it’s been brilliant watching people shift from beginner to advanced to power user.
Their confidence grows as the friction disappears.

If you want the same lift, start with the shortcuts that remove dozens of clicks and constant context switching:
→ Open Copilot, chat and switch modes without touching your mouse
→ Edit, format and clean up content with quick keystrokes
→ Use slash commands to analyse data, draft content or debug code
→ Automate repetitive work with prompts, templates and workflows

Small efficiency gains compound quickly.
Shortcuts save seconds, but more importantly they protect your flow.

Which shortcut are you adding to your workflow today?

♻️ Repost to help someone reclaim their flow. 
🔔 Follow Clare Kitching  for insights on unlocking value with data & AI.

100 Microsoft Copilot Shortcuts and Commands

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