• Pricing is impossible without value alignment. Before you even think about numbers, you need to do the work of helping a client understand what something is worth to them. Value discovery isn’t a nice-to-have. It’s the foundation everything else sits on.

• Simple structures win. Yes, you want to capture value. Yes, modular and incremental pricing can help. But complexity kills deals and, over time, it kills relationships. Don’t get so clever about squeezing value that you lose sight of what actually matters: the client succeeding.

• Nothing is free. Your sticker price is not the total cost of ownership. Implementation effort, internal resourcing, political capital, reputational risk. These are all real costs inside an organisation. If you don’t understand them, you’ll lose deals you think should have been easy.

• Find the floor, then find your sweet spot. In the early days you’ll feel the pressure to compete on price. Sometimes you have to, but don’t discount your way into irrelevance. Know what your business model allows, know what the value is really worth, and have enough confidence to hold the line.

We’ve spoken about many topics within the Failing Upward Club so far, and said they were challenging, but pricing has to be up there with one of the toughest to nail in the early days.

You can get your technology right. You can build a great team. You can find genuine appetite for your product and what it solves for.

And then the pricing conversation comes along, and it can undo all of it. Or at least slow everything down in genuinely painful ways.

For us at DataCo, pricing was made harder by the fact that we were operating in a nascent space. When you’re in a category that doesn’t fully exist yet, there’s no market rate to point to. You can’t go look at what the competition charges and build a model from there. You’re hypothesising value in real time with clients who, more often than not, have never done this before either.

That’s where pricing gets really interesting, and really difficult.

The Value Alignment Problem

Before you can talk about price, you have to do something that a lot of founders rush past: you have to get genuine alignment on what your product is actually worth to the specific person you’re talking to.

This sounds obvious. It isn’t.

In the early days, we were selling something that people couldn’t always see the exact value of immediately.

Data collaboration, Strategic partnerships. Transaction data insights.

These weren’t products where a client could point to the line item on their P&L and say, “yep, I see exactly how that maps”, or even “that would help us do this better”. That’s because they often were doing nothing at all in the space.

If we expected a client to understand the value, we had to do the work of building that understanding together.

The value discovery stage is everything. What problem are you actually solving? Is it incremental revenue? Cost savings? A workflow fix? Or is it something more political, giving an internal champion a story they can sell upstairs?

That last one is more important than people realise. A lot of the time, the value you’re creating isn’t directly measurable in the short term. You might be helping someone build a case, or giving a team a reason to stay relevant, or solving a problem that senior leadership has been talking about for two years. That’s real value, but it requires a different kind of conversation to unlock it.

And this is where deals fall apart. Not because the price is wrong, but because the value recognition isn’t there. If your client doesn’t fundamentally believe in what you’re selling, no pricing structure in the world is going to save you.

For a long time, we thought the value was obvious. But it was only when the clients actually bought in and were able to frame the value within their own context that things started to get going.

The value alignment problem gets amplified early on if you’ve built a lot, as there is the temptation to continue to try to sell and justify what you have. It's these early days when you need to stay more fluid in your thinking, stay focused on finding the big problems to be solved, and pull your solution in that direction.

Nothing Is Free

This is the one that trips up a lot of first-time founders, especially when they’re selling into large organisations.

You set a price. It seems fair. If anything, you’ve set it pretty cheap. You think it’s a bargain and are making it easy for them. And then the deal still stalls.

Even worse, you say, “why don’t we start with functionality X at no cost?” because who doesn’t want something free? Yet things still don’t progress.

What’s going on?

Inside a big organisation, your sticker price is just the beginning of the cost conversation.

There’s the implementation effort, the time from their tech team, which is often already stretched. There’s the internal training. There’s the project management burden. The time and effort to push things through compliance and legal. And sitting underneath all of that, there’s the political cost.

Someone inside that organisation has to put their name on this. They have to stand up in a meeting and say, “we’re doing this.” That’s a risk they’re wearing. And in environments where doing nothing is almost always the safe option, that risk is real.

We saw this pattern play out many times. Someone would seem enthusiastic, the numbers were fine, and then it would just go quiet. Not because of the price. Because nobody wanted to be the person who’d championed a new initiative if it didn’t work out.

We also saw this in the data and insights space specifically, in a way that was pretty confronting. When you’re offering a product that can actually measure the effectiveness of past decisions, campaigns, investments, and commercial bets, the person signing your contract sometimes realises they’re also agreeing to have their own track record interrogated. That’s a career risk dressed up as a vendor decision.

Understanding these hidden costs changes how you sell. It shifts the conversation from “how cheap can we make this” to “how do we make it safe for this person to say yes.”

Free proof of concepts are a good example of this in action. We spent time thinking about whether POCs should be free. And the truth is, it rarely matters whether it costs 10 or 20 grand. If someone’s saying no to proceeding, it’s rarely about the POC cost. It’s about whether they’re willing to put their neck on the line. Price is the wrong lever.

The Complexity Trap

Here’s where vendors consistently shoot themselves in the foot.

You’ve got a product. You’ve spent months thinking about how to monetise it. You’ve built a sophisticated pricing framework that captures usage, outputs, value, modules, tiers. The works.

You are high-fiving internally, thinking how well you’ve done in creating a structure for a client to grow in value with your capabilities. The client is sitting there thinking they need to do a specialised course to understand how much they will end up paying...

Complexity kills deals. And then it kills relationships.

We watched this happen in a few adjacent domains recently, particularly the Customer Data Platform (CDP) space. Most vendors moved to consumption-based pricing because it sounded smart. Align cost to usage, capture value when value is generated.

In theory, beautiful. In practice, clients were generating surprise bills in the millions. What should have been a win-win turned into a trust crisis.

The vendors were so focused on not leaving money on the table that they stopped thinking about what the client actually needed: predictability, simplicity, and the freedom to use the product without doing mental arithmetic every time.

The best pricing structures do two things. They capture value incrementally as the client grows, and they don’t make anyone hesitate before using your product. If someone’s sitting there asking, “should I run this query, what’s it going to cost me?” you’ve got a misalignment problem that a better pricing sheet won’t fix.

There’s a version of this in the collaboration space that we cared about specifically. If you’re trying to build an ecosystem where the whole point is that people work together and use the platform more, the last thing you want is a per-use model that disincentivises experimentation.

You want people to get in, get their hands dirty, and find their “aha” moment. Anything that creates friction at that stage is working against you.

Keep it simple. Especially early. There’s a time to get more sophisticated in how you structure commercial arrangements, and that time is when you have enough data to know where the value is actually being generated, and enough trust with the client to have that conversation.

Finding the Sweet Spot

In the early days of a business, pricing feels existential. Because it kind of is.

You need the deal. You need the reference customer. You need to show the market you’re real. And so the pressure to come in cheap is intense.

Sometimes you have to. There’s no shame in that. An early customer who helps you build your case, gives you a reference, and allows you to stay in the market is worth more than the revenue difference between what you charged and what you could have charged. Reframe that discount as a marketing investment, because that’s what it is.

But here’s the thing: you need to know where the floor is. Not in a theoretical way. In a real, modelled, business-case kind of way. What does it cost you to deliver this? What’s your minimum viable margin? What does your business model allow?

For us, our technology-led approach fundamentally shifted what we could offer commercially compared to a range of competitors. When we were entering the insights market, we knew our delivery costs were substantially different to others. That gave us headroom to compete on price where we needed to without destroying our economics. That flexibility wasn’t accidental. It came from having a business model that supported it.

That being said, pushing the price down wasn’t the intention. There needs to be a realisation of what the value being generated is, and making sure you hold to a certain level. We spent a long time thinking through this balance and making sure we were generating enterprise value in the long term.

Don’t discount your way into a relationship you’ll regret.

Pricing is one of the topics that could be spoken about for days, and is genuinely hard in practice. There’s no formula. There’s no benchmark you can just borrow. You have to do the discovery work, understand your own costs and business model, price with confidence, and keep iterating as you learn more.

The businesses that get it right do so because they’ve built the discipline to understand value before they talk about price. Everything else flows from that.

Thanks for reading Failing Upward Club. If you’ve got your own pricing war stories, the wins, the losses, the surprise bills, we’d love to hear them. And if there are topics you want us to dig into, drop us a note.

• Executive sponsorship is necessary, not sufficient: Getting a CEO in your corner gets you started. It doesn’t get you home. You still have to do the work at every layer beneath them.

• Enterprise cycles will always be a threat: Leadership changes reset priorities. An innovation function built on one CEO’s vision is always one departure away from irrelevance.

• Strategy misalignment quietly kills everything: If the innovation function and the core business aren’t rowing toward the same horizon, the innovation function is just guessing.

• Integration is where good ideas go to die: The hardest part of corporate innovation isn’t building the capability. It’s getting a foreign organism past the antibodies of a large enterprise.

• Apply the same empathy inward as outward: Innovation teams obsess over the customer. They rarely extend that same curiosity to the employees of the business they’re trying to help. That’s a costly oversight.

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A week or so ago, I was speaking to an old colleague who mentioned that we might be entering another cycle of corporate innovation in Australia. The third or fourth, depending on how you count them. Labs getting spun up again. Venture arms being reconsidered. Boards asking whether they should be doing more.

It’s a familiar pattern. And having spent time on the inside of two of Australia’s most significant attempts at this, across NAB and then ANZ through 1835i where DataCo was born, Danny and I thought this was worth putting on the record before the next wave crests and people start making the same mistakes again.

This isn’t a cynical take. We both believe corporate innovation can work. But we’ve seen enough of how it fails to know that the gap between the ambition and the execution is wide, and most of what kills these initiatives isn’t dramatic. It’s structural. It’s slow. And it was usually visible from the start to anyone who knew where to look.

Structure it wrong and nothing else matters

The first question any corporate innovation function has to answer isn’t what to build. It’s where to sit.

At NAB, the innovation function was nested inside a business unit, reporting to a mid-senior level manager alongside a half-dozen other responsibilities. That’s too low. It worked for a while because it had direct sponsorship from the then-CEO Andrew Thorburn. But when Thorburn departed around the time the Royal Commission began in 2017, the function lost its air cover practically overnight. Without a high-level sponsor, the writing was on the wall. It didn’t take long.

At ANZ, the setup looked like the opposite. Ron Spector, who led 1835i (ANZi at the time), reported directly to the C-suite and sat on the executive floor. On paper, that’s close to ideal. What I didn’t expect was that having too much executive support can create its own problem: it rubs the broader business the wrong way. If people on the ground feel like something is being mandated from the penthouse without their input, the resistance that follows is real and it will cost you.

The lesson isn’t that you need to find the perfect structural position, because there isn’t one. It’s that structure only gets you so far. Whatever perch you’re sitting on, you still have to go and do the work on the ground floor. You still have to build relationships with the people actually doing the job. Executive backing gives you access; it doesn’t give you trust.

The antibody problem

The most visceral way Danny described the integration challenge stuck with me. An external innovation capability being pushed into a large enterprise is like a foreign organism entering a body: the cells don’t ask questions, they just attack.

What that looks like in practice is a cultural resentment that rarely gets named directly but sits underneath almost every conversation. The person on the home loans team who’s spent three years trying to simplify the core product enough to just deliver it reliably, watching a group of people in jeans and sneakers come in with wild ideas about green home loans and telling him the exec says this needs to happen now. His reaction, which Danny recounted from a real meeting, was basically: fair enough mate, but we haven’t even solved the basics yet. And he was right.

Innovation functions, in my experience, do something well and something poorly in roughly equal measure. They’re genuinely good at obsessing over the customer. Human-centred design, journey mapping, bringing a customer perspective into organisations that had lost it: these were real early wins. But the same empathy that was extended to the customer was rarely extended to the employees of the business. The same curiosity about what the customer actually needed was rarely turned inward to ask what the business actually needed, what the constraints were, and what it would take to make something land.

That gap creates the resentment. And resentment, once it’s in the walls, is very hard to shift.

You need a bridge, not a mandate

When DataCo spun out of 1835i and became a standalone entity, the change in how the bank related to us was felt almost immediately. One day we had blue badges and were working alongside our peers. The next we were an external vendor. Lawyers stopped talking to us. The process changed. The warmth that comes with being part of the family evaporated.

What saved us, to the extent that anything did, was a person. Anthony: a senior figure hired by 1835i who came with significant political capital inside the bank, a track record of delivering large enterprise projects, and the credibility to operate as an impartial conduit between DataCo and the core business. Not a DataCo salesperson. Not a bank employee protecting the institution. Someone who could move between both, understand what each side needed, and get things through the process.

That role, whatever you call it, is not optional. It’s the job. And it tends to be dramatically underestimated when innovation functions are being stood up. You can have a brilliant product, strong executive backing, and a clear strategic rationale, and still watch it stall in the integration layer because nobody with the right relationships and the right internal credibility has been assigned to run it through.

If you’re building one of these functions again: invest in that person early. It will feel like overhead. It isn’t.

Strategy misalignment is a slow death

This one is subtler than the structural and integration problems, but in some ways it’s more damaging because it’s harder to see coming.

Enterprise businesses typically plan in three-year cycles. Innovation functions, almost by definition, operate in the three-to-seven year horizon. When those two things aren’t explicitly reconciled, what you get isn’t necessarily conflict. It’s drift. The innovation team pursues what appears to be a compelling long-term bet. The core business continues executing against its three-year plan. And at some point, someone at the executive level asks how the innovation function is contributing to the strategy, and nobody has a clean answer.

Danny made a point about this that I think gets to the heart of the problem: without clear strategic alignment, the innovation function or CVC has a goalpost that constantly moves and will never be able to demonstrate real success, because success was never properly defined in the first place. Are you investing for financial return? For capability building? To recruit talent from portfolio companies? To see around the next competitive corner? All of those are legitimate objectives. None of them looks like the others on a scorecard.

The person leading the innovation function has to be the one who closes this gap. That means having enough standing, political capital, and frankly, commercial courage to stand next to the CEO and say: here’s where the core business is going in the next three years, and here’s how what we’re building in years three through seven connects to that. That alignment has to be earned, maintained, and resold constantly. And in my experience, it’s one of the things that almost never gets done as well as it needs to be.

Enterprise cycles will always be the enemy

All of the above can be navigated with enough skill, time, and the right people in the right seats. But there’s one force that no amount of good execution can fully protect against: leadership change.

You get a new CEO, and they want to put their own mark on things. They have their own strategy, their own priorities, their own sense of what innovation means for this business. The thing their predecessor was building, with all its accumulated momentum and institutional knowledge, becomes a natural target. It’s visible. It’s associated with someone else. And if times get tough, it’s one of the first things that gets examined.

This isn’t anyone’s fault exactly. It’s the nature of how leadership cycles in large organisations work. But it does mean that every corporate innovation function is, at some level, built on sand. The question isn’t whether this will happen. It’s whether the function has become embedded enough in the business to survive it when it does.

DataCo, by the end, had genuinely found its footing. We had product-market fit. Inbound was accelerating. The business was pulling the product out of us rather than us pushing it in. We were close enough to independence that we thought we might clear the event horizon and survive in our own orbit. Danny’s analogy was apt: we were on the edge of a black hole, one limb stuck, and that was enough. The whole thing got pulled in.

That’s the hardest part of this to accept. It wasn’t the product. It wasn’t the team. It was timing, and the structural reality of how enterprise organisations protect their core when the pressure is on.

If you’re about to do this again

The cycle is coming back around. There will be new labs, new venture arms, new enthusiasm about what corporate innovation can do. Some of it will be legitimate and some of it will be organisations who’ve forgotten why the last version didn’t work.

If you’re inside one of these functions, or considering it, the things worth holding onto are simple enough to say and hard enough to execute: get executive sponsorship, but don’t mistake it for safety. Build relationships at every layer of the organisation, not just the top. Define what success looks like before you start, and make sure the core business has signed off on the same definition. Hire the bridge person. And go in with realistic expectations about the timeline, because large enterprise organisations do not move at startup speed and pretending otherwise is a fast road to disillusionment.

The potential in this model is real. We’ve both seen it work. But as Danny put it: you hear the word potential more than almost any other word in this space. Repeatedly, the potential is there. Repeatedly, it doesn’t quite arrive. Knowing why is the only thing that gives you a fighting chance of getting there.

• Corporate isn’t safe, it’s just a different kind of risk: Large organisations create an illusion of security. The risk doesn’t go away; it just moves somewhere you’re not looking.

• The training wheels problem: Building within a corporate structure meant we never had to confront the urgency that real founding demands. That caught up with us.

• Perspective arrives in brutal ways: Losing a team member mid-journey changed how we thought about what actually matters when you’re building something.

• PMF is a feeling, not a metric: After years of telling ourselves we were nearly there, we finally felt product-market fit in our bones. That felt different from everything before it.

• Passionately disconnected: Give it everything. But don’t let the outcome define you. Market timing and luck are real forces; you don’t control all of it.

• Fulfilment is a myth: You get there, and the goalposts shift. The journey is the thing. Always has been.

We recorded this episode two days after announcing KICO Labs to the world.

That timing wasn’t entirely planned, but it felt right. The Failing Upward Club started as an exercise in looking backward: unpacking what went wrong with DataCo and making sure we didn’t let those lessons fade. But this conversation ended up being something slightly different. It was the first time we really sat with the full arc. Where we started. Who we were as founders going into DataCo. How that changed during the journey. And what we’re carrying into this new chapter.

It also happened to be the week we stopped being ex-founders of a failed business and became founders of a new one. So the timing, messy as it was, gave the whole conversation a kind of before-and-after quality that felt worth capturing.

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What drew us in

Neither Danny nor I arrived at DataCo through a conventional founder’s path. We didn’t drop out of university with a product idea and raise a seed round. We came up through organisations, built careers, and reached a point where the corporate environment started to feel like it was working against the things we actually cared about.

For me, that came into focus at Nike. I spent around nine years there and was genuinely lucky: a great boss, a strong team, and an environment that showed me what it looks like when people who care about good work are given enough room to do it. But it also taught me something about large organisations more broadly. The qualities that tend to help you navigate them well: caution, calculation, not saying the thing that probably should be said. These aren’t the same qualities that make the work feel meaningful. Too much passion, too much ownership, too much of a willingness to take risk: those aren’t assets in most big corporate environments. They’re liabilities.

I wanted to be in a place where I could just get after it.

Danny came at it from a different angle. He’d spent time in consulting, which he describes as a fantastic learning environment but one that kept delivering him to the same frustrating destination: a PowerPoint deck. Months of thinking, genuine conviction about what a client should do, and then the work would be handed over and you’d never see what happened next. Sometimes nothing happened at all. There’s a particular kind of itch that creates, and it’s hard to scratch from the inside.

The other thing Danny brought up was sport. Both of us came from reasonably high-level sporting backgrounds, and he talked about how that shapes something in you before you even know what it’s shaping. Working hard toward ambitious goals. Losing. Getting up. Not needing to win every time to stay motivated. The startup world has a version of that loop: the dopamine hit when something works, the resilience required when it doesn’t. Danny recognised it instinctively.

Risk isn’t where you think it is

One of the more useful reframes that came out of this conversation was around risk itself. Because the conventional story about joining a startup, that you’re leaving stability for instability, security for uncertainty, turns out to be largely wrong.

I’d watched people at Nike who’d given thirty-plus years to the company get walked out in a single redundancy round. One of those rounds came every three or four years, give or take. I went to NAB’s innovation function next and watched that get quietly folded once the CEO who’d sponsored it was no longer around. No sponsor, no funding, no function. Just gone.

Once you see that pattern clearly, the risk calculus flips. The risk of staying isn’t zero. It’s just delayed and harder to see. You’re not avoiding risk by staying inside a large organisation; you’re just pushing it somewhere else in your career and hoping it doesn’t arrive before you’re ready.

Danny put it well: “just because you think this is a low risk path now, it doesn’t mean that’s the low risk path in the long term.”

He also used a rock climbing analogy that’s stuck with me. A career is like climbing a wall: you move one limb at a time, testing each hold before you commit weight to it. The risk isn’t in the movement; it’s in launching off the wall without knowing where you’re going to land. The point isn’t to stop moving. The point is to keep moving, deliberately, and in a direction you’ve actually chosen.

The training wheels problem

Our situation at DataCo was unusual compared to most startups, and it shaped our founder mindset in ways we’re still unpacking.

We didn’t start in a garage. We started inside a corporate venture structure, backed by a major bank, which gave us access to capital and data infrastructure that a typical early-stage startup would never have. It also meant we never fully experienced the raw version of founding: the moment where you stop pedaling and there’s nothing underneath you.

Danny described it as riding a bike with the training wheels still on. A kid who knows intellectually that they can balance doesn’t actually learn balance with the wheels attached. The security short-circuits the learning. And for us, knowing the bank was behind us created a subtle but real drag on the urgency we needed to develop. We had to build that urgency deliberately, almost artificially, because the structure of our situation wasn’t generating it naturally.

That’s one of the things that’s changed going into KICO. The training wheels are off. There’s no safety net that we’re trying to ignore. And whatever discomfort that creates, it’s also clarifying in a way that the DataCo structure never quite allowed.

When it really doesn’t matter

Midway through the DataCo journey, one of our team members passed away. Unexpectedly. He was on a call with Danny one Wednesday afternoon and didn’t show up to the standup the next morning. He’d passed away overnight.

We were a team of ten people at that time.

Danny talked about what that did to his perspective, and it’s something I think about a lot, too. When you lose someone in the middle of building a business, all the catastrophising you do about the business starts to look very different. What happens if we don’t hit this target? What happens if we lose this client? What happens if the company folds? Those questions don’t disappear, but they sit differently. You can see more clearly that the answers to most of them are: it’s fine, actually. You’ll be okay. The people around you will be okay.

“It actually just doesn’t matter,” Danny said. “And we always kind of catastrophise some of these things.”

That’s not a licence to stop caring. It’s almost the opposite. It’s what lets you care fully without being consumed. The business matters, and you’re going to work hard for it, but it doesn’t get to be the thing your whole sense of self hangs on. Because if it is, and it ends, you’re in real trouble. And things end. That’s just part of what happens.

The feeling of product-market fit

One of the things I’ve reflected on most from the DataCo journey is the final six months.

We spent a long time building. A long time structuring the partnership with the bank. A long time getting the product into the market. And then, finally, we started to feel something shift. Inbound started coming in. The market started pulling the product out of the business rather than us pushing it in. There were days where we genuinely couldn’t keep pace with the interest.

That’s product-market fit. And I know what it feels like now in a way that I didn’t before, because I lived it.

The hard truth is that before we got there, we told ourselves we were close more than once. We believed our own story about being nearly there. And looking back, we were further away than we thought in those moments. The feeling we eventually got was distinctly different from the story we’d been telling ourselves. I’m glad we finally felt it, and I’m glad I can carry that understanding into KICO, because finding it faster is the whole game now.

Passionately disconnected

I came across a concept recently in a podcast I can’t fully reconstruct, something I need to go back and find, but the phrase that stuck was “passionately disconnected.”

The idea being: you give it everything. You commit fully. You work hard and you care about what you’re building. And in equal measure, you hold a genuine understanding that you don’t control all the outcomes. Market timing. Luck. External forces that have nothing to do with how hard you worked or how good the product was. These things are real, and they shape results in ways that your effort cannot fully override.

Holding both of those things at the same time is the job. Work like it depends entirely on you. Know that it doesn’t.

That framing helped me make sense of something that had been sitting awkwardly for a while: why it’s possible to feel good about the DataCo journey even though it ended the way it did. Because we committed. And the commitment is the thing you actually control.

Who we are now

Going into KICO, Danny talked about the thing that’s most different: confidence. Not arrogance, not false certainty, just a quiet knowledge that we’ve done this before. If it goes badly, we know we can rebuild. We’ve already rebuilt once. And the rebuilding, as it turns out, is some of the most alive you ever feel.

Danny talked about winning the national volleyball championship with the ACT team in 2015 and then never playing again. He said he only really appreciated the full weight of those years later, looking back, when the whole arc was visible. The injuries, the training, the near-misses, the final. The gold medal was real, but it was always the whole journey that mattered.

“You’ll get to a point in your life where you look back and you’ll be like, man, that was a time to be alive.”

I think about that. And then I think about the fact that we’re right in the middle of something like that now, in real time, and we get to know it.

Which brings me to the thing that’s probably the most honest thing I took from this conversation. Fulfillment is a myth. You reach something and immediately the goalposts move, and that’s not failure, that’s just how it works. The goal was never really the destination. The goal was to show up every day and take a big swing, know that you gave it everything, and then get up and do it again the next day.

We’re going to keep doing that with KICO. And somewhere in the middle of it, we’ll probably look back at this week, the week we announced to the world that we were back, and think: that was one of the good ones.

• Optimise for learning over comfort: Every time Gaz has defaulted to safety, he’s regretted it. Every time he’s leaned into the uncertain bet, something has opened up.

• Think in angel investment timelines: The best career bets compound over seven to ten years. Zoom out to that horizon, and the calculus on risk changes entirely.

• Build the full picture: Anchoring too hard to either the founder or investor perspective leaves you with a partial read of the game. The most useful people have stood in both places.

• Know when you’re the commodity: Pouring your personal brand into a company you don’t own equity in isn’t loyalty: it’s free marketing with a redundancy notice at the end.

• Acknowledge failure, don’t celebrate it: Gaz built one of Australia’s most recognisable failure-storytelling events and walked away because the culture around it started to miss the point entirely.

• People are always the thing: Even after 150 speakers and seven years, the moment that hit hardest was a founder getting emotional about his team.

Gaz Williams has sat in the room while some of Australia’s most prominent founders fell apart on stage.

Not metaphorically. Literally on stage, in front of 400 people, finally releasing something they’d been holding too long. He’s watched people walk out lighter. He’s watched others leave the industry entirely. And for seven years, across Melbourne and Sydney, he and his co-curator Josh Liscombe were the ones who built the room, curated the speakers, and held the space where that happened.

We’ve spent most of this blog looking inward: unpacking what went right and wrong across five years of building DataCo. But from the beginning, we always knew we wanted to bring in outside voices, too. People who’ve lived in this space, thought hard about it, and have something honest to say. Gaz is the first guest we’ve had on the series, and it wasn’t a difficult decision. When you’re two ex-founders of a company that just wound down, trying to make sense of it publicly, and you know someone who spent seven years professionally holding space for exactly that kind of reckoning, you start there.

Gary Williams, Gaz to everyone who knows him, has spent his career at the intersection of tech, storytelling, and the kind of human messiness that most professional environments prefer to smooth over. He’s worked inside tech schools, university innovation hubs, and revenue-based lending funds. He’s met thousands of founders. And he has a sharper, more calibrated view of what failure actually looks like, and what it too often gets turned into, than almost anyone we know.

We sat down on a Friday morning and let the conversation run. It went to some honest places.

Read the Tea Leaves, Then Back Yourself

Across his career, Gaz has made a habit of spotting where things are moving before the crowd catches up, and then making the bet, even when it looks strange from the outside.

He got his first break in tech by doing something most people wouldn’t: showing up, repeatedly, to the General Assembly campus in Melbourne until they couldn’t ignore him anymore. He knew on paper he wasn’t the obvious hire. He was coming from a retail head office background, not a startup. So he made himself impossible to overlook. “I just kept rocking up, just kept rocking up for multiple weeks and just kept saying, I want the job.”

They gave it to him. And from there, he made a series of moves that tracked where momentum was building: deep tech commercialisation out of universities, revenue-based debt funding when it was still largely unknown in Australia, and eventually co-founding Fuck Up Nights when it had a swear word in the title and absolutely no guarantee anyone would show up.

The mental model underneath all of it is one worth borrowing. Gaz thinks about career bets the way an angel investor thinks about a portfolio: on a seven-to-ten year horizon. Not quarter to quarter, not even year to year. When you zoom out to that timeframe, the calculus on risk changes entirely. Leaving money on the table now to pursue something that feels like the right direction makes more sense when you’re thinking about where it compounds to in a decade. The short-term cost of the uncomfortable bet looks very different against a longer horizon.

Connected to that is a lesson about perspective. One of Gaz’s three opening reflections was the risk of anchoring too hard to one side of the ecosystem. Spend your whole career in founder mode, and you develop blind spots about how capital actually thinks. Spend it entirely on the investor side, and you lose touch with what building actually costs. Gaz has moved deliberately between the two: tech schools, innovation hubs, a funding platform, and events. Partly out of curiosity, but partly because he recognised that the full picture only becomes visible when you’ve stood in both places.

The flip side of that lesson, the moments he’s carried as regrets, were the ones where he defaulted to safety. Roles that felt stable but not alive. Decisions made by consensus when his instinct was pointing somewhere more interesting.

Both of us have wrestled with versions of this. There’s a gravitational pull toward the known, especially once you’ve built something and watched it not work out. The temptation is to de-risk. To choose the thing that looks sensible. But in our experience, and Gaz’s, the times that pull wins tend to be the ones you end up looking back on thinking: I knew. I just didn’t trust it.

The Seven-Year Experiment That Ended on Purpose

The Fuck Up Nights conversation was the one we lingered on longest, because what Gaz built over those seven years is genuinely remarkable; the reasons he walked away are more interesting than the reasons he started.

The events were completely free. No sponsorship, no tickets, no consulting revenue attached. Gaz and his co-curator Josh Liscombe ran it as a kind of punk-rock creative exercise: how long could they keep going without any rules, without anyone telling them what to do, just bringing a different kind of conversation into a room and filling it? The answer turned out to be seven years, 150-plus speakers, and audiences of 400 people, in venues ranging from basement bars to the Sydney Overseas Passenger Terminal.

But as the events grew, and as people grew more comfortable with the format, the stories got heavier. People started using the stage to release things they’d been carrying for years. Some walked away lighter. Others broke down. A few, Gaz believes, never quite recovered: they left the industry, lost the appetite for building, and needed years to process what the experience had unlocked.

The weight of that responsibility was significant: holding the space properly, making sure support pathways were visible, briefing 150 different people across all walks of life and trusting them to go wherever they needed to go. And so Gaz and Josh made a deliberate call to end it cleanly, on their own terms, rather than let it drift or dilute.

But there was a second reason, and this is the one that stuck with us. The broader cultural conversation around failure had started to shift. Other events, other platforms, had picked up the format and turned it into something Gaz never intended: a cheerleading exercise. A celebration of failure as an almost aspirational state. And he found himself tagged as a figurehead for something he actively disagreed with.

“Acknowledging failure in a storytelling context is a positive thing for me.”

Not celebrating it. Not positioning it as proof of character or a badge of honour. Just acknowledging it: sitting with it honestly, sharing the shape of it, letting others recognise it when it shows up in their own lives.

That distinction is almost exactly why we started this blog. We’re not here to celebrate the fact that DataCo wound down. We’re here because there are things we learned through that experience that are worth putting on record: for ourselves, and maybe for others who are somewhere in the middle of their own version of it.

Know When You’re the Commodity

Woven through the conversation was something sharper: a lesson Gaz has learned from watching hundreds of people move through the startup ecosystem and, at times, from his own experience.

He’d watched early employees at tech companies pour their identity into evangelising for a business they didn’t own. Pumping LinkedIn. Becoming the public face of the brand. Building a reputation almost entirely anchored to someone else’s company. And then, when things got hard, when the headcount needed trimming or the cap table shifted, finding themselves out the door, having spent years constructing something they couldn’t take with them.

“I would implore the younger groups coming through that are jazzed on the companies to also be self-aware enough to understand when you’re the commodity in this conversation.”

I’d clocked a version of this playing out in real time with a major fintech whose entire global new-starter cohort appeared to have been quietly encouraged to flood LinkedIn with posts about their exciting new employer. Hundreds of people, all hitting publish over the course of a month or two. A strategy dressed up as culture.

The distinction Gaz drew was important, though. It’s a completely different thing when it’s your company, or when you hold meaningful equity. Danny made the point clearly: if there’s genuine skin in the game, building that public association makes total sense. The risk calculus changes. The problem is when the association runs deep, and the equity runs shallow: when you’re doing their marketing for free and calling it identity.

It’s a trap that’s easy to fall into, especially early in a career when you want to signal momentum and belonging. The antidote isn’t cynicism. It’s just clarity about where you actually stand.

The Moment That Actually Hit Hardest

About two-thirds of the way through, Gaz flipped the dynamic and turned the questions back on us. He wanted to know how we were sitting with all of it. What our actual relationship to the DataCo failure looked like now.

Danny talked about mentality over lesson plans: the idea that sitting through every Fuck Up Night ever held wouldn’t necessarily prepare you for the moment it happens to you. What actually equips you isn’t exposure to other people’s stories. It’s a different relationship with uncertainty altogether. The capacity to absorb what comes without it destroying you, and to figure out what to do next without needing the plan to have held.

For me, the Failing Upward Club has always been less about the failure itself and more about not letting it just pass over. Diving into it: not to dwell, but to extract. To turn something that happened to us into something we’re choosing to learn from.

But the moment from the whole conversation that I keep coming back to is simpler than any of that.

Near the end, Gaz mentioned one of the last events he ever ran in Melbourne. Dom Pym, co-founder of Up Bank, got up and spoke. Pym is a well-regarded leader by any measure: a genuinely successful founder with a business that worked. And yet what he got emotional about, in front of that room, was his team. The people. The regrets that came with the decisions made about them along the way.

Gaz said it recalibrated him. Even after everything he’d heard across seven years and 150 speakers, a successful founder getting raw about people, not product, not funding, not the exit, was the thing that landed.

“I still had this really profound moment taking that in,” he said. “And so even after that moment, I adjusted my thinking again. Just to be a little bit more appreciative of the people I’m surrounded by.”

After five years of building DataCo, and everything that came with winding it down, that one lands differently than it might have before. Some lessons you have to earn the right to really hear.

Gaz is the founder of Group Group, helping challenger brands scale through strategic content and curated experience. In 2026, he’s building a new education model and a short-form podcast shining a light on under-the-radar funding groups operating in cannabis, psychedelics, sex tech, and other industries that mainstream startup media tends to overlook. We’ll be following along.

• The Gilded Cage: Having a bank as a backer gives you credibility, but a fixed cap table makes it nearly impossible to reward your best people or flex your burn when you need to most.

• Alignment > Worth: Letting someone go isn’t a judgment on their talent; it’s a realisation that the role or the moment no longer fits.

• The Utility Player: In the 0-to-1 phase, a cross-functional generalist who can wear multiple hats is worth three specialists.

• Always Be Hiring: Your best hires won’t come from job ads. They’ll come from relationships you’ve been building for years, clients, partners, and people you meet at industry events.

• Hiring for Values: You can teach a skill, but you can’t teach someone to care about the mission when the pressure is on.

DataCo’s journey was defined by people as much as by technology. Working within a corporate venture, we had the credibility and capital to attract talented individuals, but also real constraints on how we could reward them. Here’s what we learned.

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Letting Go: It’s About Alignment, Not Worth

No one we hired was a bad person. Not one. Every person who came through DataCo was someone we knew through our network or genuinely impressed us through an application. But people decisions were still the hardest calls we made.

The first lesson was the hardest: a role mismatch isn’t a reflection of someone’s worth. Early on, we had an exceptional engineer, technically brilliant, a great human, well-liked by the team. But he was at a stage in his career where he was ready to lead and manage a team. We didn’t have a team for him to manage. We had a small group of senior people going to do exceptional work and build the company. The role he needed didn’t exist yet.

We reached a mutual understanding and parted ways. He went on to do great things. But the lesson stuck: people who stay in roles that don’t have space for them to grow can start to resent it. The kindest thing, for them and for the business, is honesty.

We saw this play out in different ways across the journey. At one stage, we had too many senior people and not enough executors. In go-to-market particularly, there’s a fine line between having people who can direct and having people who can do. Too many of the former and you end up over-indexed on strategy with no one running hard on execution.

We also hired a go-to-market person slightly too early. We were still in founder-led sales mode, still crafting the pitch, still figuring out product-market fit. We weren’t ready to hand the keys to a sales engine and say “go.” They came into a role without full autonomy, couldn’t just pick up the phone and run hard, and again, it wasn’t a personality issue. It was timing and alignment.

The meta-lesson: get crystal clear on the role before you hire for it. What does it look like today? What does it look like in six months? Don’t leave assumptions unspoken. We had senior hires where both sides had different mental models of what success looked like, and those assumptions were never actually aligned.

The Gilded Cage

Having a big four bank as a strategic backer was, in many ways, a gift. Capital, credibility, distribution. But the rigidity that came with that structure was a genuine handicap, particularly towards the end.

We couldn’t easily create incentive structures for our best people. We couldn’t flex our burn when we needed to. The inability to reward exceptional contributors with meaningful equity wasn’t just a compensation problem; it was a cultural one. People need to feel like they’re building something they have a stake in.

It’s a lesson for anyone taking institutional backing: understand exactly what constraints come with the capital, and design around them from day one.

The Utility Player: Hire for Ambiguity

Outside of our core technical and engineering roles, we generally hired generalists. People who could wear multiple hats, who were comfortable operating in the grey space between startup chaos and corporate structure. It was the right call, and we’d do it again.

One of our best hires came from the client side, someone with a legal background who had transitioned into a commercial and partnerships role in the data space. Think about what that means in practice: commercial acumen, legal literacy, data domain knowledge, and a client-side perspective all in one person. We didn’t have to pay external legal fees for every contract review. We had someone who could sit in a client meeting in the morning and work through a commercial agreement in the afternoon.

The lesson: cross-functional unicorns aren’t just efficient, they’re outsized bets. Like a good investment, you’re not just getting the stable return. You’re getting the possibility of something unique that you couldn’t have generated any other way.

We made similar bets on other generalists who came to us through industry events and personal introductions. In one case, we met someone at an industry event who had just moved to the country, with an impressive background, a star in a role that didn’t yet have a clear shape in our business. We figured it out. In the time they were with us before the company wound down, they were on track to take over the entire product vision.

Another came through a genuine application process, one of the few hires not built on a prior relationship. Technically strong, analytically capable, but also someone you could put in front of a client. That combination is rare and worth every bit of the risk.

Always Be Hiring (Even When You’re Not)

The common thread across our best hires? Relationships built long before we needed them.

One of our strongest team members was a former client. We’d invested in that relationship as a relationship, not just a commercial transaction. When they moved on from their role, we spotted it, picked up the phone, and caught up. Not long after, they were on the team.

Others came from industry events. We met, clicked, and thought: this person can create value for our business. We found a way to make it work.

The lesson is simple but easy to neglect when you’re heads-down building: always be hiring. Keep investing in your network, not because you need something, but because you never know where a relationship leads. Clients can become team members. Partners can become advisors. People you meet at a South by Southwest-style event can become your best hires.

Values You Can’t Teach

You can teach skills. Curious people will upskill on their own as their role evolves. What you can’t teach is how someone shows up when things get hard, when the all-nighters are required, when the pressure is on, when the company is in a tough spot.

We chose to build a culture that was human first. Flexible, empowering, relationship-driven. Not the 24-hour grind culture, but one where people felt they were building something together, not just doing a job. That choice was deliberate, and it has to be consistent. You can’t flip between “work from wherever” and “why aren’t you in the office?” You set the tone early, and you hold it.

That relational investment paid its biggest dividend at the hardest moment. When DataCo wound down, the first conversation wasn’t about clients or creditors. It was about the team. How do we make sure everyone is okay? The founding team immediately leaned on their networks to find roles for everyone. For team members on visas facing potential complications, that urgency was even more acute. The team came first.

Looking back, that’s something worth being proud of.

The Forward View

The playbook for the next venture:

Hire for adaptability and values first, skills second. Be ruthlessly clear about what the role actually is before you hire for it, and have the explicit conversations about what success looks like over time. Favour generalists in the early phase and bring in specialists when the motion is proven, and you know what you need.

Build the relationships before you need them. Your best hire is probably someone you already know.

Be honest about what you can offer. If you can’t compete on cash, compete on mission, culture, and the chance to build something real. But design your incentive structures so that the people helping you build feel like they genuinely own a piece of what you’re creating.

A startup is a team sport. The people you choose, and how you treat them, determine whether you win or lose.

• Getting security and privacy right takes far longer than anyone expects, and it never really ends. If you want to work with enterprise clients, you need to invest early in compliance and governance, even when it feels premature.
  

• Despite all the frameworks and controls, security and compliance are still very much a people game: Over-invest in building key relationships with compliance stakeholders.
  

• Base compliance and security services are becoming commoditised and will be simply expected as part of business, similar to accounting services.
  

• Rapidly building trust with any client is critical to any business. In a world where just searching for an answer in an LLM makes things look easy, if you don’t have someone who deeply understands this, you will be left out. Trust is built by knowing your stuff, following through, and showing up when the hard questions are asked.

Security, governance and compliance were never the sexy part of the DataCo story.

But they were at the heart of our proposition.

Building a platform with a bank as your first customer means there is no choice in the matter. We needed bank-grade security and compliance from Day 1.

Getting Your House in Order Takes Time

We know getting to compliance and trust as fast as possible wasn’t going to be simple, and was something we certainly couldn’t do alone at that point.

So we did the most logical thing we could think of: We turned to partners early.

At the start of the DataCo build, we leaned on an existing partner who already had all of the compliance foundations in place in their own platform. The security architecture, the monitoring, the compliance certificates.

We negotiated a BOT Model (Build-Operate-Transfer), which would allow us to piggyback off their foundations in the earlier days to get to market as fast as possible, while ensuring we were still meeting the high security standards of the bank.

Once the capability was established and proven, we quickly transitioned to our own independent setup, rapidly progressing our own stand-alone certifications, such as ISO27001.

Again, we turned to partners, both in terms of engineering flex capacity to help to migrate to our own compliant infrastructure, and importantly, to a compliance management platform that gave us the accelerated pathway to getting the non-sexy stuff up and running (if you think compliance is non-sexy in general, the policy side is definitely the ugliest of the whole lot).

The goal was to get to a compliant baseline as fast as possible, far quicker than any organisation our size normally would, because this wasn’t a nice-to-have for us, it was a must-have.

Even with the accelerants of partner capabilities and a platform that unlocked a whole policy and compliance tracking framework out of the box, it still took time. Even longer than we thought.

Even with templated policy docs that looked like they were ready to go, it took months to comb through them and update them to be fit for the business. This may sound like an overkill job, but it’s critically important when auditors are reviewing what’s in a policy and seeing if you are actually doing it..

We brought in a consultant to do our internal audit early, which turned out to be a huge help to refine what was left to get to audit readiness (shout-out to Sandeep Kumar at Astero).

We ticked off our first ISO27001 audit and rapidly went to SOC 2 Type 1. It felt like we were in a good state, but even with our solid starting foundations, this was a 9-month journey to independent compliance.

As we continued to grow the business and roll through additional years of audits, it became obvious that we were never ‘done’ in this domain. There were always things to add and refine, and the act of forcing ourselves into audits actually pushed us to just embed this thinking in our everyday work.

People can see this as a leech on your time when you are early in a business, but the reality is that if you are working with the big end of town, it’s not really a choice, and showing you are on the ball can become a huge asset for the business.

Security and Compliance Are Still a People Game

Despite all the frameworks, controls, and certifications, security and compliance are still driven by people.

We spent a hell of a lot of time engaging security, privacy, compliance and risk stakeholders over the years.

We went the extra mile to hold education sessions and catch up with them directly, and what we found wasn’t people who want to be the gatekeepers always saying no.

It was people who wanted to enable the business safely. Who wanted to understand what you’re trying to achieve. Who wanted confidence that you’ve thought things through.

When you take the time to build relationships with compliance and security stakeholders, everything changes. Conversations become collaborative instead of adversarial. Problems get solved faster. Trust builds.

Some of our strongest internal allies came from these teams, not despite their role, but because we respected it.

These roles will always exist within large organisations, and they are only becoming more cautious in a rapidly changing AI-driven world.

There is no point trying to pretend they won’t show up, and your solution will just roll in the back door.

Engagement with compliance stakeholders needs to be proactive if you want to achieve speed and scale.

Accelerators and Compliance-as-a-service

As mentioned earlier, we leveraged a range of partners and services to accelerate our compliance journey.

We have no affiliation with Drata, but god damn their platform saved us a hell of a lot of time.

For those who haven’t used these types of platforms in the past, they essentially provide templated policies and control monitoring aligned with specific frameworks. Are you going after ISO27001? Here are all the specific requirements in the standard and all the controls you will need to meet to achieve compliance.

Of course, they don’t give you everything, and there is a lot of work you need to do on top, but they dramatically accelerate getting there.

As compliance with security standards becomes simply expected, I believe this space will move in the same direction as accounting has. Just as maintaining financial reports has come to be expected, maintaining your security and compliance program will be as well.

And services will evolve accordingly. Platforms like Drata or Vanta are the compliance versions of Xero or MYOB. Auditors with always-on services are popping up (shout out to the AssuranceLab, now Sensiba team), and others will fill the gap of rolling support as bookkeepers and accountants do today.

If you are starting a business, highly recommend you lean on these services. If you have experience in this domain, maybe it’s your time to start a business in this area..

Trust Is Built in the Details

While partners, platforms and accelerators exist, it’s not a completely outsourced job.

One of the most important lessons we learned is that trust is built fastest when you genuinely know what you’re talking about.

We spent a lot of time going deep into the technical and legal details, even when we had partners and experts in place.

We did this not to sound impressive, but so we could sit in a room with security architects, privacy teams, and legal counsel and actually work through the problem.

When someone asked a hard question, we didn’t deflect. We engaged.

That credibility compounds.

In a world where it’s easy to generate confident-sounding answers with an LLM, it will be tempting to just rely on that for answers, but when you are in a meeting or face-to-face with someone, that depth of understanding stands out quickly.

Trust comes from being honest and from demonstrating you are doing what is required to keep someone and their business safe. Find someone in your team to take this on.

We’d do it all again..

Looking back, there are a few things we would do again without hesitation.

We would over-invest in relationships with compliance and security stakeholders. The return on that investment was enormous.

We would treat compliance services as foundational infrastructure, not a checkbox. Over time, baseline compliance and security capabilities will become as expected as accounting services. Not differentiating, but essential.

And we would continue to prioritise trust above almost everything else.

Because no matter how good your technology is, no matter how compelling your vision sounds, if people don’t trust you, nothing moves.

• Flexibility is often a trap: Infinite options feel like “freedom” to a product manager but look like “work” to a user. Constraints and guided workflows usually outperform open-ended flexibility.

• Sell outcomes, not capabilities: We struggled when we sold “tech that can do anything.” We succeeded when we sold “specific answers to specific problems” using anchor data.

• The “Chat Interface” fallacy: In professional settings, inconsistent AI chats don’t replace repeatable workflows. Users often don’t know the right question to ask; the product needs to guide them.

• The real moat: It isn’t the AI or the code. It is years of domain expertise crystallised into software workflows that make decisions on the user’s behalf.

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At DataCo, we built models that could find patterns in encrypted data without ever “seeing” the raw information. The demos were impressive. But when we put these tools in front of actual users, they were overwhelmed.

What wowed in a room on a first call wasn’t what wowed clients when they got their hands on the technology. We learned that flexibility can become complexity, and that the most advanced feature is useless if it intimidates your customer.

The Agency Problem

We thought giving users many different ways to experience the product would create value for different personas. Instead, it translated to complexity in clients’ minds.

Even when the system worked perfectly, users felt disconnected. The technology created an “agency gap”: users felt either stupid for not understanding the magic, or replaceable because the machine seemed to be doing all the thinking.

Any feature that intimidates an end user is a feature that doesn’t get adopted, or worse, it impacts the adoption of your entire platform.

From Capability to Outcome

Initially, we defined our product as “units of capability”, the analytical processes the software could perform. We sold secure collaboration technology: matching, analytics, and modelling. The problem? We were selling to two organisations simultaneously. It was incredibly hard.

Then we evolved. We embedded “anchor data”, external banking transaction benchmarks, into the solution. Suddenly, instead of selling collaborative technology, we were selling specific productised insights.

Now we were selling to a single client, solving a specific problem, with a clear outcome. Your product isn’t what your technology can do. It’s the specific problem you’re solving, and for whom.

Guided Workflows Over Infinite Options

We rebuilt our platform around guided workflows rather than infinite possibilities. We gave users coached paths through specific tasks instead of overwhelming them with every option.

Users stopped asking “how do I use this?” and started asking “can you add a workflow for X?” They wanted more constraints, not fewer.

The Hybrid Reality

Our product wasn’t pure software. It was technology, data, and service. Clients valued our team’s domain knowledge and ability to interpret results as much as the tool itself.

This created a scalability problem, but the solution wasn’t to remove the human element; it was to embed it into the product. We tracked every question our team answered repeatedly and turned those into in-app guidance, workflow templates, and smart defaults. The product handled the repeatable 80% while our team focused on the novel 20%.

What This Means for AI Products

The current wave assumes “just chat with it” solves everything. It doesn’t.

Go look at your ChatGPT or Claude conversations; it’s a mess. Even with folders, you’re scrolling, trying to find that good section. It’s not a repeatable path.

We dropped an AI sidekick called Collin into our platform with natural language querying. Users stared at the blank input box. They didn’t know what to ask. If you knew the most important question for your business, you wouldn’t need the AI; that insight is at the heart of everything.

Professional work needs consistency. If you’re producing reports month on month, the AI can’t run queries differently and change the numbers. Having something that’s purely an agent with a chat interface is naive.

We believe the future is curated experiences with repeatable pathways, plus AI that accelerates specific tasks within those paths:

• A crafted experience: The repeatable pathway with your expertise built-in

• An agent: To do extra work on top as needed

• Optional depth: For those who want it

We overestimate people’s ability to ask the right questions. A good product guides users toward the insight they need, rather than just waiting for a prompt.

The Real Moat

In an era where tools like Lovable can generate slick interfaces from prompts in minutes, the competitive advantage isn’t the AI. It’s years of expertise crystallised into workflow design.

It’s understanding actual consumption, not idealised use cases. It’s being honest about what you’re selling: pure tech, service, or hybrid, and designing the business model around that truth.

The hardest part wasn’t the machine learning. It was figuring out that clients didn’t need more flexibility. They needed us to make better decisions on their behalf, then encode those into the product.

This requires real people working with real users to figure out what to build. Anyone just throwing prompts at an AI and assuming a pretty interface solves everything will face challenges.

Success isn’t measured by how smart your AI is. It’s whether the user can get their answer, generate their report, and go home; feeling competent, not confused.

The future is building products where expertise becomes infrastructure, constraint creates capability, and technology does its job so well it becomes invisible.

That’s the whole point.

• Access to an anchor data set can completely change a business. Software on its own is interesting. Software plus data is useful. Software plus data plus a clear use case is where value actually shows up.

• Most organisations dramatically overestimate how well they use data. Many are still trying to sort out their internal systems and have not even begun to think about external data.

• In a world increasingly shaped by AI, access to high-value proprietary data is becoming the real differentiator. Business who are building valuable data that others can benefit from will continue to grow in value. Data ecosystems are not a nice-to-have. They’re the future.

• Financial transaction data holds immense value, and we’ve barely scratched the surface of what can be extracted from it.

For the first couple of years of the DataCo journey, we were essentially a technology company.

We believed there was value in data being used between organisations, and we pictured a world where value would start to flow if we provided the right technology for collaboration.

But in the world of collaboration, the saying “it takes two to tango” is very apt.

You need to convince multiple organisations to buy into the idea and get involved. That is a time-consuming process, and it delays value realisation, regardless of how easy you make the technology.

Everything changed the moment we realised the starting point didn’t have to be technology alone.

The Power of an Anchor Data Set

We knew financial transaction data was compelling and had the potential to unlock significant value for the bank and its partners.

Transaction data provides a wide view of people’s behaviour: who they spend with, how much they spend, how frequently, and across large areas of their lives. It reveals how markets operate, who customers really are, how companies perform relative to competitors, and how customers move between brands and categories.

So the question became obvious: why not make this type of data available, in a safe and compliant way, for more organisations to benefit from?

The first concern, of course, was data privacy. While this information is powerful, it is also highly sensitive and must only be used in ways that are lawful and respectful of the individuals behind it.

Fortunately, we already had a platform designed for exactly this purpose: unlocking aggregate value while protecting individual privacy by design.

We deepened our partnership with the bank and shifted from being a pure technology provider to a platform embedded with insights.

Collaboration was no longer theoretical. Suddenly, there were insights available from day one.

No partner onboarding required.

No waiting period before value emerged.

The platform moved from “interesting technology” to something that generated immediate outcomes. It turned an empty shell into something alive.

In effect, we solved the two-sided collaboration cold-start problem by always having one party’s data ready to go.

The data didn’t replace the technology. It unlocked it, gave it purpose, and strengthened the case for collaboration itself.

Data Is Only Valuable When It’s Translated

One of the biggest misconceptions we encountered was the belief that data, by itself, creates value.

It doesn’t.

Raw data is inert. Even sophisticated data is useless if it can’t be turned into something meaningful for a specific decision, workflow, or outcome.

For years, we’ve seen data marketplaces launch with the assumption that organisations will simply consume vast amounts of data and magically extract value from it. In reality, most people feel overwhelmed and have no idea where to start.

The real power of our proposition came from applying a powerful data asset to a clearly defined outcome.

For example, connecting exposed marketing campaigns directly back to financial transactions to measure uplift versus control groups. In simple terms: campaign measurement based on real behaviour, not just clicks.

When clients could see a direct link between data and acquisition, retention, campaign performance, or partnership decisions, the conversation shifted.

The software made the data accessible.

The use cases made it valuable.

Not Everyone is Data-Ready

If you believed all the public claims about data capability from major organisations in Australia, you’d think we were global leaders in advanced analytics and intelligence.

Anyone who has worked inside these organisations knows how far that is from reality.

Internally, many businesses are still dealing with fragmented systems, inconsistent definitions, unclear ownership, and limited data literacy outside specialist teams. Externally, this often translates into ambition without execution.

There’s nothing wrong with this. It’s normal.

But it does mean many organisations aren’t ready to plug into broader data ecosystems, no matter how much they talk about it. Before collaborating externally, most still need to sort out their own backyard.

For a startup, this shouldn’t deter you from building in this space, but it does need to shape your expectations. Understanding the landscape is critical to managing your own ambition.

Interestingly, some organisations found value in experimenting with advanced capabilities in parallel, in a controlled way, while still fixing their foundations. It helped them stay connected to where the market was heading.

This dynamic only becomes more pronounced with AI.

Data as the Real Moat

Speaking of AI, we are living through the fastest technological shift most of us have ever experienced.

Models are improving at a breathtaking pace. What looks like a breakthrough product one day becomes a standard feature the next. This has left many startups and enterprises struggling to decide where to focus.

In the data domain, however, one truth appears increasingly clear: access to unique, high-quality data is a durable point of differentiation.

Models will become cheaper.

Tools will become more accessible.

The advantage will sit with those who can feed intelligence systems with data others cannot access.

Demand for proprietary, high-quality data will accelerate the development of data ecosystems faster than almost anything else. Networks where complementary data sets combine to create outcomes no single organisation could achieve alone.

This will require new architectures, better governance, and thoughtful commercialisation models that protect all parties involved.

It won’t be the size of the data set that will drive the value going forward, it will be the quality. Data sets that actually represent real-world behaviours and actions will be the ones that demand a premium, with financial transaction data sitting squarely at the top of this list.

We haven’t even begun to scratch the surface in terms of the value that can be extracted from financial transactions, particularly when it comes to banks and financial institutions.

Organisations that generate this data should be thinking about how they can safely generate value from this data, anchoring new data ecosystems based on the rich information they can harness.

General organisations should be thinking about how they best work with their banking partners, or wider players in the market, to access this type of data and use it to generate differentiated outcomes for their business.

As the AI wave continues to build, those investing seriously in data will be rewarded.

Just never forget the responsibility that comes with using it, especially when it relates to individuals and their privacy.

• Most clients do not want bleeding-edge tech. They want something they can understand, trust, and deploy without blowing up their internal processes.

• It takes a hell of a lot of effort and complexity to build something simple. But it’s worth it.

• We built too much. Way too much. The fewer clients you have, the more you tend to build. Have the confidence to challenge whether it’s actually needed.

• A great user experience is everything. And with AI coming fast, things are about to get spicy.

If there was one domain where our ambition overshot reality more than anywhere else, it was technology.

When you’re building something with big aspirations and an even greater sense of possibility, it’s tempting to chase the cutting edge. You convince yourself that the most advanced techniques will differentiate you, earn credibility, and create a moat.

Sometimes that’s true.

But only if people can actually understand, trust, and adopt what you’ve built.

Most of the world is not ready for bleeding-edge technology. Not because it isn’t valuable, but because new equals unknown, and unknown equals risk.

Innovation vs Traditional: The Temptation of the Cutting Edge

In the early days, we explored everything when thinking about how to build a practical data-collaboration solution: emerging Privacy Enhancing Technologies barely out of academic papers. Secure multi-party computation. Homomorphic encryption. Differential privacy.

The approaches sounded mysterious, sophisticated and impressive.

Mathematically, they were beautiful.

Security-wise, they were unbeatable.

Conceptually, they solved problems traditional methods couldn’t touch.

But here was the kicker: None of that matters if people inside an enterprise can’t verify it.

We spent far too long trying to get teams to understand how these methods made their data safer and unlocked new types of collaboration.

The modern gatekeepers to Enterprise technology are the cyber security and risk teams.

Risk teams don’t approve what they can’t assess. Security architects don’t insert something into their stack if they can’t explain it to their peers.

And if we’re being honest, some of these bleeding-edge approaches introduce entirely new risks. SMPC is mathematically elegant, but in practice, a fully distributed system requires new ports, new traffic profiles, and new data flows that smell risky to a bank.

So we changed tack.

We learned that “more secure” doesn’t automatically mean “more acceptable”, and “innovative” doesn’t mean “useful right now.”

We shifted to a hybrid architecture: combining traditional infosec concepts people already trusted with the best principles of PETs, particularly the rule that raw personally identifiable information never leaves your building.

Sometimes the most technically elegant solution is the least deployable one.

And simplicity wins.

Simple wins, but simple is hard.

Every founder says simplicity is important. Every founder believes their product is simple. Every founder is probably wrong.

We spent a lot of time in the early days working with some designers from 1835i to try to create a simple user experience for what was a reasonably complex platform.

They did a hell of a job, but it was still a lot to grok.

Part of the challenge was that we were trying to do so much while also making it easy for users. And because we didn’t yet know what the “killer application” was, we didn’t know what the core experience needed to be. Until you know that, you can’t truly simplify your user flows.

By the end, we were onto something great.

We had new designs that genuinely simplified the platform down to its core experience. But even with four years of learning and iteration, we were still hitting the same lesson: Building something simple is very complex.

It’s the classic swan floating across the pond analogy. Graceful on the outside, absolute chaos of kicking underneath.

Especially when you have the breadth of technical capability and features already baked into the platform.

We Built Too Much

This one hurts to admit, but it’s true.

It’s the classic first-time start-up issue: We built too many features.

We solved problems no one had felt yet. We built capabilities that were technically impressive but practically unused.

In some ways, it was almost inevitable.

We weren’t a garage startup scraping by on a shoestring, building the bare minimum. Our first client and eventual owner was one of the biggest banks in the country.

Big banks don’t do bare-bones MVPs. They need a lot to get comfortable.

Add to that the countless RFPs and POCs demanding every feature under the sun, often competing against global incumbents with massive platforms, and it created enormous pressure to keep building.

There were definitely moments where we should have pushed back harder.

Or done the classic enterprise-software thing: say “yes” while not actually building it yet.

Ironically, in our final six months, many of the long-idle features finally saw uptake. It felt like the platform was ready to hum.

But the truth is simple: We built a hell of a platform. We just built more than the market was ready for.

Who Are You Actually Building For?

There’s a huge difference between building for engineers and building for the marketers.

Engineers want flexibility, customisation, pipelines, APIs, and powerful backend capabilities.

Marketers want a product they can navigate without feeling stupid.

We knew we couldn’t succeed by building only for engineers, even though data analysts and engineers were our ideal users. Large organisations don’t always have those resources available, or available quickly.

Our onboarding approach was intentionally simple. But even then, protecting data before it hit the platform meant some effort was always required on the client side.

The most powerful feature we built was actually a demonstration feature.

Going through this onboarding process involved running a container locally on a laptop or in an environment. Doing this process in a demonstration wasn’t ideal - switching from the application UI to a Jupyter Notebook, triggering some commands in code. Snore.

I have no idea when, but we decided to get the guys to build a browser-based version so it would be easier to demonstrate. Maybe some small clients without big technical teams would use it.

Then suddenly, real clients were using it.

One uploaded 15 million records through it, securely, without a single data engineer involved.

Simplicity wins in ways you don’t always expect.

As I said before, we built a hell of a platform, and our technology was certainly the furthest thing from why our business collapsed.

But there are always lessons to learn. If we could build again, would we build the same?

Probably not. Not everything.

We now live in a very different technical environment with the onset of AI.

The next generation of collaboration platforms will probably have a very different feel to them, and for the better..

• Don’t Confuse Curiosity with Commitment: Recognise that most GTM partners are structured for distribution (selling proven products), not development (shaping new ideas).

• The Big Brand Trap: Big brand names might love your proposition, but they are often too structurally rigid to invest energy in an early-stage venture.

• Master the “Fast Pause”: The most valuable skill is knowing how to quickly step away from wrong-fit partners whose timing doesn’t align with yours.

• Build the Network for Tomorrow: High-trust relationships pay dividends long after a specific business ends. People do business with people, not logos.

Partnerships were at the heart of our strategy. We began with a single use case: two partners collaborating. To make that collaboration meaningful, we partnered with a bank to incorporate financial transaction data, and subsequently extended the inclusion to sociodemographic providers and other foundational data sources. While these data partnerships provided clear value, the real challenge began when we pursued Go-To-Market (GTM) partners.

Curiosity vs. Commitment

Early on, because we had an innovative technology and deep industry ties, we had no trouble landing an initial meeting. We quickly realised, however, that many partners (they are just humans afterall) are just intellectually curious; they love the concept, they see the vision, and they’ll take the meeting.

But curiosity is a nice to have, and it is easy to mistake a great conversation for a real commitment to help you shape your idea. Most GTM partners are structured to sell a proven product (distribution), not to invest time and resources in shaping a new proposition with you (development). If you are still in product discovery mode, an ‘interested’ partner can quickly become a massive drain on your time without ever moving the needle on revenue.

Choosing Your Partner Archetype

To avoid the curiosity trap, you need to know which game you are playing at each stage of your business. We categorised our ecosystem into three buckets:

1. Discovery/Development Partners: Often SIs (System Integrators) or Consultants like the team at Silver Bullet, Deloitte and Sayers. They are boots on the ground and help refine the value prop because they deal with the client side implementation reality and value delivery.

2. Marketing & Awareness Partners: These aren’t about direct sales. Think co-hosted events, like the sessions we did with Ellipsis, AKQA and Vibrance. They provide partner-led awareness and market validation.

3. Distribution Partners: The holy grail, but the hardest to land. These are pure sales channels. Until you have a repeatable playbook, these partners will likely stay in the curiosity bucket.

The Big Brand Trap

It is incredibly easy to be attracted to the big brand names. We invested significant energy working through the bureaucracy of large multinational businesses because we felt the outcome could be worth the effort.

In reality, partners with big brand names might love your proposition, but they are often too structurally rigid to invest their own time in selling an emerging business. They simply aren’t the right fit for the 0-to-1 phase. Instead, seek out Challenger Partners who are in their own phase of business building. When there is a strong, urgent, mutual benefit (where your solution helps them differentiate their own core offering) that is when a partner will actively lean in and commit.

Mastering the “Fast Pause”

Because partners require as much education and training as a client, they can act as a lead weight on a small team. The most valuable skill we learned was the “Fast Pause.” This is the ability to quickly step away from wrong-fit partners or those whose timing doesn’t align. We had several high-quality relationships where we mutually agreed to catch up periodically. There was no stress, just a high-trust connection maintained over the years. When we finally achieved visible market traction, those were the partners who circled back because the timing finally aligned with their own client demands.

Build the Network for Tomorrow

Ultimately, people do business with people. DataCo may have ended, but the partnership network we built didn’t belong to the company; it belonged to us as individuals.

Whether it was global software giants, media agencies, or boutique consultancies, the trust we cultivated is what carries forward into 2026 and beyond. When you’re going through a partner ecosystem, remember: it isn’t a transactional thing. It’s about your wider career network. Investing in a human relationship today, even if the deal never closes, is what will hopefully pay dividends in your next venture.

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Just give me the good stuff (a.k.a. TL;DR)

• The Anchor Trap: Being born inside a bank creates a permission culture. If you wait for a perfect, enterprise-ready product to launch, you’ve already started the clock too late.

• Negotiating with Handcuffs: Corporate backing gives you credibility, but it takes away your flexibility. If you can’t pivot pricing or contract terms without a board meeting, you’re in trouble.

• Glacial Timelines: We were prepared for high compliance bars; we weren’t prepared for the 12-month procurement cycles that eat startups alive.

• Hiring for 0-to-1: Don’t hire Hunters (phone-bashers) when you’re still co-creating the vision. Hire Builders who can navigate the technical exposed wires of an early build.

• Authenticity > Perfection: In a world of AI filler, shipping unpolished, human perspectives is the only way to build real trust.

• The Lagging Metric: You can hit 600% growth and still fail. Sales velocity can solve a lot of problems, but it can’t always fix a broken capital structure.

The Curse of the Corporate Anchor

Our first sale was internal. For years, we didn’t have to sell anything to the market; we had to “sell” our progress to the bank that funded us. This created a permission culture. Because our anchor client was a Tier-1 bank, we became conditioned to believe that only a perfect, enterprise-ready product could ever go out the door. We weren’t building for the market; we were building to make a bank feel safe.

In corporate venture, you spend years seeking approval when you should be seeking traction. Reid Hoffman famously said, “If you’re not embarrassed by the first version of your product, you’ve launched it too late.” By that metric, we launched incredibly late. We were too focused on the reality of the build to sell the “dream” early enough.

Negotiating with One Hand Tied

When we finally stepped outside to commercialise, we had a mature compliance posture that opened doors; but we lacked the flexibility to close them.

Because of our ownership structure, every pivot in pricing or contract terms had to go back through the corporate sponsor. We were often sitting across from clients, knowing exactly what they needed, but unable to move fast enough. Contracting with a large organisation is an endurance test through procurement and risk. We were prepared for high bars, but we weren’t prepared for glacial timelines. Our burn rate was decoupled from our revenue; we relied on the bank’s support. When that support waned, the clock didn’t just start ticking; it started screaming.

The Strategy Bet: Breadth vs. Niche

Textbook startup advice says: Find a niche and smash it. We ignored that. We bet on breadth. We wanted to prove our tech worked across Media, Retail, Insurance, Travel and Loalty simultaneously to build an “ecosystem” play.

It was a slow. In enterprise sales, you can talk to someone for a year with zero movement, only for the deal to click into place in four weeks because they finally hit a Tuesday afternoon problem they couldn’t ignore. The bet eventually paid off. In our final nine months, we saw 600% growth. The strategy was vindicated. The metrics were great. But growth is a lagging metric, and we were racing against a capital clock we didn’t fully control.

Hiring Builders, Not Just Hunters

When we realised we couldn’t do all the selling ourselves, we hit a hiring friction point. We learned first hand about the “Hunter/Farmer” dichotomy in the 0-to-1 phase. Hunters, the sales folks who want a finished product and a proven playbook, can’t sell when the playbook hasn’t been written yet.

In the early days, you are co-creating the product with the client. You need Builders. Our best hire was an ex-founder. He didn’t need a polished deck; he had the founder energy to navigate technical gaps on the fly. If you aren’t a sales-led founding team, don’t hire a scaler too early. Hire a builder who can help you find the playbook first.

Just Ship It: The Content Play

Just as we learned we launched the product too late, we realised we were being too precious with our marketing, too.

We had to learn the muscle memory of being visible before we felt ready. One of our team, Gaz Williams, always pushed us to just do the event or ship the post. Don’t wait for the perfect. Real traction comes from genuine human conversations, like our podcast, not from polished, soul-less marketing funnels. You have to be willing to be unpolished to be trusted.

The Heartbreak of Success

The hardest part of the DataCo story isn’t the failure; it’s the timing.

Seeing the strategy finally land with 600% growth only to have to tell our clients the journey was over was heart-wrenching. Those clients took personal risks inside their organisations to stand behind us. We’ve realised that while sales velocity can buy you independence, it can’t always save you from the foundational reality; the ownership and capital structure that sits behind the scenes.

What happens when the growth is there, but the “committed capital” disappears? We’ll unpack that spicy topic of ownership and incentives in a future post.

1. Your business will die without short-term value articulation. Have the long-term vision, but sell the short-term gain.

2. People will not always tell you the value they need. If you want to go white-space or category-creating, you may need to help them see it.

3. Articulating value simply, in a way that’s differentiated from competitors and alternatives, is incredibly f*cking hard. You won’t get it from an afternoon workshop with consultants. You have to test, refine, and keep refining until the results prove it.

Every startup textbook tries to convince you that defining a value proposition is simple: talk to people, ask what their problems are, and build something that solves them.

In reality, it is nowhere near that simple. Especially when the thing you’re building sits in a new or nascent category, or, in our case, didn’t have a category at all.

The truth is that most clients don’t know what’s possible, especially when the product you’re building doesn’t fit neatly into any category they’re familiar with.

We didn’t realise it at the time, but the moment we chose to build something that didn’t look like an existing product or category, we signed up for years of value-articulation pain.

On top of that, when people got it, they felt that what we were doing was really interesting, but for a long time, many couldn’t immediately see why it mattered to them right now.

Closing the gap between “interesting” and “urgent” became one of the biggest challenges of the entire journey.

Ask vs Tell, and Why “What Do You Want?” Doesn’t Work

Our beginning was a little different from most other start-ups. We weren’t asking a client what they wanted to do with a solution, we were the client, trying to articulate what we wanted to do to potential vendors.

But as we transitioned from being inside a client to building a solution for other clients, everything changed.

It became obvious that everyone wasn’t thinking the same as we were. When we went out to talk to different organisations and ask them what they needed from their partnerships, they didn’t really have productive answers.

Heck even others inside the bank, who become our new stakeholders, didn’t have many ideas or requirements.

The concepts we originally had in mind for the bank weren’t things they had thought about before.

If the thing you’re building sits outside someone’s mental model, they simply cannot tell you what they need. They anchor to the tools they’ve used before. They describe incremental improvements to broken processes.

They try to fit your idea into familiar shapes.

This is the reality of category creation: you cannot ask people to define a future they have never imagined.

You have to show them.

We had to paint the picture of what collaboration could look like, and do it with conviction long before others had fully bought it. It felt uncomfortable. But when you build something ahead of the questions, discomfort is part of the job.

A lot of people criticise the “build it and they will come” approach, mostly for good reasons, but there are opportunities for those willing to take that risk. The caveat is simple: your vision has to be right.

Do you want to build a category?

Even with a clear view in our minds of what the future of collaboration could look like, we were confronted with the decision on if we wanted to take on the cost of category creation or not.

In the early days, there wasn’t much of a choice, there was no common term for the general category.

A couple of years in and this started to change, people started mentioning the concept of ‘data clean rooms’ in the marketing industry.

We resisted the “data clean room” label for a long time, because it wasn’t accurate (we didn’t agree with the concept you could just ‘clean’ data), and didn’t represent what we could offer holistically.

But the market needs a label, even if you don’t like any of the available ones.

Being able to enter a conversation with some baseline knowledge from a client on what the solution does saves a lot of time, and by the end we were winning direct ‘data clean room’ RFPs from big global competitors in the space, even though we considered our proposition to be much more than just a clean room.

Even so, this was only in the last year of operations, and it was definitely not the case that everyone in the market was looking for a clean room.

For many, that concept was very much a nice-to-have, and that was one of the biggest challenges in the early years of our business.

The Non-Core Problem: No One Buys the Future

At the start, we were building a solution that solved a long-term strategic opportunity: enabling multi-party collaboration that could unlock new growth, new revenue lines, and completely new ways of working across industries.

Most clients were solving a Tuesday-afternoon problem.

While we had complete conviction in the power of corporate partnerships and the value they unlock for both parties, they were a nice-to-have for most organisations.

At the very least, those who championed partnerships externally were really only doing the basics internally when it came to creating value; relying on the ‘brand halo’ effect of a partnership as opposed to more strategic forms of value creation.

For the first couple of years of our existence, we weren’t able to nail the “what is the pain you are removing right now?” question with our solution. Partnerships were just down way to low on the priority list.

When we spoke about the type of value they unlocked and how they could be positioned to generate immediate strategic advantage, a lot of people nodded. They agreed the future would work this way.

But when they went back to their desk, or up to management for support, there were just way too many things keeping people awake at night.

It’s only when you break through this and find your now problems that sales begin to mount.

Finding a crown jewel.

We had a couple of ‘aha’ moments when it came to landing the value that really resonated with clients.

The first one was getting to the point where the solution was not just about technology, but embedded data as well. Not just any data, financial transaction data. We’ll touch on the importance and framing of data in another post, but the point is that everyone saw the value having access to financial transaction data would bring.

It also simplified the path to value for clients, as it removed the need to work through the complexities of partnerships, and move into simple consumption mode.

The data also amplified the value and potential of the rest of the platform, the downstream value of collaboration. The whole thing turned from a nice to have, to a complete strategic solution that would unlock immediate value, while leaving the door for ongoing value to be generated.

We also had more traction focusing on the clients where partnerships were the most important for their core business or proposition. Media. Loyalty programs.

In the end, it looked like everyone had bought into the value proposition that we had spent years crafting. Well, nearly everyone…

This is a space to reflect on the tough lessons from failure. Working out how to improve and not repeat the same mistakes again.

Of course, the reason we’ve started this is one of our biggest failures ever. DataCo.

It’s probably good to start with the context.

Building Backwards in Corporate Venture Capital

What happens when a startup is born inside a bank?

DataCo was not a scrappy garage company. It began as a project to help a bank and some of its strategic partners collaborate on data while remaining compliant. Our first use cases were narrow and practical: two partners wanted to combine their data to create joint marketing audiences or understand who their shared customers were.

We didn’t start thinking we’d build a business. But after reviewing the solutions available, we saw an opportunity to make something that would really create value. That was the moment DataCo truly began.

By the time we formally stood up the company as a separate entity, we were more than just an idea. The innovation lab was still in its infancy, and there was no established path for creating a new venture. We partnered using a Build-Operate-Transfer model so we could begin building immediately.

This approach helped us move fast, but it also created a very complex ownership structure. If only we knew at the time how pivotal that decision would become in the final outcome of the business.

The early priorities being pushed by the primary shareholder were not about growth or traction. They were about creating internal value for the bank.

But we knew we could not survive without external customers, so we split our time and our energy. It felt like we were running two races at once. One focused on delivering for the bank, and the other on trying to land external clients.

That unusual origin shaped everything.

Working within a corporate venture arm gave us instant credibility with clients and access to resources that most start-ups dream of. It also tethered us to the requirements of the bank. We gained trust and a built-in customer, but we also had to conform to enterprise-level governance and regulatory processes from day one, along with the hundreds of internal meetings that came with that.

A few years into the company, we looked nothing like a typical startup.

But we were still facing the same product-market fit challenges as one.

Data holds the key.

After some early struggles to get cold-start collaboration off the ground with two organisations, we finally found a solution that aligned the goals of everyone involved.

The breakthrough came when we realised a core data asset that accelerated insights and supercharged collaboration. Particularly when that asset was financial transaction data. The moment people saw the power and context this data provided, the lights switched on.

The bank had struggled for years to commercialise insights from its data. Now it had a ready-made collaborative technology channel that would allow it to leapfrog competitors. And for us, it solved the focus problem. The combined solution was valuable to the bank and valuable for the broader business.

The idea was strong, but progress was slow. Anyone who has worked in a major bank knows nothing moves quickly inside.

Despite existing competitors already active in the space, a twelve-month process was required to test and refine the concept, placing increasing pressure on our runway.

We knew the opportunity was worth being patient for, and once we were live with the refined proposition, growth finally began, albeit modestly. Enterprise sales cycles will test even the most established businesses...

We had conviction in the concept, and our confidence was rising every day. We kept pushing, ramping up our go-to-market efforts, feeling the market starting to take notice.

Before we knew it, the dominoes started to fall. Growth truly started to materialise, and we felt like we were at an inflection point.

Little did we know what inflection point that was.

While revenue was rapidly growing, we still needed additional financial support to reach breakeven.

This wasn’t necessarily a surprise to the team; there had been plans in place for this.

But the rules of the game were about to change.

RIP DataCo.

It wasn’t a single decision or change that killed DataCo. And it certainly didn’t happen overnight.

It was the result of a perfect storm of events that slowly gathered momentum before spiralling out of control.

Many of those events were outside our control and almost impossible to predict. But with hindsight, you can always see the decisions that were within your control, and how they might have been made differently.

We’re not people who carry many regrets or spend much time asking “what if”. But we do believe deeply in reflection and learning from difficult moments.

Years in elite sport, both as players and coaches, taught us the importance of constant improvement. That mindset is essential if you want to perform at the highest level.

This blog series is our attempt to unpack our story and the lessons we learned along the way. It will explore where the textbooks get it right, and where the practical realities of building a business diverge from theory.

Danny’s structured brain naturally wants to break things down into logical components, so that’s exactly what we’ll do. From defining a value proposition to navigating enterprise sales cycles. From balancing cutting-edge technology with simplicity and risk, to aligning ownership and incentives for long-term success.

We invite you to follow along, share your own experiences, and perhaps avoid some of the mistakes we made.

Or choose your own path and make your own mistakes.

As long as we’re learning and improving, the path itself doesn’t really matter.

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