When David Ashirov joined Freshly, a prepared meal delivery service, the company lacked systems to measure and evaluate data. The business was largely reliant on human intuition to gauge its performance. This approach was sufficient for a startup, but as the company grew, it became clear that human intuition could not scale. Ashirov's primary challenge was to build a data fabric, a system that would connect data across the company, allowing for easy querying of every bit of data without unnecessary complications. The goal was to create a single source of information for any business question, fostering trust in the data among the company's employees.

Tinkoff, Russia's leading fintech innovator, was facing challenges in managing the exponential surge in data due to the rise of innovative fintech and digital banking solutions. The success of their fintech model was heavily dependent on the quality of the customer experience they provided. However, monitoring, managing, and reconciling the vast amount of data was compromising their internal productivity and resources. They needed a technology that would not only guarantee the highest level of customer satisfaction but also ensure operational efficiency across their platform.

Uprise, an ad-tech company, uses a 'continuous delivery' approach for its software development, pushing around 20 new software releases into production each day. Each new release can affect the platform’s performance, making it crucial to monitor results in a timely fashion to determine if the new release should be kept in production or rolled back. The ad tech environment itself has many moving parts, each of which is a potential point of failure. These can include server issues, changes at the ad affiliates, introduction of ad blocking software, or even fraud. Whenever a problem occurs, isolating the source can require complex, time-consuming analysis. Identifying issues in the first place is also tricky, since network traffic behaves seasonally. With the traffic naturally reaching various peaks and valleys throughout the day, noticing a 20% loss or gain at any given point is next to impossible.

Penske Media Corporation (PMC) was facing significant delays in discovering important incidents in their active, online business. The company was using Google Analytics’ alerting function to track business incidents but found it inadequate due to the millions of users across dozens of household-name and professional publications. The initial use case for PMC was to start using Anodot to track its Google Analytics activity, for example, to identify anomalous behavior in impressions or click-through rates for advertising units.

The company, an affiliate network with over 200,000 members, was struggling to monitor business and technical incidents that were impacting their bottom line. The dynamic nature of their marketplace and the extensive metrics they had to track made it difficult to monitor changes in real-time. Factors such as changes in search engine algorithms and third-party trends, as well as changes in affiliate accounts, could significantly impact their business. The tools they were using required them to set thresholds manually, which allowed time for incidents to escalate.

As a real-time trading company, eToro must provide users with reliable market rates as quickly as possible, necessitating close monitoring of the quality of the connection from both the client and server side. eToro had been using open-source tools to monitor the metrics from their Price Streams service that sends price quotes to their users. However, the company quickly realized that it needed to expand the number of metrics being monitored and faced resource challenges adapting their traditional monitoring tools to meet the new demands. With stringent regulations in Cyprus and the UK, eToro treats any trading error or problem as critical.

Magnite, the world’s largest independent sell-side advertising platform, processes trillions of transactions each month in real-time auctions that each occur within 40 milliseconds. The company's internal teams and existing tools could not scale to handle the growing volume and velocity of data. They needed real-time insight into incidents that were being detected too late, such as anomalies in normal transaction volume from a large buyer. Their manual alerting system with static thresholds also created costly alert noise and false positives. Magnite works with many demand-side platforms (DSPs) across its global data centers in different time zones. Along the bid stream, there are many potential areas for communication or technical breakdown, which would prevent the bid from going into the auction, and negatively affect overall bid health.

Companies are generating more data than ever before, and traditional dashboards are unable to keep up with the volume and complexity of the vital business data collected. This is particularly true for companies using a Snowflake warehouse. The businesses served by Anodot have millions of customers across the globe and must manage millions of daily business metrics involving product usage, application performance, APIs, log-ins, and payment gateways, among others. Traditional manual business monitoring solutions cause significant delays of at least 24 hours or longer in detecting and resolving critical incidents, which threaten to impact customer satisfaction, brand equity, and the company’s bottom line. Transactional and customer experience data is too volatile for static monitoring. Since business data is complex and dynamic, AI/ML-based autonomous solutions are critical for achieving business outcomes and avoiding blind spots. Static monitoring approaches based on dashboards, and manual thresholds aren’t sensitive, robust, or agile enough to withstand this challenge. AI-based early detection of revenue issues and business system failures is nonnegotiable.

King, a leading mobile gaming company, was facing challenges in managing business incidents in real time. The company's most popular franchise, Candy Crush, along with 17 other games, were in production. The incident management team, responsible for investigating incidents and assessing the losses incurred, needed a tool to detect and address these incidents promptly. The goal was to minimize the impact on revenue by spotting incidents as soon as possible. The team was also tasked with monitoring 18 Key Performance Indicators (KPIs) for each game, which amounted to a significant number of metrics. The challenge was not only to monitor these metrics but also to differentiate them based on the platform, build, and country. The existing tools were not sufficient for this task, as they were not adept at detecting subtle anomalies in business KPIs.

PUMA, a global eCommerce giant, was facing difficulties in monitoring all revenue aspects of their 45 eCommerce websites. They lacked a tool to distinguish what was normal or abnormal across their platforms. For instance, an issue with gift card purchases in Switzerland went unnoticed, which could have resulted in significant financial loss if discovered later. PUMA's Senior DevOps Manager, Michael Gaskin, was interested in Anodot based on the experience he had with another Anodot customer. He understood the challenges PUMA was facing and sought a solution to monitor their websites more effectively.