Analytics and Monitoring: Better Together

As of recently, analytics were mainly targeted towards business and executives, while monitoring was referring more to systems and applications health and performance. Since both analytics and monitoring often use data from the same data sources and they both leverage machine learning algorithms and AI techniques to analyse this data and find hidden trends and patterns, it only makes sense that they are close and better together.

Faster Detection and Resolution

ML scans and analyses data in depth, correlates technical and business issues together and gets to the root cause faster. This enables immediate assignment to the appropriate person or team to take any necessary remediation actions. With AIOps being the new trend, the remediation action is in many cases triggered automatically by alerts. In doing so, issues are fixed faster and overall user experience is optimized.

You can ensure that no user or revenue is lost by getting answers to critical questions such as:
Can I identify friction points before my user base complains?
Why are conversion rates declining?
What is causing system instabilities?

Use Case #1: Login Monitoring

Many organisations are using machine learning as they are looking to understand the login behavior. Leveraging ML we can see that the number of logins is higher during specific hours of the day , or we discover more bugs after a code drop where two developers worked together etc, or a sudden drop in login attempts may imply a user experience problem, or a spike can mean a bot attack in the system.

ML technology can learn normal behavior patterns and analyse the login requests, responses and errors from the system. It enables us to identify the conversion rate from login start to login end, monitor login failures and interactions with features in the product.

A DevOps engineer can be notified once an anomaly occurs automatically and see if it is correlated to other related metrics such as users behavior or application errors.

So, analytics and monitoring are in the process of merging together.

Use case #2: Revenue monitoring

Being able to monitor detailed revenue streams across segments, products, payment providers, and devices in real-time is almost impossible without the help of AI. Through anomaly detection capabilities, we are able to identify missing revenue, create alerts and notify the appropriate people, and provide deep root-cause analysis so that any issues can be addressed quickly. Anomalies can be identified across all revenue streams such as microtransactions, purchase transactions, discount offers, revenue from partners and affiliates, and more.

Use Case #3: Customer Experience Monitoring

Customer interactions are across multiple funnels and journeys, generating huge amounts of data. To monitor this data which include logins, active users, conversion rates, and other critical KPIs is critical for ensuring the seamless customer experience that fuels your business. Customer experience metrics are hard to learn and understand, and are too dynamic to be monitored with static thresholds and manual reporting.

Through our AI-driven technology we can monitor the behavior of 100% of backend and frontend customer experience data and the system correlates all metrics to create context and visibility.

Use Case #4: Deployments Monitoring

Teams today deploy tens or hundreds of new features every day, and every version, A/B test, new feature, can impact performance. If all these deployments are not monitored closely for anomalies and defects, bad releases and performance issues can impact your revenue and damage brand reputation. With Validata Sense.ai you can detect performance issues faster and release better software more often.