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IOD Incorporated

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Big Ideas! Big Results! - What do your customers see when they look for you online?

Big Ideas! Big Results!

As a business owner in today's digital age, the online presence of your company is more important than ever before. Your customers and clients are likely searching for you online before they even step foot in your store or office. That's why it's critical to put thought into how you want to represent your business across all online platforms - especially the central hub, your website.

In order to stand out and gain clients most effectively online, you need an easy to use and informative website that helps customers take action.

Andrew's mission is to help business owners refine their personal and company presence online to maximize visibility, establish authority in their industry, and attract the right leads. Andrew and Webfitters can assist with website design, content creation, relationship building, and overall marketing guidance tailored specifically to your needs.

Andrew Bressers

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

Founders don’t lack data.
They lack confidence to make decisions with it.

I work as a Fractional Chief Data Officer for scaling tech companies, when analytics becomes business-critical — but hiring a full-time CDO is too early or too risky.

I’m the founder of Valiotti Analytics, a data consultancy trusted by 50+ teams globally. I work with companies across the US and Europe.

I have a PhD in data science, which helps when problems are complex — but my work is always grounded in business outcomes.

What I typically change:
• Data & analytics strategy aligned with business goals
• Clear metrics and decision frameworks
• Analytics org design (people, tools, processes)
• Systems that teams actually use, not just maintain

Before consulting, I was an in-house operator. I’ve lived through messy data, slow reports, and dashboards nobody trusts — and I know why “just build another report” never works.

Since 2019, I’ve helped marketing, sales, and product teams:
→ Scale revenue up to 25x
→ Eliminate manual reporting and data chaos
→ Turn analytics into a decision-making system, not a cost center

I usually work in a fractional format (3–12 months), directly with CEOs, CPOs, and CMOs — with optional execution support from my team.

Nikolay Valiotti

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

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

And every time I think:
Great. We solved the part of analytics that was never the problem.
SQL was never the bottleneck, and typing isn’t the job, thinking is.

Most companies don’t struggle because nobody knows how to write a JOIN.
They struggle because nobody knows what question actually matters, or how to connect it to the business reality they’re living in.

Give someone a “natural language → SQL” tool and here’s what happens:
→ They ask vague questions.
→ The model confidently answers the wrong thing.
→ And everyone walks away thinking the data is broken.

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

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

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

Nick Valiotti

Fractional CDO | Helping Scaling Tech founders turn data into faster decisions | Founder @ Valiotti Analytics

Data strategy is “IT’s problem.”
Which is corporate for: “Please install the growth.”

It usually starts innocently: data feels technical and IT feels technical. So we put the data team under IT and call it alignment → congratulations, you’ve just turned your future competitive advantage into a helpdesk category!!!
Now watch what happens.

The business asks, “Why are we losing customers in month three?”
Data replies, “The pipeline is stable.”

Marketing asks, “Which channel actually drives margin?”
Data replies, “We migrated to a new warehouse.”

Finance asks, “Can we trust this forecast?”
Data replies, “Access permissions are updated.”

Everyone is technically correct, aaand… completely missing the point.

When data sits inside IT, it inherits IT’s incentives: uptime, cost control, security, system health. All important. None of them answer, “Where should we place our next €5M bet?”

So the organization slowly drifts into a very modern tragedy:

Beautiful dashboards → Zero decisions.
Six definitions of “active customer” → One very awkward board meeting.
Three new tools → No one accountable for the number.

Then leadership concludes, “We need more data.”
No. You need fewer hiding spots.

A data strategy is not a collection of tools. It’s a set of hard choices.

→ Which metrics actually drive this business? 
→ Who owns them? 
→ What decision changes when they move? 
→ What are we willing to stop doing because the signal says so?

That conversation doesn’t belong in a ticketing system.
Execution is cheap now. AI will write your SQL. Vendors will promise a single source of truth by Friday. Interns can spin up a dashboard before lunch.
What’s rare is alignment: around definitions, ownership, decisions that hurt a little.

If your data team only reports into IT, you don’t have a data strategy.
You have very sophisticated plumbing. And plumbing is great, until you expect it to drive growth.

-----

👋  Hi, I’m Nick! If this felt uncomfortably accurate, you’re my kind of reader, so follow along!

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.

Hard pill to swallow:

Excel was never the problem.
Dependence on Excel was.

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

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

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

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

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

Here’s where companies should shift their focus:

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

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

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

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

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

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

Most dashboards show data.

This one supports decisions. 

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

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

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

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

Great job, Maureen Okonkwo!

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

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A Course for Executives

Digital Transformation Academy

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

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

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

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

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

Let's discuss your project and grow you business with the power of data

Join Friday Data Snack for weekly data, leadership, and business insight

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

A lot of funnel dashboards are just progress reports.

This one by Varun J. is a control panel.

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

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

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

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

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

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

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

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

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

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

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

It’s 3 steps and zero BS.

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

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

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

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

That’s the starter pack.

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

----------

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nobody wants to own "data governance".

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

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

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

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

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

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

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

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

Here’s why this works:

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

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

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

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

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

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

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

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

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

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

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

Always great work from Pradeep Kumar G

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

You can hire the best Head of Analytics in the market. Brilliant with SQL. Knows every BI tool. Can spin up dashboards before your coffee gets cold. And still have no idea how decisions are made inside your company.

That’s the uncomfortable part.

Recently had a great conversation with Wade, and this was one of the many insights we both came to highlighting:
CDO ≠ Head of Analytics.

A Head of Analytics builds and scales the analytics function. A real CDO works horizontally across the company: people, processes, culture, accountability.
The CDO’s job isn’t to “own dashboards.” It’s to make sure decisions have an owner. That’s a very different game.

Because here’s what actually breaks in growing companies:
→ Marketing optimizes for MQLs.
→ Sales optimizes for closed revenue.
→ Product optimizes for feature velocity.
→ Finance optimizes for burn.
Everyone has numbers. No one owns the contradictions. So the data team builds more reports.

But the real issue was never the chart. It was the absence of decision architecture.

A CDO looks at:
– Who makes the pricing decision?
– What metric triggers it?
– Where is that metric defined?
– Who is accountable when it moves?

If those answers aren’t clear, it doesn’t matter how modern your stack is. You can have Snowflake, dbt, five BI tools, and a Slack channel called #data-requests. And still operate on vibes. Technology is usually the smallest part of the problem.

The hard part is aligning incentives. Killing vanity metrics. Saying “no” to pet dashboards. And forcing uncomfortable clarity around ownership.
That’s not an analytics role. That’s leadership.

If you’re thinking about bringing in a CDO — fractional or full-time — ask yourself one simple question:

Do you need better dashboards or do you need better decisions?

Fractional Chief Data Officer | Supported $1.4B Campaign Success | 95% Staff Retention | 30 Years International Experience | Data Strategy & AI Leadership | Financial Services & Higher Ed | Available Interim/Advisory

Business leaders don’t open a dashboard to admire charts. They open it to answer three things fast:

→ Are we okay?
→ What changed?
→ Where do I act?

This one by Alice McKnight, MPH actually respects that.

1️⃣ First — it answers “are we okay?” in about two seconds. The five tiles are the five levers that run a retail business: Sales, Profit, Margin, Volume, Demand. Not vanity metrics. Not 18 KPIs. Just the operating heartbeat. 

I don’t have to hunt across tabs or remember definitions. My eyes scan left-to-right and I immediately know performance: revenue down, profit down, volume slightly down, margin up. 

That already tells a story: pricing or mix improved but demand softened.

2️⃣ Second — it shows change, not just numbers.
Every card gives me comparison context (PM + MoM). That’s critical. A number without direction is useless to a decision-maker. $8,656 profit means nothing. “-10.7% MoM while sales -28%” means efficiency improved. Now I’m thinking causes, not values.

3️⃣ Third — trend before detail.
The mini time series under each KPI is extremely smart. Executives don’t want tables first. We want pattern recognition first. One glance tells me whether this month is noise or part of a trajectory.

Example: orders trending up but sales down → average order value fell. I didn’t calculate that. The dashboard made me notice it.

Fourth — built-in diagnosis, not just reporting.
The ranked segments at the bottom are gold.
Instead of making me click 4 filters, it already answers the first management question: “which customer group is doing this?”

So now decisions form naturally:
→ Consumer drives volume
→ Corporate losing profit
→ Home Office high margin but low scale

This is the difference between analytics and business thinking, it points me toward action without asking me to explore.

5️⃣ Fifth — interaction is purposeful.
“Use bar charts to filter by region” is exactly the right level of optional depth. I can stay high level or drill instantly. No extra navigation, no context switching. Executives hate leaving the page they trust.

6️⃣ Sixth — cognitive load is low.
Consistent layout across all cards means my brain learns once and reads five times. Same structure: value → change → trend → drivers. I never re-interpret the UI. That’s why it feels fast.

Why this matters commercially:
→ A good dashboard reduces meetings.
→ A great dashboard reduces arguments.

This one creates shared understanding quickly enough that people will talk about decisions instead of definitions — and that’s the real ROI of BI.

Lead Data Analytics Consultant | Tableau Ambassador | BLK in Data & Analytics TUG Co-Lead

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

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

Everyone nods.
Nothing changes.

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

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

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

Same data. Different room. Different outcome.

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

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

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

-------

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

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

Hot take:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

who owns this decision when the stakes are high?

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

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

------

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

Most companies proudly say they “have BI.”

What they actually have is a very punctual PowerPoint.

Every Monday at 9:03 AM, the sacred PDF arrives. Revenue. CAC. Conversion. Maybe a tasteful line chart. Everyone nods. No one argues. The numbers look serious.

That’s reporting. It answers one very specific question: what happened. In a fixed format. On a fixed cadence. Ideally with consistent fonts.

And there’s nothing wrong with that. Reporting is useful. Finance needs it. Boards expect it. It keeps everyone aligned on the past.
But let’s not confuse it with Business Intelligence.

BI begins exactly where reporting becomes uncomfortable.
When someone asks, “Wait, what exactly do we mean by active user?” And marketing and product suddenly discover they have been using two different definitions for eighteen months.

When Germany drops three percentage points in conversion and someone refuses to accept “seasonality” as a spiritual explanation.

When CAC increases and instead of updating the slide color from green to orange, someone asks which channel, which cohort, which campaign, and since when.

→ BI includes metric governance so teams are not debating vocabulary during executive meetings. 
→ It includes data modeling so sales and finance are not operating in parallel universes. 
→ It includes exploration and diagnostics so you move from symptom to cause instead of from chart to chart. 
→ It includes segmentation, alerts, monitoring, and sometimes forecasting because grown-up businesses do not enjoy being surprised.

Here is a simple test.

If the output is always the same table on the same cadence, that is reporting.
If the output changes because the question changed, and the goal is to help someone make a decision faster with context and trust, that is BI.

Reporting tells you what happened. BI quietly lowers the cost of being wrong.
And at scale, that difference shows up in cash flow, not in slide design.

----

👋 Hi, I’m Nick, Fractional CDO. If your “BI” currently arrives as a beautifully formatted PDF every Monday morning and no one is allowed to ask why the numbers changed, we should probably talk.

A common question I hear:
“What does a CDO actually do? And why can’t our senior data analyst just do that?”

It’s a fair question.

The thing is:
A data analyst’s job is to answer questions.
A CDO’s job is to decide which questions matter and which ones shouldn’t be asked at all.

Analysts work inside the system.
CDOs are responsible for the system itself.

That means:
→ deciding what data the company should trust
→ setting priorities when everything feels important
→ choosing what to build, buy, or ignore
→ clarifying ownership so decisions don’t stall in debate

None of this is about SQL, dashboards, or tools. It’s about judgment.
At Series A–C, this gap becomes visible. Data exists. Dashboards exist. Talent exists. But nobody is accountable for direction.
That’s usually when founders start feeling the drag: slow decisions, duplicated work, quiet confusion.

This is where a fractional CDO helps — not by replacing analysts, but by giving them direction.

I wrote a short guide explaining how this actually works in practice: what a CDO does, when the fractional model makes sense, and what typically changes in the first 90 days.

Feel free to grab it in my Featured or via the link → https://lnkd.in/dxkdm3nq

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

HR data usually answers "what happened." 

This dashboard answers "what do we do next.

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

Here's why it works:

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

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

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

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

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

Great work, Alice!

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

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

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

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

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

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

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

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

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