^{READ TIME: 9 MINUTES}

Community of Practice or Centre of Excellence? You probably need both…

As organisations grow, their data capability almost always becomes distributed. Analysts embed in business units, domain expertise deepens locally, and teams evolve into some variation of hub and spoke, or federated structures.

There are good reasons for this. Proximity to stakeholders improves responsiveness, the added context improves the quality of their insights, and the business feels more supported overall.

However, once you distribute capability, you also distribute decision making around definitions, logic, tooling, and governance. Without a deliberate coordinating mechanism, a degree of fragmentation becomes an inevitability.

This is where many data leaders start to struggle. They recognise the need for alignment but are unsure how to go about it. Two common options are to establish a Community of Practice, a Centre of Excellence, or some hybrid of the two.

In reality, distributed models require both. They need the learning energy of a community and the authority of governance.

I learned this the hard way earlier in my career. I was running a federated team structure when I discovered that different business units were reporting conversion rate using slightly different logic (One team had excluded certain low value traffic sources from their denominator, which made their performance appear significantly stronger than it was). When we aligned the definition across markets, the numbers shifted materially. A simple governance layer would have prevented this situation entirely. Lesson learned.

The cost of misalignment across teams has the power to damage trust, credibility, and ultimately, your influence as a data leader.

Unfortunately, many data leaders only recognise this once the damage is already causing havoc.

^{Strategies for Effective Data Leadership is brought to you by:}

Wake up to better business news

Some business news reads like a lullaby.

Morning Brew is the opposite.

A free daily newsletter that breaks down what’s happening in business and culture — clearly, quickly, and with enough personality to keep things interesting.

Each morning brings a sharp, easy-to-read rundown of what matters, why it matters, and what it means to you. Plus, there’s daily brain games everyone’s playing.

Business news, minus the snooze. Read by over 4 million people every morning.

Try Morning Brew for Free

P.S. You too can sponsor this newsletter. The sooner you do, the cheaper it is. Learn more here

How distribution turns to inconsistency

When distributed data teams operate without a clear Community of Practice or Centre of Excellence, the following challenges start to emerge:

1️⃣ Core business metrics begin to drift as teams adapt definitions locally to suit context, creating subtle inconsistencies that are difficult to detect until they surface at executive level.

2️⃣ When shadow data teams emerge within business units, they wind up developing their own logic, documentation, and reporting approaches without visibility across the wider enterprise.

3️⃣ Decision-making forums become dominated by debates about definitions and calculation methods rather than focusing on strategic action.

4️⃣ Trust in the data function weakens when numbers differ between regions or presentations, reducing the perceived authority of the team, and your leadership.

5️⃣ Labour scaling across teams becomes difficult because analysts cannot easily move between domains when standards, documentation, and business logic vary significantly without there being a steep learning curve.

Over time, this erodes not only operational efficiency but also the strategic standing of the data function.

The encouraging part is that this is solvable with deliberate design.

(P.S. last week’s poll results are down at the bottom)

Creating alignment in a distributed model

If you lead a distributed or semi-distributed data capability, the objective is not to centralise everything. It is to coordinate intentionally.

The first step is to define the scope of your Centre of Excellence. This should focus on the non-negotiables of your data enterprise. Definitions for core business metrics sit here. Minimum governance standards, documentation expectations, shared tooling principles, and clear decision rights when disputes arise should all be explicitly owned. The Centre of Excellence does not need to control delivery, but it must control what needs to be consistent around the org.

The second step is to establish a structured Community of Practice that meets regularly and has a clear mandate. This community should not exist as a passive forum. It should actively surface edge cases, stakeholder challenges, duplication risks, and emerging inconsistencies. Discussions should be documented and fed back into the Centre of Excellence so that standards evolve in response to operational reality.

The third step is to make documentation a shared responsibility. Core definitions, business logic, and governance decisions should be visible, accessible, and version controlled. When analysts move between domains to support areas under excessive load, they should be able to orient themselves quickly because the logic and expectations are consistent.

The fourth step is to clarify decision rights explicitly. When disagreements about definitions occur, there should be a known escalation path. Ambiguity around authority is one of the primary drivers of metric drift.

The fifth step is to monitor alignment proactively. Periodic reviews of key metrics across business units can help detect divergence early before it becomes a political issue.

Taken together, these actions create a governing spine supported by a learning layer. The spine provides consistency and authority. The community ensures relevance and shared understanding.

Even centralised teams benefit from this approach as they scale. Informal variations inevitably emerge once analysts align closely with different stakeholder groups. Without a deliberate governance and community mechanism, internal fragmentation can develop quietly.

Shadow data teams will always exist in some form. The goal is not elimination but alignment. A well-designed Centre of Excellence combined with an active Community of Practice reduces the risk they pose and channels their efforts productively.

If you are leading in a distributed environment and find yourself repeatedly resolving disputes about definitions or defending metrics at executive level, it may be time to look beyond structure and towards coordination.

The right model is rarely either community or control.

Sustainable data leadership requires both.

If you would like support designing this kind of coordinated structure in your organisation, I would be happy to explore it with you.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week, I asked you What does your team primarily measure success by?

Here’s how you responded:

^{READ TIME: 8 MINUTES}

⛓️ The Data Value Chain

Before we dive into today’s topic I wanted to welcome all of you to my 100th edition of this newsletter: Strategies For Effective Data Leadership.

It feels amazing to have been able to publish this week in week out and to arrive at my 100th edition. It’s not always been easy and there have been plenty of weeks where I’d have easily just given up. But to be here today and to have you as my reader feels incredible. THANK YOU!

(If you’ve been enjoying this newsletter and think it might be valuable to others, please share this link with them so they can subscribe as well!)

👉🏼 Now onto today’s topic: The Data Value Chain

We often hear that for our work in data to be impactful, it has to deliver value to the business. But these statements can be as confusing as they are often vague.

In what sense do we mean value?

What do we mean by impactful?

How does that differ from what we’ve been doing?

I get these sorts of questions all the time. So I thought to myself, what’s the best way I can visualise the end-to-end lifecycle of data value.

To hopefully accomplish that, I’ve adapted the data value chain into the below diagram:

Via the Data Value Chain, I wanted to show that data itself is not the starting point in the value conversation, and that insights and dashboards are not the finishing line.

That these are in fact crucial middle points in the overall chain. By observing the value chain, you’ll see that the start and end-points are not at technical pieces of the puzzle, but rather business activities.

Let’s take a closer look, shall we?

^{Strategies for Effective Data Leadership is brought to you by:}

Attio is the AI CRM for modern teams.

Connect your email and calendar, and Attio instantly builds your CRM. Every contact, every company, every conversation, all organized in one place.

Then Ask Attio anything:

• Prep for meetings in seconds with full context from across your business

• Know what’s happening across your entire pipeline instantly

• Spot deals going sideways before they do

No more digging and no more data entry. Just answers.

Start your free trial →

P.S. You too can sponsor this newsletter. The sooner you do, the cheaper it is. Learn more here

What the data value chain actually looks like

When we talk about the data value chain, we are not simply talking about pipelines or dashboards. We are talking about the sequence of events that turns information into commercial impact.

The chain begins with a decision that needs to be made. That decision generates a question. The question determines what data is required. Only at that point do engineering and modelling come into play.

From there we generate insight, communicate that insight, and ideally a decision is taken. If that decision results in changed behaviour, a business outcome follows, and if that outcome is measured properly, we can demonstrate impact.

That is the full chain. The end-to-end lifecycle of data value.

Notice where engineering and modelling sit. Notice where insight sits. They are in the middle.

Dashboards etc. are not the end of the chain. They are a (possible) midpoint on the way to value. (I say possible because they are not always needed)

Impact sits at the end and in terms of value, is the most crucial piece.

Most data teams however, build their identity around the middle of this chain. They pride themselves on clean pipelines, elegant models and beautifully designed dashboards. All of that matters of course, but none of it constitutes value on its own. Value only materialises when behaviour changes and business outcomes improve.

Insight is not the destination. It is merely a transition point.

If nothing changes after your insight is delivered, you have just created an output, but no actual value.

Where value leaks out of your data team

Unfortunately, this is where many teams stop. They generate insight, present it, and consider the job done. From a delivery perspective that feels complete. From a commercial value add perspective, it‘s barely halfway.

When teams stop at insight, several predictable consequences follow.

1️⃣ First, they end up optimising delivery rather than decisions. Success becomes defined by how many dashboards were shipped or how quickly tickets were closed, rather than by whether any meaningful decision improved as a result. The team feels productive, but the organisation doesn’t necessarily feel progress.

2️⃣ Second, the business begins to categorise the data team as a reporting function. If stakeholders only ever see outputs, they will naturally treat the team as a producer of outputs and therefore a cost centre. Reporting functions are invited in late, asked to tidy up numbers, and rarely brought into early strategic conversations.

3️⃣ Third, urgency starts to dominate without any corresponding increase in influence. Work framed as requests rather than decisions leads to reactive behaviour. Everything is urgent because everything is a task. When decisions are not explicitly named and owned, the data team absorbs pressure without gaining authority over what truly matters.

4️⃣ Finally, it becomes extremely difficult to defend budget, headcount or strategic investment. It is hard to make a compelling case for growth based on activity metrics such as dashboards delivered. It is far easier to argue for investment when you can point to improved decisions, reduced churn, optimised campaigns or measurable revenue impact. The end of the chain is where the persuasive narrative lives.

The good news is that you do not need to control the entire chain to influence it. You simply need to stop pretending that insight is the finish line.

How to lead across the full value chain

If you want your data team to be perceived as strategic rather than operational, you must extend your focus beyond insight and deliberately design for decision, behaviour and impact.

We start by defining the decision that needs to be made before any technical work begins. Instead of jumping straight to data exploration or SQL, force the conversation to answer a simple but key question: what will we do differently once we know this? If that cannot be articulated clearly, the risk of building something that goes nowhere increases dramatically.

Next, build follow-up into your operating rhythm. After insight has been delivered, return to the stakeholder and ask what changed. What action was taken? What behaviour shifted? What result did that produce? This habit alone transforms how the team is perceived. You are no longer a producer of information; you become a partner invested in the outcomes.

The following step is to change the way you report on your own performance. Move beyond activity metrics or tickets completed and start tracking decisions influenced, initiatives supported and outcomes improved. Language shapes perception, and perception shapes positioning. When you speak in terms of impact, you are treated as impactful.

Finally, resist vague briefs. When someone asks for a dashboard, respond with curiosity rather than blind compliance. Ask what decision the dashboard is intended to inform and what action will follow from it. This is not about being obstructive; it is about maintaining the value chain at its earliest stage.

Understanding the full data value chain changes how you prioritise, how you frame conversations and how your team defines success. It forces you to work backwards from measurable impact rather than forwards from available data.

If you take away anything from this post, I want it to be this:

Curiosity and conversation sit at the start of the chain

Dashboards and insight sit in the middle of the chain.

Impact and value sits at the end.

Until you deliver impact and measure it’s value, you work as a data leaders is incomplete.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you, How to you rate your own literacy around strategy?

Here’s how you responded. Fairly mixed results across the board!

^{READ TIME: 8 MINUTES}

What the hell even is strategy?

We talk about data strategies a lot in data leadership circles, but what we don’t talk about enough is good old fashioned business strategy.

If one thing is resoundingly clear from my travels and interactions with data folks it’s this:

👉🏼 They don’t really know what a strategy is.

If that’s you, don’t stress. I got you.

In today’s post, I’m going to do my level best to demystify business strategy for you.

After all, how can you possibly write a data strategy without:

a) understanding what a business strategy is and;
b) knowing your org’s business strategy isn’t a pile of sh*t (clue: most are)

Strategy is one of those things that get talked about a lot but is **actually** understood by very few. Much like nutrition 🤣 

So anyway let’s strap in for some strategy! 👇🏻

-

Further reading: If you’re looking for 2 well respected titles on the topic of business strategy, these are a great place to start:

• Good Strategy Bad Strategy by Richard Rumelt
  Excellent for learning how to evaluate strategies by spotting weak signals, vague ambition, and the common patterns that make most strategies ineffective in practice.

• Playing to Win by A.G. Lafley and Roger L. Martin
  A practical, choice-led view of strategy that clearly explains what strategy actually is and how leaders can make deliberate decisions about where to compete and how to win.

^{Strategies for Effective Data Leadership is brought to you by:}

Slawomir Tulski

Fix the blockers holding back your analytics

Many data teams get buried under ad‑hoc requests and technical debt, while business questions that actually matter go unanswered..

Analytics should be a revenue driver, not just a cost center.

I help organisations cut through the noise, find the real blockers, and turn analytics into positive ROI.

As a former Data Engineering leader at Meta, I’ve built and scaled the high-velocity teams behind the AI ranking systems powering global ad revenue. I know exactly what high performance looks like.

I specialize in identifying the friction points at the intersection of your people, technical stack, and stakeholder expectations. With the technical chops to see through the jargon and the leadership experience to back it up, I pinpoint the structural leaks that turn analytics teams into cost centers.

I’m offering 3 free 60‑minute Triage Calls on a first‑come, first‑served basis. In the meeting we discuss your current friction points, and I help you categorize your primary bottleneck so you know exactly where to start looking for the solution. No guesswork needed.

Book time now!

What people usually think a business strategy is

When most data leaders hear the word “strategy”, they typically picture one of the following:

• A vision statement about the future

• A list of priorities or OKRs

• A set of financial targets

• A roadmap of initiatives

• A slide deck full of aspirational language

• A glorified ‘to-do’ list

All of these things might sit around strategy. But none of them are strategy on their own.

This confusion matters, because if you mistake goals, plans, or metrics for strategy, then it become extremely difficult to know how to help deliver on it. This is especially true for data leaders who are expected to “enable” or “support” it via data initiatives.

A simple, working definition of business strategy

Here is the definition we are going to use for the rest of this post:

This framing is heavily inspired by Playing to Win, and I think it is the most useful definition for people who actually have to execute downstream (like data leaders).

There are two important implications baked into it:

1. Strategy is about choices, not intentions

2. Strategy is about winning, not just participating

If there are no hard choices to be made, there is no strategy.
If there is no clear idea of how the organisation will win, there is no strategy.

What sits inside an actual business strategy

In Playing to Win, Lafley and Martin describe five connected choices. You do not need the jargon, but here is the logic:

A real business strategy answers these questions clearly:

1. What is our winning aspiration?
   Not a vague mission, but what success actually looks like.
   

2. Where will we play?
   Which customers, markets, geographies, products, or segments we are deliberately focusing on.
   

3. How will we win there?
   The specific advantage we believe we can sustain. I.e. Lower cost, faster delivery, better experience, deeper relationships, etc.
   

4. What capabilities must we be great at?
   The few things the organisation must excel at to make that advantage real.
   

5. What management systems support this?
   How decisions get made, how progress is tracked, and how trade-offs are enforced. Obviously data plays a key role here.

You will notice something important here, there is no mention of projects, backlogs, tools, or technologies. Those come waaaaay later.

How to spot a badly written strategy

This is where the book Good Strategy Bad Strategy is particularly useful.

Based on Rumelt’s work, most internal “strategies” fail in very predictable ways. Here are some concrete warning signs.

A strategy is weak if it:

• Is written almost entirely as aspirations or values, with no diagnosis of the problem(s) being addressed

• Lists many priorities without explaining what is being deprioritised

• Avoids making trade-offs explicit, so everything appears equally important

• Uses abstract language that could apply to almost any organisation

• Cannot be used to say “no” to reasonable sounding requests

That last one should ring very loudly in a data leaders ear. Having a clear strategy tells us what we should (and should not) be working on. Does that request for another dashboard help drive our strategy forward? No? Than you have explicit permission not to do it.

If you cannot use a strategy to make difficult decisions, it is not doing its job. This is also very much true of how we prioritise, accept and reject data projects.

Just chuck it all in there?!

Why this matters so much for data leaders

If you do not understand the business strategy (or one does not exist), you cannot create a meaningful data strategy.

Not because data is special, but because strategy flows in one direction. Business strategy sets direction. Functional strategies exist to support it.

When business strategy is vague or misunderstood, data leaders are left having to decipher clues from fragments around the business. Board slides. Annual goals. Casual comments from executives. This is where misalignment comes from.

Data teams then end up optimising for activity, responsiveness, or local efficiency rather than contributing to the organisation’s chosen and stated ways of winning.

What a data leader should be listening for

When you read or hear a business strategy, your job is not to translate it into a list of technologies, tools or dashboards.

Your job is to listen for:

• The explicit choices being made about focus

• The advantages the organisation believes it has or wants to build

• The constraints and trade-offs leadership is accepting

If you cannot clearly articulate those three things, pause. Do not jump into execution ❌

This is the moment where good data leaders need to ask better questions upstream rather than delivering faster downstream. If things aren’t made clear to you, it possible they’re not clear to anyone. Be a leader and seek that clarity.

Examples of clearly articulated business strategy

There are some solid public examples worth studying, not because they are perfect, but because the choices are visible.

A few worth linking to and reading critically:

• Amazon shareholder letters
  Bezos consistently articulated where Amazon chose to play and how it intended to win, especially around customer obsession and long-term advantage.

• Netflix culture and strategy documents
  Particularly their writing on focus, trade-offs, and talent density. The choices are explicit, even when uncomfortable.

• IKEA strategy explanations
  Their articulation of cost leadership, scale, and design trade-offs is unusually clear and consistent over time.

When you read these, ask yourself one question: “Could I make a hard prioritisation decision based on this?”

If the answer is yes, you are looking at an actual strategy!

Good times.

Bringing this back to your role

If any of this feels fuzzy in your current role, you are vey much not alone. Very few data leaders are ever taught how to read, interpret, or pressure-test business strategies. Never mind turning that into a coherent data strategy off the back of it.

This is exactly the gap I spend most of my time working through with Heads of Data. Not just writing strategy documents, but building confidence in how to interpret direction, challenge ambiguity, and align data work to what actually matters.

Before you try to fix your data strategy, make sure you truly understand the business one you are meant to be supporting.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

^{READ TIME: 6 MINUTES}

🦇 Batman is obviously the best.

Billionaire playboy that stalks the night beating up criminals and cruising around in his rocket car. What’s not to like?

Yeah, Batman is cool. But what’s even cool is having a career superhero, and getting them to be your mentor.

What’s a career superhero, you ask?

A career superhero is someone you know, or know of. Perhaps you’ve worked with them in the past, or just observed them from a far. They’re someone you admire. Someone who’s career trajectory inspires you. Maybe it’s their energy and can do attitude. Maybe it’s their success in the face of adversity. It could be that what they’ve built has impressed you and drawn you to them.

Maybe, they’ve just done the things that you would like to do.

I’m always advising my data leadership coaching clients to identify the career superheroes in their lives and to then seek their mentorship.

Mentorship is a wonderful thing when done right. But we absolutely must pick the right people to be our mentors.

Getting it right can be an insane career boost for data leaders.

Let’s take a look, shall we? 👇🏻

(read more on this topic in my post: Haunted by managers guilt)

^{Strategies for Effective Data Leadership is brought to you by:}

Value from Data & AI

Do you know how much £££ you've generated for your business?

“After the training, I challenged assumptions more proactively. That avoided $200,000 in unnecessary delivery.” Senior Data PM, Fortune 50 CPG

Data teams that quantify value get strategic influence. Those that don't get treated like cost centres.

I've spent a decade building high-ROI data & ML solutions as a consultant and in-house. Last year, I turned my commercial data toolkit into a training programme: ROI of Data & AI

You'll learn how to estimate ROI before writing a single line of code, map value to specific revenue or cost levers, build CFO-approved business cases, and claim credit using the right attribution frameworks.

Individual contributors use these frameworks to escape ad-hoc request traps and accelerate into strategic roles. Leaders use them to transform teams from cost centres to profit drivers. Consultants use them to justify higher project fees.

Stop saying "improved efficiency." Start showing "£2M impact." 

Classes starts February 2nd.

See the curriculum and past reviews →

P.S. You too can sponsor this newsletter. The sooner you do, the cheaper it is. Learn more here

🪧 Signs you might need a mentor

People who finally decide to seek out a mentor often find themselves experiencing one or more of the following:

• They feel little a lonely in their post. They're too senior to share frustrations with their direct reports, and their relationship with their manager is generally tactical rather than someone they can connect with on bigger picture things. (Managers rarely make good mentors - I'll explain below).

• Their perspectives and assumptions are not being challenged. When they believe things to be true without properly analysing those beliefs or having them pressure tested, they’re open to making big miscalculation both in their work and in their career choices.

• They feel like they lack a big picture view of the world. They're heavily focused on their day to day activities and deliverables, but they don't have a bird's eye view of their work or careers. "Where the hell is this taking me?" they wonder. They’re constantly reacting rather than shaping things.

   

• They’re unsure of what “good” actually looks like at the next level. They know how to do their current role, but the path beyond it feels very vague. They don’t want signals to react to and which ones to ignore. They don’t know where to double down or where to let up.

These signals typically show up when we’re operating without a solid reference point.

When a great mentor comes into our lives, they’re really able to help us lift the fog and provide that reference for us.

⚡️ Why having a good mentor changes the game

A good mentor is not there to motivate you or pat you on the head. They are valuable because they compress learning and remove unnecessary friction that would otherwise slow you down.

Here is what actually makes the difference:

• You are directly connected to someone who has already been where you are now. They have seen the trade offs, made the mistakes, and learned which battles are worth fighting. That means the advice you get is grounded in reality, rather than theory or speculation.

• It speeds up your career progress. Without a mentor, you end up reinventing solutions through trial and error. With a mentor, you shortcut that process. You still have to do the work, but you spend far less time sucking at things.

• It is networking at warp speed. Mentors often have access to people, opportunities, and perspectives you would not encounter on your own. Even when they never introduce you directly, they help you see how the system actually works.

• You gain a proper thought partner. Someone who will challenge your assumptions, ask better questions, and push you out of default thinking. Not someone who simply agrees with you to be polite.

• You get a safe space to be honest. Because they sit outside your immediate area of influence, you can talk openly about doubts, frustrations, and missteps without worrying how it will land politically.

• BONUS point (one that often gets overlooked): a good mentor helps normalise the mess. Many data leaders assume everyone else has it figured out. Mentorship punctures that illusion very, very quickly.

🔍 How to find and keep a mentoring relationship that actually works

Most mentoring relationships fail not because of bad intent, but because of vagueness and neglect. A few simple behaviours make a huge difference:

• Look beyond your direct manager. Managers are important, but they are often too close to the day to day. Conversations drift into delivery and tactics. Mentorship works best when there is enough distance to talk honestly about direction, identity, and career moves.

• Choose experience over prestige. A good mentor has usually been through the challenges you are facing. If you want to grow as a data leader, another more senior data leader is often a better fit than a generic “leader” without the context of the challenge you’re facing.

• Meet regularly, even if it is informal. Coffee, a walk, or a Zoom call all work. The cadence should match your needs. Weekly when things feel heavy. Monthly when you want a sense check and perspective.

• Take ownership of the relationship. You are the primary beneficiary here. That means you own the calendar, the follow ups, and the momentum. Be proactive, but also respectful of their time. Your mentor is themselves very busy and you can’t expect them to drive things for you.

• Come prepared. Have topics, questions, or situations you want to explore. Turning up empty handed puts all the burden on them and will waste goodwill very quickly.

• Let relationships end when they have run their course. Mentors are there for particular seasons of your career. If you are no longer getting value, it is fine to move on. This is a professional relationship. One it has served it course, its in both your best interests to move on.

Done well, mentorship becomes a power force shaping your career. I’m willing to bet the house that every successful CEO, Founder, Athlete, Recording Artist etc. will credit much of their success to mentorship.

So why shouldn’t you also?

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week, I ask you Do your data teams operate a formalised intake process?

Here is how you responded:

^{READ TIME: 6 MINUTES}

The unbearable weight of ad hoc requests

I’m often asked by clients, “what is the top challenge you see holding back data teams?”

I love this question. The answer is suuuuper easy becuase it’s basically the same everywhere I go, and with every team I talk to.

👉🏼 Ad hoc data requests 👈🏼

Data requests are obviously a part of the role. In many cases they’re the reason the data team was establised in the first place. But it’s the ad hoc component that’s the real killer.

What do I mean by this?

Ad hoc data requests refer to unstructured and unexpected requests (or demands) for data, or reports, or dashboards etc. from outside the data team.

They can arrive in a variety of manners (email, text, slack, teams, whatsapp, scrunched up piece of paper, shouted across the office floor etc.), they’re invariably URGENT and they often take the form of what I call a “prescriptive solution”.

A presriptive solution is where the requestor (not a data expert) tells the data person (expert) the specific solution that they require. “I need a dashoard that shows xyz” or “can you put it in an excel file with abc headings for me?”

Of course, this is a some backwards BS right there. You wouldn’t go to a doctor to tell them what prescriptions you need! Particularly without discussing symptoms at all.

While most individual ad hoc requests appear small, their combined weight and volume is the number one challenge holding data leaders and their teams back from being a more influential and strategic contributor to the business.

And in an age where every organisation strives to be data driven, and emerging technologies live or die on the power of data, this is a challenge every single data leader will want to get a handle on!

Today, I’m going to show you how👇🏻

^{Strategies for Effective Data Leadership is brought to you by:}

Shachar Meir

Failure shouldn't be the default mode for data teams

The question I asked myself when I left Meta: "why do so many companies still fail with their data?". So I did my research and realised two things:

1. I have the playbook. I know how to fix it. It’s what I’ve been doing for 20+ years.

2. I achieve the most impact by working directly with data executives, helping them transform their organisations.

And that’s what I do today as an advisor.

Want to learn more? Don’t be shy, let’s chat.

Message me on LinkedIn now!

Let’s chat!

P.S. You too can sponsor this newsletter. The sooner you do, the cheaper it is. Learn more here

The crushing part

I could probably talk all day about how allowing ad hoc requests to dicate the work your data team focuses on is a bad idea. But to save us all from that long lecture, I’ve listed below the key impacts this dynamic creates. This ain’t all of them, but these ones are bad enough… let’s dig in:

☢️ Permanent reactivity
Work becomes driven by whoever’s request appears most urgent in the moment rather than by any shared view of value or priority. Strategic initiatives the team would otherwise give their attention to are repeatedly delayed as teams are constantly distracted by the never-ending supply of ad hoc requests.

🚨 Everything is urgent
Typically, most requests received are labelled urgent. When that happens, the word loses all meaning. Stakeholders want their request done first of course, so they call it urgent to jump the queue. This makes it impossible for data teams to actually know where to focus their limited time and attention in a value-add way.

🫥 Steady decline in influence
A data team that is no more than a help desk doesn’t get a seat at the big people table. In the eyes of their peers, the data leader is a supporting act hired to answer the questions raised by the important people in the business. When this is the case, you can kiss good-bye to any influence you hoped to attain.

😶‍🌫️ Invisible pressure on the data leader
Instead of leading, they become the routing layer for the entire organisation. They spend their time negotiating priorities, shielding the team, and absorbing frustration, which leaves very little capacity for direction setting or relationship building.

🤺 Internal disengagement
Teams who are constantly interrupted struggle to see the point of deeper work. They don’t feel like they’re adding value so their motivation drops, and the role becomes about surviving the next round of layoffs that invariable hit “support” teams first.

None of this happens because the team lacks skill. It happens because of a lack of a controlled and systematic appraoch, that enables teams to focus on vlaue add work rather than endless random requests.

Regaining influence with a formalised intake process

Fixing this problem does not mean shutting the door on requests or creating bureaucracy for its own sake. Ad hoc needs will always exist. The goal is to change how those needs enter the system, how you react to them and how badly they impact the team.

Here are some simple steps to help you get a handle on things:

1️⃣ Step 1: Create a single, front door for data work
Whether this lives in Jira, Google Sheets, Tally, or something similar is largely irrelevant. What matters is that every request enters through the same mechanism and is subject to the same questions. If folks want you guys to do some work, then they must submit it in the same manner every time (regardless of seniority).

Alongside being a way to receive data requests - it is also a data collection exercise (see bonus step on why this is key)

2️⃣ Step 2: Reframing requests away from solutions and towards problems
Requests need to take the form of “problems that need solving” rather than prescriptive solutions (described above). We must train our stakeholders to tell us what problem they are trying to solve is, rather than telling us precisely what they need from us. They are not the data expert. They may know what data we have/don’t have, how fresh it is, where it is stored etc. By desribing the problem that needs solving, we data experts can get to work discovering solutions, rather than just producing what they’ve asked for.

3️⃣ Step 3: Make trade-offs explicit
When ad hoc requests are visible alongside planned work, it becomes easier to show what will be delayed if something new is pulled forward. This protects strategic initiatives and creates healthier conversations with stakeholders. A systematic approach enables visibility and transparency for the team and the wider business.

4️⃣ Step 4: Use the process to rebuild relationships
As interruptions reduce, the data team has more space to engage earlier, ask better questions, and operate with confidence rather than defensiveness. Over time, this changes how stakeholders approach them in the first place and increases trust in the team’s knowledge and abilities.

⭐️ Bonus Step: Treate intake as a data collection exercise
Over time, the system shows where demand really comes from, which teams consume the most capacity, and which types of work dominate. This gives data leaders evidence they can use in leadership conversations about resourcing, autonomy, and scope. Not tracking this means you’re operating in the dark, which makes these crucial management conversations so much harder to have.

Handled well, a formalised intake process does more than create an orderly system. It gives data leaders the leverage they need to move from firefighting towards strategy, and from just being busy to actually being influential.

These are the first steps. Why not start taking them today?

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

^{READ TIME: 6 MINUTES}

They really sucked…

Yesterday I had an all-too-familiar conversation with a client of mine. They were complaining about their data team, who, from the sounds of things, suck quite badly.

They didn’t suck at the hands-on aspects of their work. They were technically solid, reliable delivery people who closed tickets, followed process, and generally did exactly what was asked of them. Albeit with a bit of attitude.

What they sucked at though was how they interacted with the wider business. It is a challenge I see data teams all over the world also sucking at.

The team avoided stakeholder conversations altogether and seemed to genuinely believe that engaging with the business was not part of their role.

Their attitude was, “I’m a data person, I do data tasks. That’s it.” 

As a result, every decision, every trade off, and every moment of judgement landed squarely on my client’s - the data leader - shoulders. They were pulled into managing every priority, every stakeholder conversations, firefighting escalations, and was still expected to operate alongside their peers as a strategic leader.

Add in hands-on technical work and constant context switching, they were utterly overwhelmed.

They asked me a simple question that I often hear from data leaders:

“How do I get my team to be more proactive and closer to the business, so that I can focus where I need to?”

Unfortunately, this pattern shows up when teams are expected to operate with judgement and autonomy, but have never been given a clear structure for doing so. When that gap exists, even highly capable people default and fall back on to what they’re already good at: process and tickets.

These challenge occur regardless of teams structures as well (I wrote about these yesterday)

Over time that quietly erodes both performance and sanity and create enormous headaches for ambitious data leaders.

Let’s take a look 👇🏻

^{Strategies for Effective Data Leadership is brought to you by:}

Clean Data. Clean Exit.

Crawford McMillan helps CEOs and private equity firms make their numbers stand up when it matters most.

As companies scale or prepare for investment, their data often becomes fragmented and unreliable. Finance, operations, and sales tell different stories. Reporting is slow. Forecasts lack credibility. In high-stakes moments like diligence or exit prep, those gaps translate directly into risk and lost value.

Crawford McMillan was founded by Graeme Crawford, a former Fortune 100 data transformation executive with two decades of experience leading complex analytics and data programs. Today, he and his team work directly with CEOs and PE firms to clean up data foundations, align metrics across teams, and produce investor-grade reporting that holds up in boardrooms, buyer meetings and negotiations.

The result is faster answers, fewer surprises, and a clear data story that supports confidence when valuation is on the line.

Take your data exit-readiness quiz now!

P.S. You too can sponsor this newsletter. The sooner you do, the cheaper it is. Learn more here

The challenges this dynamic raises

When teams default to passivity and wait for work to arrive in the form of tickets, the consequences tend to show up slowly at first, then all at once.

Here’s are the impacts we typically see from this. I imagine some of you are already feeling a few of these:

• The data leader becomes the single point of judgement for everything that matters, which means priorities, stakeholder expectations, and trade offs all bottleneck through one brain rather than being shared across the team.

• Stakeholder relationships weaken over time because data teams never build enough context to anticipate business needs or spot issues early. This leaves the business feeling unsupported and the data function constantly in reactive mode.

• Decision making slows to a grinding halt. It starts to rely purely on the individual leader rather than the team through shared ways of thinking. When the leader is unavailable or overloaded, progress stalls and confidence drops across the board.

• Technical capability is there and is growing, but noticeable impact made to the organisation isn’t. This creates a frustrating gap where the team is objectively better than ever at delivery, yet still perceived as reactive and operational rather than strategic.

• The leader’s role moves away from strategy and direction setting towards constant triage, which makes the job feel heavier, lonelier, and far less sustainable than it should be at that level. They also fail to match/achieve the level of recognition across the org as their BU peers do.

None of this happens because the team lacks talent or motivation. It happens because the team is not provided with clear expectations or frameworks for how they should engage with the business to better understand it, and the challenges it faces.

The good news is that this is fixable. Let’s take a look at how below.

Shifting the team from passive to proactive

Closing this gap matters because the moment your team can operate autonomously an with shared judgement, you stop being the engine that pulls everyone uphill and start becoming the leader who sets direction, like you should.

• The first step is making expectations explicit with your team members, so that stakeholder engagement, prioritisation, and decision making are clearly part of the role rather than optional extras that only confident individuals attempt on their own terms. Make this a part of the performance reviews and/or OKRs.

• From there, it becomes possible to give your team a common language and principals for talking about trade offs, prioritisation, impact, and value. This helps them move beyond simple task completion and start framing their work in terms of it’s associated business outcomes.

• Practical structures such as simple prioritisation principles, clear escalation paths, and shared decision criteria give people something to refer to for guidance, reducing the need for constant checking and approval from you, while increasing confidence across the team.

• Regular, supported exposure to stakeholder conversations helps them understand the business. Enabling team members to build this context gradually rather than being thrown into the deep end and retreating back to what they’re comfortable with: hiding being ticketing systems.

• Over time, these behaviours become ingrained, good judgement spreads and pressure eventually lifts from the leader. The team starts to feel like a genuine partner rather than a delivery function. This will do wonders for their motivation too.

This is the kind of shift that rarely sticks through one off training or well intentioned reminders. It requires space, structure, and consistency, all focused on how the team actually operates day to day.

That is exactly what my Data Team Accelerator is designed to support. It gives teams a shared operating model for leadership behaviours, communication, and decision making, so that progress does not depend purely on data leaders carrying everything.

If this story felt uncomfortably familiar, you are not alone, and you are not behind. You are simply at the point where the team needs a different system to grow into the role the organisation already expects them to play.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

^{READ TIME: 5 MINUTES}

🎯 In the firing line.

SaaS sales folks have done a number on the data industry. They’ve convinced us (and our business stakeholders) that the only thing between you and “data driven excellence” is their shiny new tool.

Millions upon millions are spent every year at conferences and expos designed to put data leaders and their juicy budgets firmly in the scope of tech sales teams.

Today it’s AI FOMO, a few years ago it was self-service BI, before that, Big Data.

Each of these waves have reframed their associated tools and tech as the goal themselves, rather than as a means to improve decisions, strategy, or performance.

And data leaders, as well as their business counterparts, have taken the bait, hook, line and sinker.

The problem this presents is that we find ourselves on a technology treadmill that’s just getting faster and faster. Constantly taking our attention away from the real reasons we invest time, energy and money in data: to deliver strategic outcomes for our businesses.

Today I’m taking a look at how we got here, and how the hell we get back!

^{Strategies for Effective Data Leadership is brought to you by:}

How much could AI save your support team?

Peak season is here. Most retail and ecommerce teams face the same problem: volume spikes, but headcount doesn't.

Instead of hiring temporary staff or burning out your team, there’s a smarter move. Let AI handle the predictable stuff, like answering FAQs, routing tickets, and processing returns, so your people focus on what they do best: building loyalty.

Gladly’s ROI calculator shows exactly what this looks like for your business: how many tickets AI could resolve, how much that costs, and what that means for your bottom line. Real numbers. Your data.

See your savings

(Did you know: You too can sponsor this newsletter!)

The real cost of Shiny Object Syndrome

Let’s talk about what this tech-first obsession actually does to you, and your organisation. This is not a neutral distraction. It comes with a huge bill (and I’m not just talking about the massive SaaS fees!)

Let’s take a look at the impacts of this:

🛍️ Strategy gets replaced with shopping

Instead of asking “what decisions matter most this year?”, conversations move towards “what stack should we be using?”

Data strategies become nothing more than vendor comparison exercises. You end up optimising the architecture before you’ve agreed what success and progress for the business even look like.

🏭 Data teams become feature factories

Teams get pulled into building capabilities nobody has clearly asked for, because “it’s foundational” or “we might need it later”.

You ship dashboards, models or pipelines, but struggle to point to either their strategic purpose or any decisions that actually changed because of them.

😵‍💫 Executives confuse activity with progress

New tools create the illusion of momentum, “we’re busy building!”

Budgets get approved and demos look impressive. But meanwhile the underlying problems remain unchanged because nobody managed to define them properly. ROI remains elusive (let’s hope no one brings it up!)

😰 You lose credibility when value stays vague

After the third or fourth “transformational” platform fails to transform much of anything, trust in data teams is completely destroyed.

Suddenly data feels too expensive, hard to explain, and utterly disconnected from strategy. And this is where heads start to role. Is there any wonder why CDOs have the shortest tenures in the C-suite?

The good news is this is all fixable. You just have to step off the damned treadmill.

Breaking the techno-centric mindset.

Most data initiatives fail because they are framed as technical work instead of decision support. Fixing that requires changing how work gets defined and approved.

📐 Define the decision first
Every data initiative should start with a clear statement of the decision it supports.

👉🏼 Who makes the decision?
👉🏼 How often they make it?
👉🏼 What information do they rely on today.?
👉🏼 What is missing or unreliable?

If you cannot answer those points, the initiative is not ready to move forward.

🪢 Tie the work to the org strategy execs have already laid out
Describe the expected impact using measures and KPIs that underpin the strategic pillars the business is trying to drive forward.

At the highest level it is typically: Revenue growth, cost reduction, risk exposure, operational efficiency, customer retention etc.

If the benefit only makes sense when explained alongside a specific tool, then the link to value is too weak and the strategic alignment is NOT there.

👩🏽‍💻 Create space between problem understanding and solution design
Technical teams usually move quickly to implementation. That speed often can hide poor problem definition.

👉🏼 Slow this phase down deliberately.
👉🏼 Test assumptions.
👉🏼 Compare alternatives.
👉🏼 Ask what happens if nothing changes?

Most organisations over-invest in tools because no one challenges this initial framing. make that YOUR job.

Select technology based on its ability to support decisions and strategies
Once the desired outcome is clear, technology choices become far more straightforward.

👉🏼 Will it improve the quality of the decision?
👉🏼 Will it reduce the time to make it?
👉🏼 Will it reduce effort or cost in a measurable way?
👉🏼 Will it help us achieve a strategic goal?

If the answer is unclear, the technology is not the priority.

Good luck out there!

-

I hope this has given you a concrete way to push back on tech-led distractions and to lead with strategic outcomes in your tech evaluation process.

Remember, instead of asking “what else could we build?”, shift the conversation to “are these the right business priorities?”

That’s how data teams stay aligned to strategy and avoid the next tool wave.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: What do you think exec teams care about most in data conversations?

Here’s how you responded:

^{READ TIME: 6 MINUTES}

☠️ Where great data initiatives drop dead

Today I want to talk about a very specific frustration I hear from ambitious senior data leaders.

It usually sounds like this: “I’ve got great ideas. Solid initiatives. Clear upside. But when I take them to the exec team… they just don’t land.”

Recently, a client asked me, “What’s the best way to pitch initiatives to execs so we actually get support, buy-in and budget?”

The ideas they planned to pitch seemed excellent to me. But the pitch itself was focused on the wrong thing.

They were trying to sell the execs on what they cared about (new functionality, capabilities and features), rather than what the exec team cared about.

And sadly, that’s how good initiatives quietly die. Not because they are bad ideas, but because they are framed in a language the room does not reward (or understand).

^{Strategies for Effective Data Leadership is brought to you by:}

Shoppers are adding to cart for the holidays

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

“Interesting, but not now”

I’ll be blunt. Exec teams are not sitting around thinking about data capabilities, platforms, or what might be technically possible in the future.

They are thinking about a very small set of things:

👉🏼 What moves the strategy forward?
👉🏼 What reduces risk?
👉🏼 What makes the business more money?
👉🏼 What helps me sleep at night?
👉🏼 What’s my bonus gonna be next year?

When data leaders pitch initiatives without anchoring to those questions, a few things happen.

The impact of pitching the wrong benefits

Impact #1
The initiative(s) gets labelled as “nice to haves”. If the business value is not obvious from the outset of your pitch, it will quickly slide down the priority list. And we walk away from the meeting feeling absolutely deflated.

Impact #2
You wind up sounding more operational than strategic. Talking about tools, pipelines, or capabilities to the wrong audience positions you as a delivery leader, not a peer in their room. To but it bluntly, it can be seriously career limiting.

Impact #3
Budget conversations do not go your way. Execs will be listening out for strategic assurances and outcomes — AKA ROI. If the return is unclear, the investment simply won’t be coming your way.

Impact #4
You lose momentum and credibility as a leader. After a few lukewarm receptions, people stop leaning in when you speak…they’ll be on their phones checking emails or making lunch plans instead.

The most frustrating part? None of this necessarily reflects the quality of the idea itself (assuming it is a good one).

The problem is simply framing.

How senior data leaders actually win exec buy-in

At the exec level, strategy is the key. If your idea cannot be clearly mapped to it, you are asking the room to do extra thinking on your behalf.

And they do not like that!

Here’s a simple way to flip your approach.

Step 1: Start with the strategic pillar, not the data idea

Before you start writing your pitch, ask yourself:

• Is the business prioritising growth and market share?

• Is the focus on profitability and customer lifetime value?

• Are they trying to raise capital or attract investors?

• Is it about cost control, efficiency, or risk reduction?

• Or some other strategic pillar?

Which ever it is, you initiative should clearly map to the org’s started strategic objectives. This is exactly what they have already told you is their top list of priorities.

If it doesn’t, you may need to ask yourself if this is actually something the business needs/wants right now…

Step 2: Translate your idea into a business bet

Execs are constantly making bets.

So frame yours like one.

❌ Not: “We want to build X capability.”

✅ But: “If we invest £X here, we expect Y impact on revenue, cost, risk, or growth.”

Remember your stating your hypothesis here, not making a promise. It’s a bet after all.

Step 3: Match the story to the strategy

Some examples:

• If the strategy is about increasing market share:
  Talk about greater customer acquisition and beating competitors to action.

• If the strategy is improving profitability:
  Talk about churn reduction, pricing optimisation, and margin protection.

• If the strategy is fundraising or a better valuation:
  Talk about confidence for investors, defendability and strengthening USP

• If the strategy is about improving growth
  Talk about acquisition efficiency, retention, and scalable decision making.

Same data work, but a very different story being told.

Step 4: Leave the techy stuff until they ask

This is the hardest part for data leaders, particularly the technical among you.

Exec buy-in is not earned by demonstrating how smart the solution is.
It is earned by showing you understand the business as deeply as they do.

If they want detail, they will ask. And when they do, you will sound even stronger.

So whack that detail it in the appendix and be prepared to talk about if requested, but don’t make it the focal point by any means.

The real shift senior data leaders need to make

At this level, the question is no longer “Is this a good data idea?”

It is: “Does this help the organisation win, right now, on its stated terms?”

The data leaders who progress fastest are the ones who answer that question first.

If this is the layer of your role you are wrestling with, this is exactly the kind of work I do with senior leaders behind closed doors. I help data leaders and their teams around the world become more strategic and influential.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: If you were promoted from IC to manager, what was the hardest adjustment?

Here’s how you responded:

^{READ TIME: 4 MINUTES}

Manager or just highest paid IC?

Today I’d like to talk about one of the biggest traps in data leadership. A trap that turns bright, ambitious data professionals into overworked, thinly stretched managers who are stuck reacting to everything instead of influencing and shaping things.

The trap is simple: It frequently occurs when the data leader also happens to be the most technical person on the team, and for that reasons remains relatively hands on.

Why is this a trap?

Because if you are stuck writing SQL, debugging pipelines and building half the dashboards your team were hired to produce, you’re hardly operating as a manager. You’re not building capability, you’re not elevating your team and you’re certainly not being seen across the business as a strategic peer.

You’re simply the highest paid individual contributor (IC).

Unfortunately, this is exactly what happens when companies reward the top individual contributor by promoting them into a management role without actually teaching them how to manage. A classic example of the Peter Principal in action.

When fantastic ICs are promoted to lead their team without sufficient guidance on how to be a manager, the transition can be very tough.

^{Strategies for Effective Data Leadership is brought to you by:}

Shoppers are adding to cart for the holidays

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

Why this kills your effectiveness as a leader

Here is what this dynamic creates for your career and your team.

👩🏼‍🔧 Impact 1. You get dragged back into IC mode
Because you’re the fastest and most capable technically, your team naturally lean on you for help. Before long, you’re knee deep in stakeholder data request tickets, switching from high level architecture thinking to wrangling data within the same hour. Your strategic responsibilities take a back seat and the business sees you as a doer, not the leader they’re looking for and need.

🚌 Impact 2. Your team stop developing
If you keep jumping in to fix problems because you can do it faster, your team never get the chance to properly grow. Their learning stalls, their confidence dips and you become a bottleneck without even realising it. Aka Key Person Dependency Risk or Bus Factor.

🦮 Impact 3. Micromanagement creeps in
Managers who stay too deep in the weeds often end up controlling every decision. Not intentionally. It just happens. The message your team hears is that their judgement is not trusted, only your is. That erodes psychological safety and eventually kills team morale. Your folks will move on before you know it.

🛠️ Impact 4. Your peers do not see you as a peer
It’s almost impossible to be accepted as a strategic leader when half the company only ever sees you with your sleeves rolled up trying to fix broken models. You need to show up at a different level and you simply cannot do that if you are always in the weeds. To but in bluntly, behaving like this is a career limiting move.

Good news. This is all fixable. And you can start right now.

How to break out of the technical manager trap

The reason this matters is simple. Until you step out of IC mode, you cannot step into true leadership. And until your team is able to grow without you, you will stay stuck at your current level.

Here is how to change the dynamic.

1. Strive to be the least technical person in your own team
This is not a humiliation. It is the goal. Your job now is to nurture people who are better than you at these things. That is what high performing leaders do. They aim to be the dumbest person in the room.

If your team outgrow you technically, congratulations. You are doing it right.

2. Delegate properly and allow things to take longer than you would like
Yes, it is slower at first. Yes, your inner perfectionist will scream. But this is how people learn. Think of it as compound interest on your future capacity. Every hour you spend coaching them to be better is one hundred hours you get back later.

3. Time box your technical contributions
Work backwards from your true responsibilities: Stakeholder management. Strategic planning. Building alignment. Coaching and mentoring. Then decide how much time is left for hands on work. Maybe you shift from fifty percent hands on to twenty percent. Maybe to ten. But decide the boundary and stick to it. Let your team pick up the slack!

4. Redefine your identity. You are no longer the athlete. You are the team coach.
Your job is not to score goals. Your job is to create the environment where others can. When the team win, you win. If you keep kicking the ball yourself, you are holding everyone back, including you. Get off the damn field.

👉🏼 See my recent post on overcoming “manager’s guilt”

5. Build systems that reduce dependency on you
Clear ownership. Documented processes. Structured intake. Defined decision rights. Create scaffolding that allows your team to operate without constant managerial intervention from you. Delegate authority to certain individuals to make certain decisions without needing to consult you.

6. Invest heavily in one to ones
Most new managers wing it. Do not wing it. Use one to ones to build trust, coach problem solving skills, understand skill gaps and help people build confidence. When your team grow, you grow.

If you can do even two of these consistently, the entire shape of your role will change. The business starts seeing you differently. Your team start stepping up. And you finally get the headspace to operate as the leader you were promoted to be.

Good luck out there, and if you need any help with this. I’m your guy.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: What’s the biggest blocker to aligning your data strategy with your organisation’s actual strategy?

Here’s how you responded:

For most folks, their is either no clear strategy in existence, or you don’t have the headspace to align with it. These are not good to hear…

^{READ TIME: 5 MINUTES}

Data strategy is not a Christmas wish list.

Today we are tackling one of the most misunderstood and abused phrases in our industry: data strategy.

Every man and his dog on LinkedIn loves telling you what data strategy is not. But very few come forward to explain what it actually is. Most data strategy explanations descend into a wash of buzzwords and architecture diagrams that look like they belong in a NASA control room.

So today, we’re gonna fix that.

(Check out this earlier post on the difference between strategy and tactics)

Put simply, a data strategy is: “the plan for how your organisation will use data to create business value”. 

Nothing crazy. Nothing obscure or complex. Just a clear set of choices that help you influence revenue, reduce costs, manage risk or improve customer outcomes - powered by data.

If you get this right, your data team becomes a strategic partner that helps shape business performance and overarching strategy.

Get it wrong and you’ll likely spend the next two years building platforms no one needed and trying to justify why there is no ROI for all of your extremely expensive data toys.

Sadly, most data strategies fall apart because they are execution details, basically a list everything the team could do, instead of making the few focused choices that help the business win.

Let us look at the real impacts when your so called strategy is actually just a Christmas wish list of toys you want from Santa.

^{Strategies for Effective Data Leadership is brought to you by:}

Shoppers are adding to cart for the holidays

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

What happens when we get it wrong?

Here are the problems you create for yourself and the business when you misinterpret data strategy:

☹️ Impact 1: You build everything except what the business needed
A long list of projects or tools is not a strategy. It’s execution detail that sits way downstream from the actual strategic layer. What you plan to build as part of your strategy is actually the last piece of the puzzle - not the first.

☹️ Impact 2: You confuse activity with progress
Your people stay very busy, but nothing shifts the dial. We love demonstrating tangible output, and strategy can often feel like something that’s quite intangible. But beware, this is a trap.

☹️ Impact 3: You burn goodwill with the exec team
Expectations rise, results do not. At some point the CFO asks where the impact is. No one enjoys this conversation and it is one of the key reasons why Chief Data Officers are famously the shortest tenured C-suites. ROI matters to execs.

☹️ Impact 4: You stretch your team too thin
Without clear choices, everything becomes equal priority. Morale sinks, quality drops and you become a ticket machine instead of a strategic function, led by the needs of others rather than the org’s strategy.

☹️ Impact 5: No one knows what success looks like
If you cannot measure and demonstrate success, the business assumes failure. Organisational strategic goals need KPIs and measurement. Data teams play a crucial role in this. We hold a mirror up to the business that lets it know how it’s tracking against it’s strategy.

While these challenges are many, fear not. They are fixable.

Here’s how we can fix this.

A good data strategy does one thing: it creates clarity. It makes it painfully obvious what you will do, why you will do it and how it helps the business win.

Here is the simple way to build one.

🛠️ 1. Start with the business goals
What specific outcomes is the overall organisation trying to influence? Growth, efficiency, risk, customer retention? If your data strategy does not anchor to the relevant strategic pillar, it is already lost. Your org strategy is 100% your starting point, so get clear on it!

🛠️ 2. Identify the data required to influence those goals
Strip it back to basics. What information do we need to understand, track or predict in order to help the business succeed? This is a commercial thinking exercise so put your CEO hats on for this.

🛠️ 3. Define the few capabilities that actually matter
What skills, roles, tools and processes are essential for the value you want to unlock? It’s often phrased as People, Process and Technology. The key thing to remember her is the technology is the LAST piece of the puzzle.

🛠️ 4. Decide your priorities and trade offs
Strategy is choice. You cannot do everything. What are you intentionally saying yes to, and what are you saying no to? This is where teams grow up and start performing. Do not do things that the business has not asked you to prioritise via its strategy. Aka - if it is not in the biz strategy it is not on the to-do list! That’s the fast lane to critical failure.

🛠️ 5. Create an operating model that makes the work flow
Who owns what? How are decisions made? How does data move from source to insight to action? What is the mechanism for partnering with the various business domains and ensuring the right data is a part of the critical conversations?

🛠️ 6. Establish how you will measure success
If your strategy is anchored to business goals, your success measures need to be anchored there too. What are it’s strategic goals and what KPIs or metrics are needed to understand how the business is progressing towards those goals? Decide which KPIs or business metrics you expect to influence and how you will track that influence over time. This is your proof that the choices you made in the strategy were the right ones. If you cannot show movement in a metric the organisation cares about, the strategy needs rewriting.

And there you have it.

A data strategy. Not just a slide deck. Not a glorified shopping list. A set of deliberate choices that position your team as a strategic partner instead of a cost centre.

💡 Oh, and don’t forget to tell people what your strategy is!!

Buy-in and alignment are crucial!

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

Previous poll results

Last week I asked you: What is the biggest reason data teams jump into solution mode too early?

Here’s how you responded:

You overwhelming voted stakeholders asking for outputs rather than out comes.

^{READ TIME: 5 MINUTES}

🧠 Stop solving problems you don’t understand

If you want to deliver data work that actually matters, start by asking better questions.

Too many data teams jump into solution mode the moment a stakeholder says “I need a dashboard,” debating tools and architecture before they understand the real problem.

But no one stops to ask what the dashboard is meant to change. Or if a dashboard is even needed in the first place (usually not).

Skip the questioning part, and you burn time, burn trust, and build solutions that fix nothing.

In today’s edition of Strategies For Effective Data Leadership, we’ll unpack why good questions matter and how to ask them.

The Tech newsletter for Engineers who want to stay ahead

Tech moves fast, but you're still playing catch-up?

That's exactly why 100K+ engineers working at Google, Meta, and Apple read The Code twice a week.

Here's what you get:

• Curated tech news that shapes your career - Filtered from thousands of sources so you know what's coming 6 months early.

• Practical resources you can use immediately - Real tutorials and tools that solve actual engineering problems.

• Research papers and insights decoded - We break down complex tech so you understand what matters.

All delivered twice a week in just 2 short emails.

Join 100K+ engineers

😩 The cost of bad questions

Here are a few consequences of not interrogating a business ask properly:

Impact 1: You build something that looks good but fixes nothing
Perfect design. Zero strategic impact. No stakeholder alignment.

Impact 2: You trap your team in endless rework
Not asking great questions and understanding the challenge from the start means you’ll be constantly iterating as feedback is drip fed.

Impact 3: You create a dependency loop
Stakeholders lose confidence in your work. They’ll start checking everything and question every single number. You and your teams credibility tanks big time.

Impact 4: You reinforce the stereotype that data teams are technical operators
Executives start to believe the narrative that data people cannot think commercially. Which is ironic because most of us absolutely can. We just do not slow down long enough to ask the right questions.

Good news. You can fix all of this. It starts with becoming the kind of data leader who interrogates the business problem properly before even thinking about solutions.

But first, let’s all get poll-yamorous 👇

💪🏻 How to ask better business questions

Before you touch SQL, open a Jira ticket or architect anything, you need to slow everything down. Your job is to understand the business context so deeply that the right solution becomes obvious.

Here is how to do that.

1. Always ask what outcome they want, not what product they think they need
If they say they want a dashboard, ask what they want to happen as a result of that dashboard.

• What decision will this inform?

• What behaviour do you want to change?

• What metric should move and by how much?

Do not let them anchor you to the output. Pull them back to the outcome.

2. Ask them to show you the pain, not their wish list
Real business needs are born out of pain. Ask:

• What is slow?

• What is costing money?

• Where are you losing customers?

• What are your competitors doing better than you?

• Make them show you the suffering. That is where the real opportunity is hiding.

3. Ask what they have tried before and why it did not work
Executives hate admitting when something flopped. But you need this context. It helps you avoid repeating the sins of the past and wasting everyone’s time.

4. Ask who owns the decision and who is affected by it
A single request often touches multiple teams. If you do not map the ecosystem at the start, you will be slapped with conflicting needs halfway through the project. make sure you understand the lay of the land.

5. Ask what success looks like and what failure costs
This one changes everything.

If the business cannot articulate what winning looks like, the project is no worth pursuing. And if they cannot articulate what is at stake if nothing changes, then it probably is not a real priority.

6. Translate everything into a commercial frame
Once you have the answers above, turn the problem into a commercial statement.

For example: Instead of building a churn dashboard, you are helping the business retain 3 percent more customers in Q1 and protecting seven figures of revenue.

Now the work actually matters.

If you want to be seen as a strategic data leader, you cannot skip the questioning. Better questions lead to clearer problems. Clearer problems lead to higher impact solutions. And higher impact solutions turn your data team into a strategic partner rather than a ticket service.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

^{READ TIME: 5 MINUTES}

84% Deploy Gen AI Use Cases in Under Six Months – Real-Time Web Access Makes the Difference

Your product is only as good as the data it’s built on. Outdated, blocked, or missing web sources force your team to fix infrastructure instead of delivering new features.

Bright Data connects your AI agents to public web data in real time with reliable APIs. That means you spend less time on maintenance and more time building. No more chasing after unexpected failures or mismatches your agents get the data they need, when they need it.

Teams using Bright Data consistently deliver stable and predictable products, accelerate feature development, and unlock new opportunities with continuous, unblocked web access.

Power Your AI

₍🙏 _{checking out ads helps me keep this newsletter alive and free)}

 🧠 The entrepreneurial mindset

If you want to become a genuinely influential data leader, perhaps it’s time to start thinking like an entrepreneur.

Entrepreneurs create value where none existed before. The best data leaders do exactly the same. They take raw and unorganised data and turn it into something that either makes the business more money, saves the business money, or stops the business from making huge mistakes.

This mindset shift matters because organisations are finally waking up to the fact that technical delivery is not enough. Anyone can build a dashboard. Anyone can buy a modern data stack. Very few people can turn those investments into a commercial advantage. Those who can are the ones who get invited into the decisions that actually shape the business.

Unfortunately, most data leaders are still stuck behaving like caretakers instead of creators. They prioritise platforms over profit, pipelines over performance, and dashboards over decisions. They spend their careers trying to perfect the back end when the real influence sits at the business end. And yes, this absolutely limits your career. You cannot climb the ladder if the exec team only sees you as the person who delivers dashboards on time. You rise when they see you as the person who helps the company win.

Here is how to shift into an entrepreneurial mindset and start making yourself indispensable.

😩 What happens when you lack an entrepreneurial mindset

• You become the caretaker of data instead of the creator of value.

• You get stuck in technical delivery while the commercial conversations happen without you.

• You become known for dashboards instead of decisions.

• You miss opportunities to tie your work to revenue, margin, customer value, or risk reduction.

• You struggle to get budget because you cannot demonstrate commercial impact.

• You quietly fall behind peers who are more comfortable playing in the business playground.

The good news is this mindset can be learned. And once you embrace it, doors open that have been welded shut for years.

💪🏻 How to become a more entrepreneurial data leader

1. Start with value, not requirements
Before you ever discuss data sources or pipelines, ask a simple question: what commercial outcome are we trying to unlock and what strategic pillar does it align with. Entrepreneurs start with value and work backwards. You should too.

2. Create a product vision for your data capability
Treat your data function like a portfolio of products. Position it clearly. Sell it internally. Show stakeholders what problems you solve, how you reduce pain, and how you generate upside. Speak in terms of commercial outcomes where possible.

3. Tell commercial stories, not technical ones
Swap talk of joins and warehouses for narratives that matter. Faster sales cycles. Lower churn. Reduced acquisition costs. Better underwriting decisions. More transactions. Executives do not want features. They want outcomes they can take to the bank.

4. Spend more time with the business than with your dashboard backlog
Entrepreneurial leaders understand their partners deeply. For you, those partners are the commercial teams. Sit with them. Shadow them. Learn their world. They will tell you exactly where the money leaks out, so you can set about solving it.

5. Seek out revenue generating opportunities
An entrepreneurial data leader is constantly scanning the business for underused assets, pricing inefficiencies, funnel drop offs, process bottlenecks, or places where the company flies blind. Then they pounce.

6. Optimise for speed, not perfection
Entrepreneurs get value in front of customers quickly. You should do the same. Ship useful insights fast rather than waiting six months for the perfect model. Commercial teams reward speed.

7. Influence through confidence, clarity and conviction
Entrepreneurs sell. They pitch. They persuade. They help others see what they see. The best data leaders do the same. They champion their vision and make it very clear why the organisation should listen.

If you want to be influential, get promoted, and have a seat at the table for the decisions that matter, adopt this mindset. Stop running the data factory. Start acting like the founder of a value generating business that just happens to sit inside your company.

If you need help figuring out how to do this, then I’m your guy.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: How far along is your organisation when it comes to data democratisation?

Here is how you responded:

^{READ TIME: 5 MINUTES}

Shoppers are adding to cart for the holidays

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

₍🙏 _{checking out ads helps me keep this newsletter alive and free)}

👨🏼‍💻 Is data democratisation a good thing?

Yes, of course it is.

Enabling the organisation access to its data should be the goal of every data team. Without the ability to check and interrogate data on their own, business stakeholders invariable come directly to data teams to have them provide it.

This positions the data team as a huge bottleneck and key dependency in helping the business get their hands on data.

When done right, democratisation builds trust, transparency, and speed of decision-making.

BUT (you knew there was a a big ‘but’ coming didn’t you?)

When done badly, data democratisation can fuel chaos, confusion, and political tension faster than you can say “single source of truth”.

“Wubba Lubba Dub Dub!” - wait wrong Rick…

Data leaders the world over see data democratisation as a key goal for their teams, whether they believe it is right or they’re being pushed aggressively by the business to achieve it.

In today’s edition we’re gonna dissect the concept of data democratisation, look at it’s potential shortfalls, and go from there to see what a success data democratisation approach might look like.

Buckle up!

😱 “My analysis is better than yours!”

Here’s what happens when the push for self-service goes wrong:

🤥 1. Conflicting truths start circulating
Without shared definitions, governance, and alignment on key metrics, you may end up with multiple versions of reality. Two people can look at the same dataset and walk into a meeting with entirely different numbers. Suddenly, your execs are arguing about which analysis is right rather than what the data means!!

🥊 2. The data team loses credibility
When stakeholders build their own reports and misinterpret the data, it quite often reflects poorly on the data team. You become the scapegoat for “bad data” even though the issue was misuse, not quality. This erodes trust and turns the data function into a convenient punching bag.

🫥 3. Strategic focus disappears
Instead of reducing workload, poorly executed democratisation can actually increase it. The data team spends more time fixing mistakes, clarifying definitions, and cleaning up after stakeholders who “went rogue”. Strategic projects stall because you’re too busy firefighting.

😖 4. Politics, not insight, drives decisions
Inconsistent data access creates power struggles. Certain teams or leaders start promoting “their” numbers that best support their narrative. Once politics take over, the data team’s ability to influence with objectivity plummets.

💔 5. You expose the cracks in business maturity
If the business isn’t ready either culturally or operationally for self-service, democratisation will simply expose weak data literacy, inconsistent decision-making, and a lack of accountability. It’s like giving everyone a Ferrari before they’ve passed their driving test.

The result the end up with is: More dashboards, fewer decisions, and a growing perception that the data team can’t deliver value.

👨🏻‍🔧 Here’s whatcha gotta do

Strong data leadership means knowing when not to give the business what it’s asking for. Democratisation is an earned capability. And earning it requires collaboration, governance, and maturity on both sides.

Here’s how to make that happen.

📏 1. Start with alignment, not access
Before anyone gets the keys to the data, align on the fundamentals: business logic, KPIs, metric definitions, and calculation methods (aka business logic). Every team must speak the same language before they start exploring the data. This is not a technical exercise; it’s a leadership exercise. Facilitate workshops where finance, marketing, and ops all agree on what “revenue,” “conversion,” or “churn” actually mean. (BTW I can help with this so hmu if you wanna chat)

👨🏼‍💻 2. Redefine the data team’s role
The best data leaders position their teams as enablers rather than gatekeepers. Your job is to coach the business to use data responsibly, not to guard it behind a wall. Build frameworks for quality assurance, version control, and metric ownership that make it easier for non-data folks to stay aligned without constant hand-holding.

🔋 3. Set guardrails that empower, not restrict
Self-service does not mean free-for-all. Implement sensible permissions, automated data quality checks, and clear sign-off processes for new metrics or dashboards. Guardrails protect both your data team’s reputation and the business’s confidence in what they see. You must remain in the loop.

🤝 4. Develop real collaboration rituals
Democratisation only works when the business and data teams are in regular, structured dialogue. That could mean a monthly “data council,” cross-functional working groups, or shared OKRs that tie data initiatives to business outcomes. These rituals turn alignment into a valuable habit.

✋🏼 5. Be brave enough to say “not yet”
This is where leadership and political capital come in. Sometimes, the smartest move is to slow things down. If your organisation doesn’t have the governance, literacy, or accountability in place, you’re better off delaying democratisation until the groundwork is solid. Saying no (or not yet) pure leadership!!

Data democratisation can transform how your business uses data. But without the right maturity, it’s like handing out matches in a fireworks factory.

Before opening the floodgates, build the trust, alignment, and collaboration your organisation needs to use data wisely.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: What’s the hardest part of helping your data team communicate effectively with business stakeholders?

Over 50 of you voted and you responded with some very interesting split!

^{READ TIME: 5 MINUTES}

The Gold standard for AI news

AI keeps coming up at work, but you still don't get it?

That's exactly why 1M+ professionals working at Google, Meta, and OpenAI read Superhuman AI daily.

Here's what you get:

• Daily AI news that matters for your career - Filtered from 1000s of sources so you know what affects your industry.

• Step-by-step tutorials you can use immediately - Real prompts and workflows that solve actual business problems.

• New AI tools tested and reviewed - We try everything to deliver tools that drive real results.

• All in just 3 minutes a day

Join 1M+ pros

₍🙏 _{checking out ads helps me keep this newsletter alive and free)}

👨🏼‍💻 I’m not going to treat you like an idiot.

Most data leaders already know this: if your team cannot connect their work to business outcomes, they will always be treated as a service function.

You know it. Your peers know it. The problem is, your team often does not.

They are smart, capable, and brilliant at the technical side of things. But ask them to explain their impact to a senior stakeholder and they start describing pipelines, models, or processes instead of outcomes, value, influence, or strategic relevance.

It’s not because they don’t care, but because no one has ever taught them how to think and communicate in business terms. It’s not a common or natural skill for data folks.

Home is your business stakeholder…

Unfortunately, this is one of the hardest transitions to coach your team on.

Until now of course.

In today’s edition, we’re going to break down how to actually help your team shift their mindset and behaviour.

🧠 Why your team struggles to think like the business

Your analysts and engineers live in a world of accuracy, efficiency, and proof. They thrive on solving complex problems, but the art of persuasion doesn’t come naturally to them.

So when you ask them to speak the language of the business, they hear:
“Stop being technical”, which often goes against their very nature.

To them, it can feel like you are asking them to stop being good at their jobs. And this is one of the key reasons this shift does not stick. You are asking them to rewire what they believe creates value.

If you want to change that, you have to show them that business fluency is part of technical excellence, not something separate from it.

In other words, they’re not wrong, they just have more to learn (as well all do).

🏗️ How to build business-centric thinking in your data team

1. Start by showing, not telling.
Run a short team session where you take a real project and ask the group to write two summaries:

• One that describes what was built.

• One that describes what impact it created.

Read both versions aloud and ask, “Which of these would make the CFO care?”

This makes the difference between outputs and outcomes clear rather than theoretical.

2. Make business fluency part of your rituals.
If every stand-up, sprint review, or showcase only focuses on what was delivered, your team will assume that is what matters.
Shift your rituals so people start reporting on:

• What business problem did we solve this week?

• Who benefitted and how do we know?

• What is the next decision this enables?

At first, they will struggle to answer. That is fine. You are building a new muscle, not auditing a skill.

3. Use coaching questions instead of corrections.
When someone gives a highly technical update, do not jump in with “Make it more business focused.”
Ask:

• “Who is this update for?”

• “What do you think they care about most?”

• “How would you explain this if you only had 30 seconds?”

These questions help them think rather than just translate. Over time, they will start self-correcting before you even say a word.

4. Create stakeholder empathy exercises.
Once a month, have a team member shadow a stakeholder meeting, product review, or sales call.
Their task is to observe what people actually talk about, worry about, and celebrate.
Then, in your next team catch-up, ask them to share what they learned.

It is one of the fastest ways to close the empathy gap between data problems and business realities.

5. Reward impact, not output.
When you recognise people, highlight stories of business value, not technical brilliance.
Say, “Sarah helped Marketing cut campaign costs by 20%,” not “Sarah optimised a data pipeline.”

You are teaching the team what success looks like through what you choose to celebrate.

6. Build translation time into delivery.
When projects finish, do not just ship them and move on.
Hold a short retrospective where the team must answer:

• What was the business objective of this work?

• What decisions did it help enable?

• How will we measure ongoing value?

Turn those answers into talking points for your stakeholder updates.
Your team will start to see their work through the same lens as the business.

Helping your team become more commercially is about building a habit of curiosity, teaching them to look up from their code, ask better questions, and see their work as part of something bigger.

Do that, and you will have a data team the business actually listens to.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: What do you think is the biggest reason so many data leaders fail within 2 years?

Here’s how you responded…an overwhelming 45% of you said that it comes down to business stakeholders not knowing what they actually want!!

^{READ TIME: 6 MINUTES}

I’ll be attending and hosting a round table at the CDO BFSI Exchange in London in November. This is an invitation only event for CDOs in the UK Banking Financial and Insurance sectors. If you’re interested in attending, head here to grab a copy of the event brochure!

Looking forward to seeing you there!

💥 Data leadership hiring is broken

Let’s talk about a problem that’s quietly poisoning the well of the entire data industry.

The way organisations hire data leaders is fundamentally broken.

Most job descriptions for senior data roles read like a shopping list of technical requirements: cloud platforms, governance frameworks, machine learning, dashboards, warehouses, pipelines, lakes, meshes, and whatever the latest shiny acronym happens to be.

The underlying message is clear:

And so, that’s exactly what companies get.

They hire technical leaders who’ve spent years mastering the art of delivery. People who know how to design scalable architectures, migrate data to the cloud, and spin up a new BI platform.

But here’s where it starts to hurt: once they’re in the role, those same leaders are judged not on the quality of the platforms they build, but on the impact those platforms have on the business (aka cash money in most cases).

Eighteen months later, the CFO is asking the same question every CFO eventually asks:

And that’s when the wheels come off in a devastating fashion.

gets it yoda does

The data leader was hired to deliver a technical outcome, not a business one. They’ve been building pipelines when they should have been building influence. They’ve been optimising storage costs when they should have been optimising business decisions.

It’s not that they’ve failed. It’s that they were set up to fail from the very start.

The Gold standard for AI news

AI will eliminate 300 million jobs in the next 5 years.

Yours doesn't have to be one of them.

Here's how to future-proof your career:

• Join the Superhuman AI newsletter - read by 1M+ professionals

• Learn AI skills in 3 mins a day

• Become the AI expert on your team

Start learning AI now

_{(checking out ads is appreciated as they help keep this newsletter going - thank you)}

🔄 The cycle of broken expectations

Here’s how it usually plays out:

1. The company decides it needs to “get serious about data”.

2. They draft a job description focused on tools and technical capability.

3. They hire a technically brilliant leader who builds the technical foundations.

4. The exec team applauds... until they realise the business still isn’t more data-driven or moving in the right direction strategically.

5. They lose patience, blame the data leader, and start the cycle all over again - having learnt Sweet FA.

And with each spin of the wheel, our industry’s credibility takes another hit. Data gets seen less as a strategic enabler and more as a costly, complex, never-ending project that doesn’t deliver much in return. Just more pain.

When this happens across multiple organisations (and it does), it creates a systemic perception problem. Data is viewed as a cost centre rather than a growth engine. The technical bias in hiring decisions has built an entire ecosystem of well-intentioned leaders who are doing exactly what they were asked to do... just not what the business needed them to do.

set up to fail…

🔀 The double whammy: hiring managers and data leaders

The problem isn’t just on one side of the table.

On one hand, hiring managers keep making the same mistake when they equate data leadership with technical competence. They want strategic influence, but they’re hiring for technical mastery. It’s like expecting to win Formula 1 races by recruiting a mechanic to do the driving because they “know cars better”.

On the other hand, data professionals themselves often reinforce this cycle. Many of them believe that their value lies in what they build rather than what they enable. That mindset is deeply ingrained in the culture of data work, where success has traditionally been measured in dashboards deployed, pipelines built, and queries optimised.

But technical outputs don’t automatically translate into strategic outcomes.

‼️ Why this matters more than ever

The longer this continues, the more damage it does to the perception of data leadership. It makes the role look risky, high-turnover, and ultimately dispensable.

The truth is, businesses aren’t running out of patience because they hate data. They’re running out of patience because they can’t see the value in what’s being delivered.

And until more data leaders start positioning themselves as strategic partners who can help the business achieve its most important goals through data, the cycle won’t break.

🛣️ So where do we go from here?

If you’re already in a data leadership role, the first step is awareness. Recognise that you might have been hired for the wrong reasons, and that this not your fault. But it is however your responsibility to course-correct.

Start spending less time talking about data infrastructure and more time talking about business impact. Translate data work into commercial value. Make it clear how your team’s work supports the organisation’s biggest bets.

The data leaders who will survive and thrive over the next five years won’t be folks who’ve built the most advanced technical environments. Those folks will be long gone. The survivors will be the ones who tie every data initiative back to a clear business outcome and help their organisations win.

The future of data leadership lies with the ones who bridge the gap between data and strategy.

And until hiring managers start to value that over certifications and cloud credentials, we’ll keep repeating this same broken loop.

But if you’re reading this, you’ve already got an advantage. You’ve seen the trap. And that means you can start leading differently.

So start today.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week, I ask you: What’s the biggest frustration you face as a data leader right now?

Here’s how you answered…

^{READ TIME: 5 MINUTES}

👋 Data Leadership coaching with me.

Far out, it’s almost November.

Some people start to wind down at this time of year and say, “screw it”. That’s next year’s problem.

Others (people like me), see this time of year as a golden opportunity to finish strong and make sure we achieve the things we set out to do. These are my people.

If you feel like you’ve still got some juice in the tank and want to finish the year as strong as possible, then why not come work with me to give it a red hot go?

I have availability for a few new clients starting in November. Also, anyone who signs on with me now will lock in 2025 pricing!!

Book a free and informal 1:1 chat with me to discover ways I can help you make sure you finish strong in 2025. Find time with me here.

☠️ You’re fighting a losing battle.

At least that’s how it feel sometimes being a data leader.

And while we put on a brave face like the knight in the famous Monty Python sketch, many of us are bleeding out and unlikely to survive much longer in our roles (Particularly with AI breathing down our necks).

Becoming an effective and influential data leader is hard, but not impossible. Understanding the way forward, requires a sober look at the biggest challenges holding us back.

For this week’s edition, I’ve decided to compile a short list of the Top 5 most common challenges I see holding back ambitious data leaders from being the strategic powerhouses they know they can be.

Let’s take a look shall we?

🙅🏼‍♂️ 1. Companies saying they’re “data driven” when they’re not

PROBLEM:
Data Driven is an overused and misunderstood buzzword. In most cases, companies that call themselves ‘data' driven’ simply mean that they have data available to look at via dashboards.

Whether they are truly data driven will come down to how effectively data plays a role in the evaluation and decision making process - something which is much rarer.

IMPACT:
If leadership thinks they’re already data driven, they’ll stop investing in what it takes to actually get there. These companies will stifle investment in data and hold data leaders back, both their influence and their career progression.

WHAT TO DO ABOUT IT:
Call it out; tactfully.
As data leaders it is on us to hold decision makers accountable for their decisions and the data they used to make it. We should encourage transparency around the decision making process and ensure we have a seat at the table when data is being discussed.

👉🏼 See more on this here.

💸 2. Data teams are under-resourced and undervalued

PROBLEM:
Data teams are often seen as a cost centre, not a value creator. Budgets are squeezed, expectations are sky-high, and headcount requests get punted into “next quarter.” Or if you’re like me it’s next decade…

IMPACT:
When your team’s drowning in work and still expected to “add strategic value,” you end up doing reactive, low-impact reporting that convinces everyone they were right not to invest more. It’s a vicious circle that holds you back more than anyone.

WHAT TO DO ABOUT IT:
Make your wins visible and tie them directly to outcomes the business already cares about: revenue, efficiency, risk reduction, customer experience. Every conversation about data should centre on impact, not outputs.

The message you’re trying to land here is that the data team is a strong value contributor, just like marketing and sales. We’re not just an expense item.

👉🏼 See more on this here.

🕵🏻‍♀️ 3. Shadow data teams popping up everywhere

PROBLEM:
When the central data team can’t meet demand, other departments start spinning up their own mini data ops. Before long, you’ve got rogue analysts, conflicting metrics, and a Frankenstein stack of tools no one owns. Oh, and every number is being reported differently!

IMPACT:
Chaos. Duplication. Confusion. Suddenly, your team’s reputation tanks because “nothing matches.” Even worse, it’s proof to leadership that you’ve “lost control” of data because others have had to take matter into their own hands.

WHAT TO DO ABOUT IT:
You can’t stop it completely, but you can shape it.
If a team insists on running its own data projects, bring them closer, not further away.
Offer guidance, set some guardrails, and create shared definitions. Better a loosely federated model than a wild west.

Did you know - I run a book club for Bad Asses?

Each month members vote on a non-fiction book to read together. We also have a private discord channel where we chat about the book and share insights. At the end of the month, we come together for a book club video call. This month, we’re read Quit by Annie Duke.

If you’d like to join the Bad Ass Bookshelf - check it out here.

☎️ 4. The data team has become a service desk

PROBLEM:
You know the drill. Endless Slack messages: “Can you pull this number?” “Can you slice that dashboard by region?” It’s ad hoc hell, and it keeps your team reactive instead of strategic. A.K.A. death by 1,000 paper cuts.

IMPACT:
Morale plummets. Your team stops thinking critically about the business context and starts behaving like report monkeys. You lose credibility as a strategic partner and become the data equivalent of IT support.

WHAT TO DO ABOUT IT:
Teach your team to ask why. Every time a request lands, train them to understand the business question and strategy behind it. Build processes that triage and prioritise work based on value, not output volume. You’re not there to serve requests, you’re there to shape decisions and solve problems.

👉🏼 See more on this here.

🛠 5. Believing every data problem can be solved with more tech

PROBLEM:
Too many leaders think the answer to data challenges is more data tech. If you’ve ever heard “we just need a better tool for this,” you know exactly what I mean. The things is that tools themselves rarely solve problems. They’re a distraction from the actual problem, but nonetheless feel like progress.

IMPACT:
Millions get poured into technology with no real shift in behaviour, culture, or outcomes.
When the ROI doesn’t materialise, confidence in data drops even further. Cue another tool purchase (and probably a new data leader while we’re at it).

WHAT TO DO ABOUT IT:
Shift the conversation. Data is a business discipline, not a technical one.
Start with strategy: what are we trying to achieve? Then identify the data needed to support it. Only once you’ve nailed that should you ask, “does this require new tech?” and if so, “what tech do we need?”

Spoiler: it’s usually less than you think.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: What’s your next big career focus?

Here’s how you responded…

^{READ TIME: 4 MINUTES}

I’ll be attending and hosting a round table at the CDO BFSI Exchange in London in November. This is an invitation only event for CDOs in the UK Banking Financial and Insurance sectors. If you’re interested in attending, head here to grab a copy of the event brochure!

Looking forward to seeing you there!

🤔 Where do you see this going?

I think it is fair to say that data career pathways are not as well defined as they are in many other disciplines.

Data folks often arrive at a point in their career where they have very little idea about what’s supposed to come next. Some will be ICs who’ve reached the ceiling for technical roles inside their orgs - with management appearing to be the only option for anyone wanting to progress.

While others who are already on the managerial track will struggle with belonging as they straddle the fence between being a technical delivery manager/support desk operator, and wanting to contribute their skills in a more strategic sense.

It’s as tricky and stressful as it is commonplace.

So what are the options?

Some will want to make a bigger impact where they are and shift gears from operator to strategic influencer. Others are quietly toying with a different thought entirely: What if I built something of my own?

👉🏼 A side hustle.
👉🏼 A data consultancy.
👉🏼 A new business.
👉🏼 Or something totally outside the data world.

Either way, the feeling is the same. They’re standing at a career crossroad wondering what’s next.

Unfortunately, most data careers don’t come with a map. Which means unless you design your next chapter yourself, you’ll likely end up drifting.

In this post, I’ll show you how to take control of that next chapter. Whether you want to become more influential where you are, or finally start building something of your own.

The assistant that scales with you

Great leaders don’t run out of ideas. They run out of hours.

Wing gives you a dedicated assistant who plugs into your workflows – calendar, inbox, research, outreach, and ops – so execution never stalls. Wing can:

• Onboard in days, not months

• Run the day-to-day so you don’t have to

• Adapt as your business grows

With Wing, you buy back time without adding headcount. More focus for strategy, more follow-through on priorities, and a lot fewer “forgot to send” moments.

Work smarter with Wing

🧍‍♀️ Here’s what happens when you just stand still…

1. Your growth stalls.
You’re still doing good work, but it doesn’t feel like it is going anywhere. You’re busy solving operational problems, writing decks, or firefighting delivery issues, but none of it feels strategic or career-defining. You stop learning new things, stop being stretched, and before long, others with less experience start overtaking you simply because they’ve been more intentional about where they’re headed. It’s not that you’ve stopped growing, it’s that you’ve stopped growing in the right direction.

2. Your energy drains.
You’re putting in the hours, but the spark’s gone. The days start to blur into each other, and even the “wins” don’t feel particularly satisfying. You’re caught in a cycle of meetings, stakeholder requests, and urgent-but-unimportant work. It’s exhausting because you’re mistaking being busy for being effective: lots of motion, very little momentum.

3. You lose influence.
Without a clear vision for where you’re going, it’s hard to position your role as strategic. You become reactive, responding to whatever lands in your inbox instead of being one who shapes the agenda. Before long, your peers stop involving you in the big conversations because they don’t see you as a driver of business outcomes. And the longer that continues, the harder it becomes to change their perception.

4. You start fantasising about quitting.
You scroll through LinkedIn, browsing other roles, or start thinking about doing something completely different. It’s not that you dislike your company or your team, it’s that you’ve outgrown the version of yourself who once wanted this job. The frustration builds because you know you’re capable of more, you just can’t quite see what “more” looks like yet.

The good news?
You don’t have to wait for someone else to hand you your next opportunity.
You can design it yourself. Whether that means making a bigger impact where you are or finally building something of your own.

It’s in your hands!

**BTW on the topic of quitting things that no longer serve you, my book club, The Bad Ass Bookshelf is reading Quit but Annie Duke in October. Come join us - we just started!

🎯 Get intentional about what’s next

You don’t need a ten-year plan or some grand career manifesto. You just need clarity about what you actually want, and the courage to start moving towards it.

Here’s how to get started:

1. Audit your joy.
Take a hard look at your week and ask: which parts of my work energise me, and which parts completely drain me? The things that light you up are your compass. Most people skip this reflection and then wonder why they keep chasing roles that make them miserable. You can’t steer your career if you don’t know which direction feels right.

2. Experiment and stretch.
Growth rarely happens inside your comfort zone. Say yes to projects that scare you a little, get you in rooms with new people, or expose you to business areas you’ve never touched before. The goal here is discovery. Every stretch assignment, every awkward meeting, every new responsibility is a data point that helps you refine what you actually want next. You gotta talk it out.

3. Build visibility inside and out.
Inside your company, make sure people see the impact you’re having. That means sharing wins, offering ideas, and showing up to conversations that shape direction rather than just delivery.

Outside your company, start building your personal brand. Post insights on LinkedIn, start a newsletter, comment thoughtfully, share your perspective as much as you can. Visibility compounds, and the more people who know what you’re good at, the more great opportunities find their way to you.

4. Have real conversations.
Talk to mentors, peers, and trusted colleagues about your goals, even if they still feel half-baked. You’ll be surprised how often a casual chat turns into a connection, introduction, or new idea (I’ve seen it a million times). And don’t be afraid to tell your manager what you want next. A good one will support you, even if that means helping you grow beyond their team (I’ve experienced this personally).

5. Reverse-engineer your path.
Once you’ve got a sense of direction, work backwards. Let’s say your goal is to become a Director of Data or launch your own consultancy in the next two years. What needs to be true 12 months from now for that to be possible? What skills, relationships, and results need to be built? Break it down into quarterly experiments or milestones. The smaller and clearer your next steps are, the easier it is to keep you moving.

Careers doesn’t just happen. They’re built one intentional move at a time.

So whether your next step is to elevate your impact inside your organisation or to start creating something entirely your own, stop waiting for clarity to appear. Create it.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last time I asked you: Have you experienced the guilt that goes with being a new manager?

Here’s how you guys responded:

^{READ TIME: 4 MINUTES}

I’ll be attending and hosting a round table at the CDO BFSI Exchange in London in November. This is an invitation only event for CDOs in the UK Banking Financial and Insurance sectors. If you’re interested in attending, head here to grab a copy of the event brochure!

Looking forward to seeing you there!

🤷‍♂️ Feel like you’re not adding value?

One of the biggest shocks for new managers is when the work you used to do (the dashboards, the models, the queries, the experiments) is no longer your job anymore.

Most of that belongs to your team now. And you? You spend your days in meetings, setting direction, managing stakeholders, and fighting for the air cover your team needs to deliver.

It’s important work. It’s leadership. It’s scaling impact.

But it doesn’t feel like work. At least, not in the way you were used to as an IC.

this could be you

Unfortunately, that’s where manager’s guilt creeps in. You feel like your team is doing all the heavy lifting while you’re just “talking about stuff”. It leaves you questioning your value and whether you’re still contributing enough.

👨🏻‍⚖️ When the guilt takes over

Here’s what it does to you and your career:

1. You cling to old tasks
You dive back into IC work to feel useful. But instead of helping, you slow things down and disempower the people you hired to do the job. This is where micro managers come from so beware!

2. You undervalue your contribution
You forget that strategy, influence, and stakeholder management are real work. If you don’t see the value in it, no one else will either and you’ll struggle to communicate the why to your team and your stakeholders.

3. You struggle to tell your story
You can’t point to neat deliverables anymore to demonstrate your contribution, so when it comes to reviews, promotions, or cross-functional meetings, you may feel like you’ve very little to show for your hard work.

4. You plateau as a leader
By trying to prove yourself through outputs instead of outcomes, you end up stuck in the weeds and miss opportunities to show you’re ready for bigger roles and greater challenges.

Left unchecked, manager’s guilt makes you both a worse manager and a worse IC. Which is where you want to be.

❤️‍🩹 How to avoid feeling like a fraud

Escaping manager’s guilt starts with reframing what “doing work” looks like at your level.

Here’s how to do it:

📐 Define your new outputs
Your deliverables aren’t dashboards or models anymore. They’re things like stronger business alignment, clarity/direction for your team, and influence in the boardroom. Keep a solid record of these so you can see the clear impact you’re really creating.

📣 Shout about your wins
If you don’t articulate your value, don’t expect anyone else to. Translate your leadership into business outcomes and repeat them as often as you can.

🧭 Step back without guilt
Your job is to create the conditions for your team to thrive, not to compete with them for outputs. Trusting your people to get the job done is the most productive thing you can do.

🏆 Redefine success
Stop measuring yourself by the pace of your team’s output. Measure yourself by the scale of impact your team achieves because of your leadership.

When you make this shift, the guilt doesn’t vanish overnight, but it stops being a weight and starts being a reminder that you’re levelling up.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

^{READ TIME: 5 MINUTES}

Find your next winning ad creative in seconds with AI

Most AI tools promise you thousands of ads at the click of a button. But do you really need more ads—or just better ones?

Kojo helps you cut through the noise. We analyze your paid social data to uncover the ideas with the highest chance of success. Then, our AI predicts which concepts will perform best, so you don’t waste budget testing what won’t work.

Instead of drowning in endless variations, Kojo sends your best idea straight to a real human creator who makes it engaging, authentic, and ready to win on social. The entire process takes less than 20 seconds, giving you certainty before you spend and better performance without the waste.

Why gamble on guesswork or settle for AI spam when you can launch ads proven to work, made by people, and backed by data?

Book a Demo

_{(checking out ads is appreciated as they help keep this newsletter going - thank you)}

🫨 Data tech ≠ data strategy you guys.

Too many data folks confuse a data strategy for a shopping list of tools and tech…

I get it though… SaaS sales people have done a phenomenal job convincing us that tools maketh the data team (🥩 no beef with sale people, they cool).

Sure, tools and tech are a cornerstone data capabilities in any org. But your data strategy they are not.

“data strategy”

A real data strategy is about making your organisation’s actual overarching strategy deliverable with data. It's the part of the strategy that outlines how the people, processes and technologies within the data function will be managed and aligned to help accomplish these goals. In some circumstances, this can be achieve without investing in new tools.

Unfortunately, that’s not how most data strategies are being built. In today’s edition of Strategies For Effective Data Leadership, we’re gonna find out why that’s bad and how to fix it.

Buckle up.

🥲 Why getting data strategy wrong can cost you…

Here are the pitfalls data leaders fall into when they treat data strategy as a tech play rather than a business strategy play:

1. You lose credibility
Business leaders hear you talk about platforms and pipelines and think, “Nice, but how does this grow revenue, reduce costs or keep us alive next year?” If you can’t answer that, I’m afraid you’ve lost them.

2. You end up fighting for scraps
If your “strategy” is just a list of technical toys, you’ll constantly be begging for budget against functions who are directly aligned to strategic and commercial goals. Spoiler: they’ll win and you’ll be frustrated.

3. You build the wrong things
Without clarity on the overarching organisational strategy, you could be building beautiful dashboards that no one needs, or scaling projects/tools that don’t help move the business needle. It looks like progress because it is tangible, but it’s just wasted effort.

4. You risk becoming irrelevant
When leadership realises your team isn’t aligned with where the company is actually heading, you’ll be sidelined. At best, you’ll become a reporting factory. At worst, you’ll be restructured out of existence.

Curiously, CDOs are among the shortest tenured C-suite roles. This is largely attributed to their failure to demonstrate ROI for their organisations - a symptom of poor data strategy alignment with overarching business goals.

🛣️ The path forward

The uncomfortable truth: your data strategy is not separate to the organisational strategy.

It’s part of it.

That means step one is to get really clear on what your company’s actual strategy is. If you don’t know it or no one has told you, ask. Thought it not being clearly communicated around the business is red flag in itself. 🚩

And if you find out it doesn’t exist, you’ve got a much bigger problem than “what data stack should we buy this year?”

👉🏼 If you need brushing up on corporate strategy concepts, grab a copy of this book for yourself and your team.

From there:

• Map your data strategy to business outcomes. Start every initiative with, “How does this directly support the organisation’s strategic priorities?” If you can’t connect the dots, then bin it and move on.

• Speak the business language. Data people love to talk features. Executives care about business outcomes. Stop pitching for technical capabilities and expansion and start discussing the impacts your data work expects to provide.

• Position yourself as part of the strategic machine. Data isn’t a bolt-on or something that sits aside from the rest of the org just doing it’s thing.. Your job is to show how your function is essential in delivering what the company already says it wants. Because, let’s be real - that’s what they’ve told their shareholders, and that’s what your bosses will be bonused on.

Do this well and your “data strategy” stops being just another PowerPoint schematic that no one looks at. It becomes the evidence-backed, execution-ready part of the company strategy designed and working in unison to make the whole thing possible.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week, I ask you: Have you worked with/for a data phobic-exec?

Here’s how you answered. It’s quite wild, but 80% of you said that you have!! A scary result. Also, I found it interesting that 0% of you outed yourselves as being data-phobic… probably a good thing seeing as you work in data though right?

^{READ TIME: 5 MINUTES}

The back office, built for founders

We’ve worked with over 800 startups—from first-time founders at pre-seed to fast-moving teams raising Series A and beyond—and we’d love to help you navigate whatever’s next.

Here’s how we’re willing to help you:

• Incorporating a new startup? We’ll take care of it—no legal fees, no delays.

• Spending at scale? You’ll earn 3% cash back on every dollar spent with our cards.

• Transferring $250K+? We’ll add $2,000 directly to your account.

Get started today.

😖 Some stakeholders are painful AF

Whoever just popped into your head - yeah, that’s who I’m talking about!

It’s often the execs who’ve built a career on gut feel and “years of experience,” and look at your data as if it’s a personal attack on their brilliance.

These people aren’t (always) bad or stupid. They’re (mostly) human. But when data challenges their sense of value, or worse: their ego, they get defensive. And if you come along and barge in with charts and models without any respect for that ego, you’re very unlikely to win them over.

Unfortunately, in many organisations, data-phobic stakeholders are often the gatekeepers to budget, influence, and whether your initiative actually gets a chance to take off.

So if you get this wrong, you’ll end up stuck in political quicksand.

i believe you

⚠️ Handle with care

💥 1. Turf wars erupt
When senior leaders feel threatened, they don’t roll over and admit defeat. They dig in. Suddenly, it’s not about whether your insight is good or bad, it’s about whose version of “reality” the company should believe. That’s when little empires spring up, with each exec rallying their teams behind “their” way of doing things. Instead of progress, you get guerrilla warfare.

🤥 2. Your credibility suffers
One of the harshest labels you can get is “doesn’t understand the business.” But this is often just political and usually has nothing to do with whether your analysis is correct. If your message bruises egos or contradicts long-held beliefs, the data can be perfect and it will still not matter. If that label sticks, your influence is capped. It does not just slow down one project, it puts your future seat at the table at risk.

🎭 3. Analysis becomes theatre
Rather than focusing on driving outcomes, you end up staging performances. Your decks get watered down, the insights softened, and your time spent anticipating which parts will spark an argument. The work becomes less about solving business problems and more about not upsetting the wrong people. That is a dangerous shift because data integrity turns into an afterthought instead of a lever for action.

🐌 4. Progress stalls
With the turf war rages and your credibility is dented, every decision now requires endless rounds of “alignment.” Meetings multiply, decisions get pushed back, and the opportunity that your analysis uncovered has already been missed. Momentum dies, and so does trust in the value of your team’s work.

But it don’t gotta be like that… you can still turn things around here. You can still “defeat” the villain.

🩸 Achieving a bloodless victory

You want to win. But you want to do it in a way that makes them think they’ve won. Call it subtle manipulation if you like, but sometimes that’s necessary with tricky stakeholders. If you want your data initiatives to survive, you need to approach these people with care and create the conditions where they actually want to back you.

Let’s explore how 👇🏻

🤝 1. Position data as a partner
Your analysis should never feel like it is in competition with their instincts. Instead, frame it as an ally, something that strengthens their experience and helps them make sharper bets. A simple change in tone can shift the dynamic: “This supports your view and highlights where we can double down” lands a lot better than “The data says you’re wrong.”

⚓️ 2. Anchor everything in what matters to them
Execs do not lose sleep over SQL code values or p-tests. They care about revenue growth, risk exposure, customer retention, and shareholder value (interestingly, the same things you should care about). The more you tie your insights to those outcomes, the more relevance you build. If your work is positioned as their tool for hitting their goals, you have a much better shot at being listened to.

🤗 3. Invite their experience in
A great way to disarm defensiveness is to ask how your insight combines with their expertise. This flips the dynamic from confrontation to collaboration. Instead of being the person who “knows better,” you become the person who brings valuable evidence to their table. It signals respect for their background while nudging them toward better decisions. It’s collaborative rather than combative.

🥊 4. Be strategic with your fights
Not every hill is worth dying on. If you pick battles over small details, you will burn goodwill fast. Sometimes it pays to let minor disagreements slide, so that when the big, high-stakes issues come up, you have built enough trust to hold the line. Influence is not about being right all the time, it is about being effective at the right time.

You don’t need to defeat the “villain” in this story. You just need to make sure the business wins in the end.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

⚡️ Previous poll results

Last week I asked you: When was the last time you pitched a new business opportunity (not just an insight) based on your data analysis?

Here’s how you responded: