Why iGaming remains ‘disconnected’ amid ‘crucial’ transition period

The iGaming industry does not have a data shortage. It has a context problem.

Operators collect enormous volumes of information across player activity, payments, bonuses, marketing, fraud, compliance and product performance. Yet in many businesses, those sources remain disconnected. Data sits across different systems, platforms and departments, often making it difficult to build a complete picture of what is actually happening across the business. The result is a familiar contradiction: more dashboards, more reports and more technology, but not always better decisions.

The challenge is not necessarily having access to more information. It is being able to bring the right information together, understand what it means and turn it into something useful. When teams are working from different datasets or looking at performance in isolation, important patterns can easily be missed.

Data and finance teams are often pulled into reactive work. They reconcile invoices, prepare compliance reports, clean inconsistent datasets and answer urgent questions. All of that matters, but it leaves little time for the questions that could actually change performance. Where are players dropping out of the journey? Which acquisition channels are genuinely profitable? Where is the business wasting money? And perhaps most importantly, what is the data telling the business that it has not yet noticed?

This is not unique to gambling. The 2025 FP&A Trends Survey found that data teams spend 46% of their time collecting and validating data, while only 31% is spent generating insights and driving action. In organisations with poor-quality data, the time spent collecting and validating information rises to 59%

An industry in transition

For years, strong year-on-year growth made the questions easier to ignore. If revenue kept rising, inefficient spending could be hidden inside wider expansion. That is becoming harder. Many legacy iGaming markets are in a state of transition. The UK has raised its remote gaming duty, the Netherlands implemented a deposit restriction, Brazil is discussing removing casinos and many other markets are going through some form of change.

Growth is more uneven – and several major operators are showing pressure. FDJ United reported a 4.5% decline in revenue for the first half of 2026, primarily stating the cause was due to troubles in the UK and Netherlands. Flutter reported that US revenue fell by 6% in the second quarter, driven by a 15% reduction in sportsbook revenue. More strikingly, US adjusted EBITDA fell from $400m to $119m. DraftKings faced similar numbers in its Q2 report...

When growth is difficult, metrics change, an operator can report a 40% increase in registrations, a 20% rise in activity within a target segment or a 20% increase in platform logins. Each number can be presented as a success. None of which proves the business is profitable.

Was the increase driven by a major promotion? Did a bonus code appear on a public forum? Was a streamer paid £20,000 ($26,900) to promote the site? Did those players remain active after the incentive ended? Without the full context, the KPI shows only as a vanity metric and not a trend towards success.

Consider a simple example. An operator spends £50 acquiring a player and gives that player another £50 in bonuses. The true acquisition cost is £100. However, these costs sit across 2 or 3 different databases and in some cases the acquisition cost sits on a separate tool, never to be seen by operations.

While the bonus cost sits in the player account management system. If those systems are not connected, the financial report may recognise only the £50 media cost. At £50 of player revenue, the report shows break-even. In reality, the player is still £50 negative. The same player appears profitable in one report and loss-making in another. The business has only 50% visibility of the player’s direct acquisition cost, and its reported break-even point is half the real figure.

The value of separating metrics

Even that may understate the problem. Payment fees, provider charges, gaming duties, affiliate commissions, chargebacks and fraud costs are often stored in separate systems.

This is why revenue, GGR, NGR and player contribution cannot be treated as interchangeable. A business can grow revenue while weakening contribution. It can increase registrations while increasing waste. Lifetime value can create another layer of false confidence. A model may estimate what a player could generate over 18 months, but predicted value is not realised profit. The question is simpler: how long must that player remain active before the full acquisition cost is recovered?

This is also where AI can make the situation worse. AI does not automatically identify that a commercial definition is incomplete. It works with the information, rules and assumptions it is given. If bonus costs are missing, the system may optimise towards players who look valuable but are unprofitable. It will not fix the flaw. It will scale it.

Not just AI – the next stage of data maturity

Before automating acquisition, retention, fraud or player value, operators need to answer basic questions. What is a profitable player? Which costs should be attributed to that player? Where is each cost stored? Which system is correct when two sources disagree?

The next stage of data maturity in iGaming should not begin with another dashboard or AI tool. It should begin with connected costs, clear commercial definitions and a shared understanding of profitability. Once those foundations are in place, data teams can stop preparing numbers and start uncovering the insights that drive sustainable growth.

Only then can automation improve decisions – instead of making bad decisions faster.