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Why this issue matters:

• If you skip commercial translation, your analysis will look busy but deliver no recognised value

• Most analysts bury impact by focusing on SQL or dashboards instead of business timing

• The three step structure, problem plus solution plus quantified outcome, forces clear commercial relevance

• The real leverage is commercial impact, the metric that drives promotions and leadership trust

• Single source of truth is a LIE and you must know WHY

In part 1 of our 8 part we begin to dive into what I suggest you focus on in the next 12 months to boost your CV. The job market is shaky. You need to be as prepared as you can

We break down simple, impactful steps that will ensure you are in the best position possible as soon as practically possible. I have created these with action in mind, and with impact as proof.

We also introduce a new mini series - Truth is Messy, Contested and Human. I am going to blow your mind with some truth bombs if you havent beed in data for long and are blindly accepting some of the garbage you read on LinkedIn and blogs across the net.

Lets go!

Most analytics work dies in a dashboard. It sits, pristine but inert. Regularly updated. Rarely actioned. That’s not a flaw in the data.

It’s a failure in communication.

We, dear readers, are re-visiting this topic, once again. Yes I have surfaced this one a few times in prior newsletters, and if you haven’t fully internalised or understood yet (or are new to the group) then this is the day to take action.

The real leverage in analytics lies not in elegant SQL or polished visualisations, but in commercial translation.

Thats it. Without commercials - go home. Game over. This is 30 years working in data speaking.

The analysts who move fastest and furthest aren't necessarily the most technical. They’re the ones who make their work commercially legible. They show how an insight translates into revenue won, cost avoided, risk mitigated or time reclaimed. When you can tie your technical work to a business outcome, your influence compounds.

And yet, most analysts can’t do this. Dont do this. Not because they lack ability. But because they’ve never been taught to quantify impact in executive terms.

They can explain what they built, but not why it mattered.

Take a churn model. Without commercial framing, it’s just another algorithm. With context: “We identified at-risk customers 45 days earlier, enabling retention campaigns that saved $890K annually.”

Same model. Different story. One gets you a thank you. The other, a seat at the table.

The failure is systemic. Most analytics training ends at model validation. Very few programs teach analysts to link their work to commercial metrics. Which is why most dashboards sit untouched, or any value they deliver not attributed to your hard work.

(And ergo - most analysts stay in the backroom)

The fix is a three part framework that repositions any analytics effort:

1. The Business Problem: What poor decision or missed opportunity was costing the company? State it commercially. "Pricing inconsistency eroded $340K in annual margin across three key segments."

2. The Technical Solution: What was built, briefly. Focus on why it matters. Not just the tool used, but the reason it solved the problem.

3. The Measurable Impact: Quantify. Always. Not "improved accuracy" but "reduced false positives by 24%, cutting customer support costs by $110K per quarter."

Adopt this structure and every stakeholder conversation shifts. Instead of walking through data tables, you lead with the decision being improved and the value at stake. Instead of being the person who answers questions, you become the person who frames them.

This has career effects too. Most CVs describe activities. Few prove value. But when you articulate your work in business terms, you stop sounding like a technician. You sound like a strategic partner. Which is exactly what gets you promoted.

Two Moves You Can Make Today

1. Make Impact Quantification a Standard: Require every analytics deliverable to include an estimated or actual business impact. Frame every project in commercial terms from inception. Don’t let technical teams operate in abstraction.

2. Audit for Commercial Visibility: Review the last six months of analytics output. How many projects had measurable outcomes tied to business goals? How many stakeholder presentations led with value over data? If less than half, that’s your signal. Build the muscle now.

Gotchas & Watch-Outs

• Attribution Isn’t Always Clean: Business outcomes have many inputs. Be honest about what part analytics played. Inflate the impact, and trust erodes.

• Not All Value is Immediate: Some wins are lagging indicators. Track them over time, but don’t dismiss them just because they don’t show up in Q1.

• Assumptions Must Be Transparent: When estimating value (e.g. time saved), document the basis. Make it easy for others to validate or challenge the numbers.

Positioning analytics as a strategic lever isn’t optional anymore. The analysts who win are the ones who connect insights to outcomes.

The leaders who thrive are those who build teams that do the same.

🔥 The professional that clearly quantifies on their resume the IMPACT they had in their prior jobs will stand out above those that just name

Next week we cover part2: AI Literacy & Applied GenAI – Partner with automation instead of competing against it.

Truth is messy, contested and HUMAN

This is also a new mini series.

There is so much talk about governance and clean data and truth and model accuracy .. but it is all so superficial. Anyone that has worked in data for a while will immediately tell you that none of those topics are ever easy, there is no one answer or path or rule.

I am going to dispel a whole bunch of these for you.

(I am a little tired of seeing posts on LinkedIn professing the need for governance, data quality, model accuracy .. they entirely and always miss the nuance)

Why do I think this is an important set of topics to write about?

You will eventually stumble across all of these anyway, so you may as well be armed with some intuition to get a head start.

The Single Source of Truth Is A Myth (Here’s What To Build Instead)

The Fantasy of a Single Source of Truth 🦄✨🌈

We’ve been sold a tidy lie: that with enough engineering, governance and tooling, we can arrive at a single source of truth.

One clean, central version of the facts that everyone in the business can trust.

It’s comforting. It’s neat. And it’s damn well false.

In practice, every so-called “source of truth” you’ve worked with has been the result of compromise. Behind the clean numbers sit clashing definitions, unclear lineage, conflicting incentives, and layers of political negotiation. The dashboard doesn’t show you the truth. It shows you the outcome of arguments and backchannel decisions made by people who often don’t speak to each other - and sometimes don’t even know (or care that) they disagree.

This becomes dangerous when AI enters the picture. It will generate coherent answers using flawed foundations. The confidence in output increases, while the quality of the input remains uncertain. If you don’t understand the human friction beneath your data, you will make poor decisions faster and with more certainty than ever before.

Truth inside organisations is not a dataset. It’s a power struggle.

Each department brings different motivations:

• Finance wants control and defensibility.

• Sales wants performance to look strong.

• Product wants flexibility and strategic ambiguity.

• Technology wants consistency and ease of maintenance.

Every metric reflects these tensions. Tools can’t resolve them. Not your warehouse. Not your semantic layer. Not even your most rigorous data governance process.

Because truth isn’t engineered. It’s negotiated.

So how do practitioners respond when their job still demands clarity, confidence and impact?

You start by acknowledging that the “single source of truth” is an illusion, and then you map where it fractures.

First, find the pressure points.

Look for reports that claim to answer the same question and compare them. Where do definitions drift? Where do numbers diverge? Don’t debate the correctness, just lay out the deltas. People might resist the conclusion, but they can’t argue with side-by-side discrepancies.

Next, run a truth reconciliation workshop. It sounds dry. It’s not.

It’s one of the most politically sensitive and strategically important sessions you’ll ever run. Bring the conflicting definitions into the open. Ask one simple question: what decision is this metric meant to inform? You're not resolving fields and filters. You’re clarifying purpose. That’s what changes how people define and defend the numbers.

Finally, replace the “one truth” model with an aligned truths model. Senior leaders don’t need absolute accuracy. They need internal consistency; numbers that don’t contradict each other, surprise them, or collapse under scrutiny. You deliver that by documenting definitions, surfacing assumptions, agreeing ownership, and setting escalation paths.

This is not technical work. It’s human work. And it’s often uncomfortable. (And it makes you the rockstar)

.. and it’s what separates tactical reporting from strategic impact.

The analysts who grow in influence aren’t the ones who chase purity. They are the ones who manage ambiguity. They understand that truth is not found, it’s built in conversations, compromises and context.

This is the real work. Always has been. And forseeably - always will be.

GREAT NEWS WEEKLY - NOVEMBER 8-15, 2025

Date range: November 9-15, 2025

Let’s shift gear this week and look at some more positive, if maybe not unusual stories from the past week or so…

Story #1: Daily GenAI Users Command 52% Higher Salaries and Superior Job Security

PwC - November 12, 2025

Workers who used generative AI daily over the last year report dramatic career advantages according to PwC's 2025 Global Workforce Hopes & Fears Survey of nearly 50,000 workers globally. Daily GenAI users are far more likely than infrequent users to have seen tangible benefits to salary (52% vs 32%), job security (58% vs 36%), and productivity (92% vs 58%). The survey reveals only 14% of respondents currently use GenAI daily, suggesting massive untapped opportunity for analytics professionals who invest in AI skills now to gain competitive advantage in the talent market.

URL: https://www.pwc.com/gx/en/news-room/press-releases/2025/pwc-2025-global-workforce-survey.html

Story #2: AI Startups Raise $3.5 Billion in November, Signaling Hiring Surge Across 20+ Companies

Second Talent - November 12, 2025

November 2025 saw over $3.5 billion flow into AI startups during the first two weeks alone, spanning enterprise AI agents, healthcare automation, cybersecurity, and infrastructure. Major deals included Metropolis ($500M), Armis ($435M), and Beacon Software ($250M). As AI now accounts for 52.5% of all global venture capital in 2025 totaling $192.7 billion year-to-date, these funding rounds signal where the AI talent war is heating up and which skills organizations need to compete, creating thousands of new roles for ML engineers, AI product managers, and applied research scientists.

URL: https://www.secondtalent.com/resources/top-ai-startups-that-raised-funding-in-november-2025/

Story #3: Enterprise AI Adoption Hits 78%, Delivering Up to 55% Productivity Gains

FullView AI - November 12, 2025 (updated November 13)

AI adoption reached 78% of enterprises in 2025, up from 55% in 2023, delivering proven business value with 26-55% productivity gains and $3.70 ROI per dollar invested according to comprehensive 2025 AI statistics. This surge in enterprise adoption validates continued investment in AI talent and infrastructure. With 70-85% of AI projects still facing implementation challenges, organizations are competing intensely for analytics professionals who can bridge the gap between AI hype and implementation reality, creating premium opportunities for skilled data practitioners.

URL: https://www.fullview.io/blog/ai-statistics

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If you take only one thing from today’s issue, let it be this:

Technical skill will always matter, but commercial clarity is the multiplier that decides who gets heard, who gets trusted and who gets promoted.

Analysts who can quantify impact become impossible to overlook. Analysts who can navigate the messy, human side of truth become indispensable.

This is the season to build both.

Over the coming weeks we will keep sharpening these muscles. Part 2 tackles AI literacy and applied GenAI, not in abstract terms but in the exact ways practitioners can partner with automation instead of competing against it. The new mini series on the human mess of truth continues as well, where we will dismantle more tidy myths and replace them with workable, reality-tested approaches.

You are not here to consume information. You are here to get ahead of the next twelve months.

I will give you everything I can to help you do that.

Best,

Tom.

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