The Frustration with Traditional BI in iGaming

Imagine you’re a product manager at an iGaming studio, starting your day by checking the Power BI dashboards. You notice a sharp drop in revenue for a popular game, but the dashboard doesn’t explain why it happened. Meanwhile, a high-roller had an unusual winning streak last night, is it just luck or a sign of fraud? With a traditional BI setup, getting answers to these questions is slow and cumbersome. Non-technical decision makers often feel frustrated that BI tools show what happened (charts and figures) but not why, leaving them to rely on gut feeling or wait days for analysts to investigate. In fact, less than 20% of employees on average even use BI tools, largely due to steep learning curves and limited access. And among those who do, only 1 in 9 data requests get answered within the same day, over 60% of requests take more than four business days to turn into insights. In the fast-paced iGaming industry, such delays and low adoption can mean missed opportunities, unchecked fraud, or reactive (rather than proactive) decisions.

This case study explores how a Generative BI platform called Milo addresses these pain points. We’ll examine two critical use cases for iGaming companies, automated risk management and product performance analysis, and illustrate why Milo outperforms Microsoft Power BI in each. The perspective here is that of a non-technical business stakeholder (e.g., an operations manager or an executive at a game studio) who needs actionable insights quickly and easily. By the end, you’ll see how companies like BetGames (a leading game studio) scaled their operations and kept every risk in check by switching from Power BI to Milo.

Power BI’s Limitations in iGaming Analytics

Microsoft Power BI is a popular business intelligence tool, and it certainly has strengths. It offers powerful data visualizations and can integrate multiple data sources, features that many iGaming companies leverage for things like revenue reporting and player segmentation. However, these strengths come with significant drawbacks in practice for iGaming teams such as:  

• Steep Learning Curve & Complex Setup: Power BI’s interface and setup are often complex, especially for teams without dedicated BI experts. Its user experience is largely tailored to Excel-savvy power users, which poses a hurdle for others. In an industry where many decision-makers are business-oriented (not data engineers), this complexity limits widespread adoption. Even industry analyses note that Power BI’s rich capabilities “come with drawbacks”, namely a steep learning curve and intricate setup that can be obstacles for teams with limited BI experience.  

• Static Dashboards (What vs. Why): Traditional dashboards in Power BI are mostly static snapshots. They excel at showing what is happening (e.g., a chart of daily bets or Gross Gaming Revenue). But uncovering the why behind a trend requires manual exploration, slicing data by region, player segment, game title, etc., often by writing new queries or asking an analyst to build additional reports. Power BI does have tools like the Key Influencers visual, but using them effectively still demands analytics expertise. For a non-technical stakeholder, it’s not straightforward to dig into root causes without constant analyst support. This often results in decision-makers resorting to intuition; indeed, a majority of executives admit they rely on gut feeling rather than data for many decisions.  

• Struggling with large data quantities: Large amounts of data (i.e. billions of rows from betting transactions) are a critical factor in iGaming. Power BI struggles here like many traditional BI tools and requires data engineers or BI developers to prepare and aggregate data properly before it can be consumed, often leading to significant delays.

• Limited support for unstructured data: Building rich context from unstructured and qualitative data is a key limitation of Microsoft’s product. Hence, drawing conclusions almost always requires human assessment.

• Dependence on Data Teams: Because of the above factors, business stakeholders remain dependent on data analysts/teams to get deeper answers. If a manager sees a KPI plummet on a dashboard, they likely have to file a data request to understand the cause. As noted earlier, most such requests aren’t fulfilled for days, unless you are the CEO. This slow, back-and-forth process is frustrating in a dynamic gaming environment. Power BI on its own doesn’t bridge that gap, it delivers visuals, but not an ongoing conversational analysis.

In summary, Power BI is a solid foundation for visualizing iGaming data, but it often underperforms when it comes to providing proactive, explanatory, and easy-to-access insights for non-technical users. The good news is that emerging solutions like Milo are specifically designed to close these gaps.

Automated Risk Management: Milo vs. Power BI

Automated risk management in iGaming includes things like player risk scoring, fraud detection, anti-cheating measures, and compliance checks (KYC/AML). It’s a mission-critical domain: operators and game platforms must quickly spot players who are exploiting vulnerabilities or engaging in fraud, while also managing normal “lucky streak” winners fairly. Let’s compare how Power BI and Milo handle this domain:

• Real-Time Fraud Detection: Effective risk management requires real-time monitoring of player activities and transactions. A good system will automatically flag suspicious behavior, for example, rapid bet patterns that resemble bot activity, or a player who suddenly wins far beyond statistical probability. With Power BI, achieving real-time alerts is difficult. You could build dashboards that update frequently and even use Power BI’s alert functions on certain thresholds, but it’s not truly built for instant pattern recognition. Power BI dashboards don’t “think”, they won’t tell you that Player X is likely a fraud risk unless you’ve pre-defined specific rules or manually notice the pattern. By contrast, Milo operates as an AI-driven analyst that continuously watches the data. Milo can ingest streaming data from various sources and leverage AI/ML models to identify anomalies or outliers in real time. For example, Milo might detect that a player’s winning streak is statistically aberrant and correlate it with known fraud patterns (like using multiple accounts or exploiting a game bug). It can then proactively alert the team or even trigger an automated action. Alternatively, it can ingest game rules and detect a player simply exploiting a certain strategy (e.g. martingale) and having “a run” (see below). In essence, Milo adds a layer of intelligent surveillance on top of your data, whereas Power BI is only going to show what you ask it to, after the fact.

• Differentiating Lucky Streaks from Cheating: A perennial challenge in iGaming risk management is telling apart genuine luck from foul play. Suppose a player hits an unusually long lucky streak on a roulette game. A human risk analyst might need to dig into game logs, rule, RNG outputs, and the player’s betting history to conclude if it’s just luck or something nefarious. With Power BI, the analyst could pull up reports of the player’s activity, but the tool won’t give an explanation; it’s a manual investigation. Milo, on the other hand, can act like an AI risk analyst continuously doing that heavy lifting. Milo’s unified data model can combine data from gameplay, transactions, rules, user accounts, and even third-party fraud blacklists. Through machine learning, it can learn typical patterns of play and identify “unusual patterns” that deviate from normal behavior. For instance, Milo might flag that this “lucky” player won 15 out of 15 bets in an improbable pattern and note that those bets exploited a specific game at a specific time, something that correlates with a known software glitch. It might conclude the player is exploiting a vulnerability. Conversely, if the streak looks random and the player’s behavior doesn’t match any fraud patterns, Milo would score it as low risk, avoiding unnecessary alarm. The key is automation: Milo provides AI-based risk analysis that can adapt and learn, whereas Power BI would require a person to ask the right questions of the data.

• Action and Response Times: When a risky event occurs (say, suspected fraud or a breach of betting limits), time is of the essence. Power BI being a reporting tool has no built-in mechanism to directly act on data, it won’t lock an account or send an email to compliance by itself. You would need to integrate it with another system or have a human do it. Milo is designed with an “agentic” philosophy, not only does it explain the data, it can also trigger actions if allowed. In a risk management context, Milo could automatically trigger an alert to the fraud team on Slack/Teams, or even invoke an API to freeze a withdrawal if the data strongly indicates fraud (based on rules you set) if you allow it. This drastically cuts down response times. Milo’s creators report that it can cut the time from data insight to operational action from days to minutes, all without waiting on a human analyst. For a non-technical manager, this means peace of mind, you know the “AI analyst” is always on duty, catching red flags immediately, instead of you discovering them only in a weekly metrics meeting.

In short, Power BI vs. Milo in risk management is like comparing a rear-view mirror to an AI co-worker. Power BI shows you historical data and maybe basic alerts if configured, but Milo actively monitors, analyzes, and reacts in real time. It provides the kind of continuous, smart oversight that modern iGaming operations need to combat fraud and ensure player safety.

Product Performance Analysis: Finding the “Why” Behind the Numbers

Beyond risk, iGaming companies live and die by their product performance, how well games or betting products engage players and generate revenue. Business stakeholders constantly ask questions like: “Why did our live casino game’s revenue drop 10% this month?” or “What’s driving the surge in bets on game X?” Traditional BI tools often leave these “why” questions unanswered.

Here’s how Power BI and Milo differ when it comes to product performance analysis and insight generation:

• From Metrics to Meaning: Power BI will faithfully present your KPIs, e.g., showing Gross Gaming Revenue, Active Users, Bet Conversion Rate, etc., often in real-time or near-real-time dashboards. This is useful for monitoring the health of your product. However, when a metric moves unexpectedly (say retention drops by a few percentage points in a week), Power BI doesn’t explain the change. As a manager, you see the “what” instantly but then face a gap before you get the “why”. You might try to drill down in a dashboard or use a built-in filter, but often the real causes hide across multiple data dimensions (player demographics, game versions, marketing promotions, time of day, competitor activity, game positioning etc.). It typically requires an analyst to perform ad-hoc analysis to pinpoint the drivers of the change. Milo tackles this challenge head-on by providing conversational, AI-driven analysis of the data. Instead of just static charts, Milo is built to uncover root causes automatically. For example, if revenue for a game dropped, you could simply ask Milo: “Why did game X’s revenue fall last month?” Milo might respond with an analysis like: “Revenue fell 10% mainly because the average bet size declined. Notably, high-value players from Portugal  wagered 20% less, coinciding with the end of a promotional bonus on Oct 15th.” In effect, Milo does the multi-factor breakdown for you, highlighting which player segments or external factors are most correlated with the change. This kind of causal analysis (the “Why”) is delivered in minutes conversationally, without waiting days for a detailed report.

• Ease of Use: Ask, Don’t Build: With Power BI, gaining a new insight often means building something a new visual, a new measure, a new slice of data. Non-technical users can find this daunting; the tool’s strength (flexibility and depth) becomes a weakness if you don’t know how to harness it. Milo turns insight-seeking into a simple Q&A experience. It’s a conversational BI platform, you literally interact with it like you would with a colleague. You can type or even voice message a question, and Milo will generate an answer backed by data, complete with charts when appropriate. This is a game-changer for product performance analysis: a product owner can get clarifications on the fly. For instance, “Milo, which player cohort has the highest drop in activity for game X?” or “Show me a breakdown of game X performance by region and highlight anything unusual.” Milo would not only create the visuals but also narrate insights (“Players in the 18-25 age group showed a significant drop after a certain date, which is unusual compared to other groups”). The natural language interface means you don’t need to be a data expert, any stakeholder can probe the data in plain English and get meaningful answers. Milo is trained to ask the non-technical user clarifying questions if context is lacking. This is critical as it avoids users having to be prompt engineers to get something useful out of the AI. As a result, insights no longer remain trapped with the data team; they’re democratized across the organization.

• Understanding Context and “Connecting the Dots”: One of the harder parts of analyzing product performance is connecting data from different sources to get a full picture. For example, understanding why a game’s performance changed might require correlating gameplay data with marketing campaign data, customer support logs (were there outages or complaints?), or even external factors like a competitor’s new release. In Power BI, if these data sources are not already modeled together, someone has to do that integration and figure out relationships. Milo was built with a Context-Aware Unified Data Model, which essentially means it can bring together data from various sources and automatically build logical relationships between them over time. Practically, this means Milo “learns” the context of your business. It might know that a drop in in-game purchases could be related to a recent change in the payment gateway or a surge in support tickets about a bug, because it has access to all those data points and recognizes their connections. Milo uses this context to inform its answers. To a business user, this comes across as the system having business acumen. You might get an insight like, “The performance dipped due to a payment failure issue, transactions dropped on that game specifically during the last week, which matches an increase in error logs from our payment API.” Such multi-source, contextual insight is hard to achieve with static BI unless you anticipate every question in advance. Milo’s AI-driven approach surfaces these insights proactively. It’s like having a diligent analyst who not only looks at the primary metrics but also checks all related factors before answering your question.

• Faster Decisions, Data-Backed Actions: Ultimately, the goal of performance analysis is not just to understand the past, but to decide what to do next, quickly. If it takes a week to figure out why players are churning, that’s a week of inaction. With Milo, the loop from question to answer to action is drastically shortened. As noted, Milo can cut the time from a data question to an operational decision from days to minutes. And because it can integrate with communication tools (Slack, Teams, WhatsApp) and even trigger actions, the insights are immediately actionable. A decision maker could get an alert from Milo saying “Retention for VIP players is down due to issue Y” and within the same interface instruct Milo to “schedule a bonus campaign for those VIP players” (assuming marketing systems are connected). This kind of seamless insight-to-action workflow is something Power BI doesn’t offer out of the box. Power BI might tell you retention is down (if you look at the dashboard), but everything after that, understanding cause, deciding on a remedy, executing it, falls on humans and separate systems. Milo strives to close that loop within one platform, which is particularly valuable for business stakeholders who want results, not just reports.

Case in Point: BetGames Embraces Milo for Scalable Insights

To illustrate these differences in a real-world scenario, consider BetGames, a rapidly growing game studio known for its live dealer and RNG games. As BetGames expanded their games portfolio by 2x YoY, their data volumes and operational complexity grew. They needed to ensure fraud and risk were under control even as player numbers soared, and they wanted detailed insight into game performance drivers to stay competitive. Initially, like many studios, BetGames relied on a traditional BI setup (with tools like Power BI) for dashboards and reporting on game KPIs. However, they encountered the typical challenges, static reports that didn’t tell why metrics were changing, and slow turn-around on deeper analysis. Their risk team also struggled with disparate data sources; catching a fraud pattern required piecing together data from game logs, user accounts, and payment records, largely by manually exporting data into spreadsheets or BI queries. This reactive approach meant some suspicious behaviors slipped through the cracks until after losses mounted.

BetGames decided to onboard Milo to augment and eventually replace parts of their BI workflow. The results were transformative. According to a case study presented by Milo’s team, BetGames was able to “scale operations and keep every risk in check with Milo”. In practice, this meant that BetGames integrated all their key data streams (gameplay, transactions, user profiles, game rules, marketing events etc.) into Milo’s platform. Non-technical team members, from product managers to the CEO, began using Milo’s conversational interface to get insights on the fly. Instead of waiting for a weekly report, a product manager could ask “Milo, how did yesterday’s new feature release impact player bets?” and get an immediate analysis. Milo might highlight that overall bets increased 5%, primarily from high-value players on the new feature, but also note an anomaly that new player bets slightly dropped (perhaps indicating a need to tweak the tutorial). This level of nuanced insight (including anomalies) was something they rarely got from static dashboards before.

On the risk management side, BetGames saw an immediate upgrade. Milo’s AI risk monitoring started flagging patterns that the team hadn’t programmed in manually. For example, Milo identified a cluster of accounts that were coordinating bets (“player syndicate”) in a way that suggested collusion, a subtle form of fraud that hadn’t been visualized in any Power BI report. By catching it early, BetGames intervened and saved significant potential losses. Another time, Milo distinguished a true positive fraud (a player exploiting a loophole in a bonus system) from what initially looked like a lucky streak, preventing an unnecessary VIP ban on a genuine player. These kinds of judgments typically required senior analysts; now the AI analyst (Milo) was handling first-line detection, 24/7. The result was not just fewer fraud incidents, but also a boost in the risk team’s efficiency, they could focus on confirmed cases and strategy, rather than combing through data logs.

Importantly, BetGames reported improved decision speed and confidence across the board. With faster insight into why KPIs were moving, their leadership could make data-backed decisions in hours, not days. One executive noted that decisions no longer “died in dashboards”, a nod to Milo’s philosophy of turning insights into actions. If revenue was dipping, they identified the cause and took action (e.g., launching a targeted promotion or fixing a product issue) before the trend worsened. Since adopting Milo, BetGames has maintained strong growth without the growing pains of information bottlenecks; it exemplifies how an AI-driven BI approach can empower a team to be both data-driven and agile.

Embracing AI-Driven BI for iGaming Success

In the iGaming world, where minutes can mean thousands of dollars and player trust is paramount, relying on traditional BI tools like Power BI can leave decision-makers a step behind. Power BI remains a powerful tool for aggregating and visualizing data, but as we’ve seen, it often underperforms in scenarios that demand real-time intelligence, deep why-level insights, and easy accessibility for non-technical users. Its dashboards show the numbers but seldom the narrative behind those numbers. This gap forces many leaders to either wait on data teams or fall back to intuition, neither of which is ideal in a competitive industry.

Milo, the AI-driven GenBI platform, represents the next evolution of business intelligence tailored for such challenges. By acting as an agentic AI data analyst, Milo brings several key advantages for iGaming stakeholders:  

• Proactive Risk Management: continuous monitoring for fraud and anomalies, with AI that learns patterns and can act in real time to mitigate risks.  

• Conversational Insights: the ability to ask questions in natural language and get meaningful answers instantly, making analytics accessible to anyone in the organization.  

• Why, Not Just What: automated root-cause analysis that explains performance changes (e.g., why a metric is up or down), enabling truly data-informed decisions rather than guesswork.  

• Integrated Actions: a seamless bridge from insight to action – whether it’s alerting a team, recommending a response, or even executing a task in an integrated system – compressing the decision cycle from days to minutes.  

• High Adoption & Low Friction: an intuitive interface (available through everyday tools like Slack or WhatsApp) that encourages frequent use. This helps overcome the low adoption problem seen with traditional BI – when getting an answer is as easy as messaging a colleague, more people in the business will actually use data daily.

For iGaming decision-makers frustrated with static reports and blind spots, Milo offers a path to regain control and agility. It empowers teams to be proactive, catching issues before they escalate, and to deeply understand their business drivers without waiting in a data queue. As the case of BetGames demonstrated, adopting an AI-gen BI approach led to faster insights, safer operations, and ultimately better performance outcomes.

In conclusion, while Power BI and similar tools laid the groundwork for data-driven decisions in the past, the future belongs to conversational, AI-powered analytics, especially in fast-moving industries like iGaming. By embracing tools like Milo, gaming operators can turn their data into a true competitive advantage: making every decision informed by evidence, every risk managed with vigilance, and every opportunity seized at the right moment. Don’t let critical decisions die in static dashboards; it’s time to let an AI analyst like Milo help them come alive and drive action in your iGaming business.  

‍