Why Observability Matters for iGaming Platforms
There are millions of events processed by iGaming platforms every day. With so many different elements of the technology system for these companies, it is almost impossible to identify what is causing any performance issues for the platform.
While monitoring is helpful for understanding when something has happened to a platform, observability allows engineering teams to understand the reason behind the event.
With observability, engineering teams are able to gain deep visibility into the applications, the infrastructure of a platform, and the activities of its users. This provides an understanding of any issues with the applications and allows engineers to resolve them quickly.
For companies that are online 24/7, having observability as an engineering tool is crucial to providing an experience that meets the expectations of their customers.
What Is Observability?
Observability is the capability for a software system to be understood by analyzing the data that it generates.
Engineering teams are able to collect information from multiple data sources about the software system to understand how it is operating.
There are three main components of observability:
Metrics
Logs
Traces
These three elements will provide engineering teams with a complete understanding of how their platform is performing.
Metrics: Measures the health of the system
Examples of metrics include:
API response times
Active users
Database latency
Error rates
CPU utilization
Memory consumption
All of these will be displayed on a dashboard that engineers can use to monitor the platform’s health.
Logs: Provides information about the system’s activities
Some of the information that will be logged includes:
User authentication
Payment processing
Affiliate redirects
API requests
System errors
Security events
The information logged can be reviewed at a later date to determine how the platform functioned during that time.
Traces: Follow the path of requests going through the system
Many iGaming platforms have a variety of services into which players’ requests must go.
An example of such a process for a player attempting to register on a site may include:
Traffic routing
Landing page services
User authentication
Identity verification
Payment processing
CRM synchronization
Through trace logs, engineers can follow these requests and determine which service is causing any issues.
Five Ways to Improve Observability for iGaming Platforms
Improve incident response
Monitor affiliate tracking
Protect revenue during traffic spikes
Detect problems before users report them
Support better deployments
Improve incident response
When something happens to a platform, every minute counts for the engineering teams working to resolve that issue.
Through observability, engineers are able to detect an issue, recognize the systems that are involved in that problem, determine its root cause, and then measure how well the implemented fixes resolve that presented problem.
Monitor affiliate traffic
For affiliate marketing platforms, it is important to ensure that the tracking of where users came from and where they went is accurate.
Through observability, teams can monitor aspects of affiliate traffic such as:
Click tracking
Redirect performance
Registration attribution
Commission events
Conversion reporting
Protect revenue during traffic spikes
For many iGaming platforms, there are traffic spikes following major sporting events.
During these periods, engineers must monitor the platform’s resources to ensure that it can handle the increase in users. These resources can include:
Request throughput
Queue processing
Server capacity
Database load
Network latency
These measurements will allow engineers to adjust platform resources to ensure that there are no declines in performance.
Detect problems before users report them
Engineering teams want to avoid situations in which they must respond to customer complaints or feedback regarding the performance of their applications.
Through observability, the tools can automatically detect issues with their applications such as:
High error rates
Slow page loads
Failed API requests
High infrastructure usages
These issues can then be resolved before they have an impact on the customers of that platform.
Support better deployments
When a new version of software is deployed into production, monitoring that software through observability allows engineers to measure the impact of that software update.
For example, engineers can use observability to determine if the new software:
Improves the performance of the iGaming platform
Creates new errors within the system
Maintains the expected response times for platform requests
All of these elements help engineers to ensure that a software deployment will not adversely impact the platform’s customers.
Improve API reliability
As with any platform, APIs are used as the means of connecting the platform to other systems and services.
By using observability for these APIs, engineers gain insight into their:
Request success rates
Authentication failures
Timeout frequency
External service performance
By ensuring that these APIs are running smoothly, the experience of both the customers and partners is positively impacted.
Strengthen security monitoring
In addition to monitoring the performance of the system, observability tools also provide insights into the security of that platform.
Some of the elements that can be monitored include:
Suspicious login activity
Unusual API requests
Traffic anomalies
Failed authentication attempts
Potential DDos attacks
By monitoring these aspects of security, engineers can ensure that the platform is protected from potentially damaging events.
Improve the customer experience
A major goal of any iGaming company is to provide an experience for their customers that is as satisfying as possible.
By using observability to monitor the performance of their platform, engineers can recognize elements that may affect the customer experience, such as:
Slow registration pages
Payment processing delays
Mobile performance
Broken redirects
Navigation issues
These elements, when resolved, will improve the customer experience and increase the number of customers that a platform can acquire.
Reduce Mean Time to Resolution (MTTR)
One of the most important engineering metrics is Mean Time to Resolution (MTTR) - the average amount of time that it takes to resolve an incident in a software system.
By giving engineers the information that they need to resolve incidents, observability will reduce the MTTR for the engineering teams for that platform.
By reducing the amount of time it takes to resolve an issue, the platform will experience the following:
Increased uptime
Improved user experiences
Lower operational costs
Increased customer confidence
AI Is Transforming Observability
Artificial intelligence technologies are adding new capabilities to monitoring platforms for engineering teams.
AI can allow engineers to:
Detect anomalies in a system automatically
Predict when failures in the infrastructure may occur
Recommend actions for increasing the scalability of a platform
Recognize which incidents are related to one another
Prioritize alerts according to the criticality of the issues
These AI abilities can help to reduce alert fatigue by engineers and allow them to focus on the most important alerts.
Best Practices for Observability
Engineering teams that have successfully managed software platforms have typically used the following practices regarding observability:
Monitor every service in production
Collect all system logs in one centralized location
Instrument their APIs with distributed tracing technologies
Define performance baselines for their software
Create alerts for engineering teams to respond to
Review the health of the system regularly
Automate alerts for engineering teams regarding any new issues
Continuously improve their observability dashboards according to their changing software
By following these best practices, engineering teams gain the most out of their observability software tools.
Future Trends in Platform Monitoring
Observability software is still evolving in response to the types of infrastructure upon which many software platforms are built.
Some of the future trends in platform monitoring software may include:
Predictive monitoring for detecting upcoming issues
Autonomous resolution of incidents without human engineers
AI-generated insight into the root cause of system failures
Alerting engineers of issues that may impact a business or revenue rather than the software itself
Monitoring the entire customer journey through the platform to identify areas for improvement
All of these developments will allow engineers to identify and resolve issues before they have an impact on the customers of a platform.
Final Thoughts
For iGaming platforms, observability software has become a necessity in monitoring the health and performance of their systems.
As the platforms become even more distributed and the amount of data that they process increases, it is harder for engineers to understand the health of those platforms based on monitoring software alone.
However, by using a system that incorporates metrics, logs, traces, and alerts, engineering teams can gain deep visibility into the iGaming platforms that they manage. This deep visibility allows engineers to diagnose and resolve any issues quickly, as well as provide a better experience to the customers of that platform.
For platforms looking to scale and grow in the future, implementing an observability software system is one of the smartest engineering decisions that they can make.
AI Is Transforming Observability
Artificial intelligence technologies are adding new capabilities to the monitoring platforms upon which engineering teams rely to manage the systems of those companies.
These new features of artificial intelligence for monitoring platforms may allow engineers to automatically detect anomalies in systems, predict failures of the infrastructure of those companies, recommend actions to increase the scalability of those platforms, recognize which incidents are related to each other within the system logs of those companies, and prioritize alerts for engineers according to their criticality.
Each of these artificial intelligence features will reduce alert fatigue of engineers and allow them to focus on the most critical alerts.