In this final installment of our three-part series, let’s recap our previous discussions of anomalies – what they are and why we need to find them. Our starting point was that every business has many metrics which they record and analyze. Each of these business metrics takes the form of a time series of data...

Learn how leading telcos are using Anodot's ML-based anomaly detection to ensure business support systems can keep pace with the high level of service required for mission-critical applications.

Learn how leading telcos are using Anodot and its real-time alerts to automatically monitor their network operations for proactive incident management.

If you're one of the many consumers using native apps 90% of the time you're on your smartphone, you know first hand that mobile apps are big business. So big, in fact, that they are expected to generate 188.9 billion U.S. dollars in revenues via app stores and in-app advertising by 2020. There's an app for just about everything, from games to ebooks, to dating, cooking, shipping, sharing photos and more, businesses are developing more and more mobile apps to reach and engage their customers. But do these apps make money? In addition to charging for the app, many app developers monetize through advertising, in-app purchases, referrals and cross promotions.   https://youtu.be/UBzh4McuFDc Once using an app, businesses offer targeted advertising per user, options for premium content like access to extra levels or additional features, suggestions for additional apps by the same company or for related content that the company will receive revenue from for referring anyone who clicks through and converts. With so many moving parts (e.g. frontend, backend, advertising platforms, partners), there are a multitude of opportunities for something to break, such as partner integration or data format changes, device changes like OS updates or new devices, external changes like media coverage or social media exposure, and company changes like deployments, new game releases, AB tests and more. Just like the butterfly effect, where the flap of a butterfly's wings can cause a string of events leading to a huge storm, if one element of an application is working less than optimally, it can cause major problems elsewhere, which translates into unhappy customers, uninstalls, revenue losses and drops in market share. With traditional BI and monitoring tools like dashboards and alerts, you may only realize that something has broken down once your uninstall numbers begin to rise or you notice that users have stopped returning. Only a small percentage of very dedicated users will try a crashing app more than twice, so fixing the problem before you've lost users in droves is of key importance. So, how can you mitigate problems on your business's mobile app keeping users happy and engaged? In a recent session at Strata Data San Jose, Ira Cohen, Anodot's Chief Data Scientist and co-founder, presented "The App Trap: Why Every Mobile App Needs Anomaly Detection," showing how to use automated anomaly detection to monitor all areas of your mobile app to fully optimize it. Watch the full video to learn more about the processes involved in automated anomaly detection -- metric collection, normal behavior learning, abnormal behavior learning, behavior topology learning and feedback-based learning -- and how, together, they can keep your app on track, making money, and keeping users happy.  

Learn how telcos are using Anodot to automatically monitor their OSS, BSS and CEM layers and use real-time alerts for proactive incident management.

The business case In the first of our three-part series, What is anomaly detection?, we summarize how machine learning is enabling real-time, automated incident management. In this second post, we’ll discuss the reasons why this capability is so essential to today's data-driven business. The necessity In our previous post, we gave an example of a software update causing online sales from Asia to plummet. Obviously an anomaly in online sales volume for any specific region or device type needs to be detected immediately, and the same is true for other anomalies. This is because many real-life business anomalies require immediate action. That bad software update is causing you to lose a lot of money every second. And since discovering the problem is the first step in resolving it, eliminating the delay between when the problem occurs and when the problem is detected immediately brings you one crucial step closer to rolling back that update and restoring revenue flow from Asia. This is also true for anomalies which aren’t problems to be solved, but opportunities to be seized. For example, an unusual uptick in mobile app installations from a specific geographical area may be due to a successful social media marketing campaign that has gone viral in that region. Given the short lifespan of such surges, your business has a limited time window in which to capitalize on this popularity and turn all those shares, likes and tweets into sales. Real-time anomaly detection is advantageous even when the detected anomalies include ones which don’t require an immediate response. This is because you can always choose to postpone action on an instant alert, but you can never react in real-time to a delayed alert. In other words, real-time anomaly detection is always advantageous over delayed detection. But let’s think about it - what kind of anomaly of detection systems are able to provide this type of real-time notification? For only one or a few KPIs, a human monitoring a dashboard may work. This manual approach, however is not scalable to thousands or millions of metrics while maintaining real-time responsiveness. Beyond the mere number of metrics in many businesses, is the complexity of each individual metric: different metrics have different patterns (or no patterns at all) and different amounts of variability in the values of the sampled data. In addition, the metrics themselves are often changing, often exhibiting different patterns as the data exhibits a new “normal.” Manual vs. automated anomaly detection If manual anomaly detection is inadequate, then automated anomaly detection must be used to achieve real-time anomaly detection at large scale, and it must be sophisticated enough to handle all the complexity described above at the scale of millions of data points or more, updating every second. The machine learning algorithms that power Anodot’s automated anomaly detection system utilize the latest in AI research to meet this task. Our patented machine learning algorithms fall under the “online” category. This means that each data point in the sequence is processed only once and then never considered again. Online machine learning applications have the added benefit of scalability to the massive amount of metrics businesses keep track of. As each data point is processed, the online machine learning algorithms work in a way similar to the human brain in the jogger example of the previous post: A model which fits the data is created. This model, in turn, is used to predict the value of the next data point. If the next data point differs significantly from what the model predicted, that data point is flagged as a potential anomaly. Anodot’s machine learning algorithms use each new data point to intelligently update the model. AI anomaly detection in the real world The power of this application of AI to spot anomalies and the opportunities they present far faster than humans could, has already been used to great scientific success. An AI system developed by NASA’s Jet Propulsion Laboratory was able to detect and command an orbital satellite to image a rare volcanic event in Ethiopia - before volcanologists even asked NASA for that satellite to take images of the eruption. When working with thousands or millions of metrics, real-time decision making requires online machine learning algorithms. Whether it’s saving your business money or gleaning scientific insights from a brief volcanic eruption, real-time anomaly detection has enormous potential for catching the important deviations in the data. In the third post, we’ll dive a little deeper into the anomaly detection techniques which power Anodot’s software.

Learn how leading adtech companies -- including Rubicon Project, Uprise and NetSeer -- are leveraging the power of machine learning to find outliers in time series data and turn them into valuable business insights.

Leveraging the vast amount of business data available today to better meet customer needs and detect business incidents presents organizations with the challenge of whether to build their own anomaly detection system or buy one ready-made. Before organizations make this critical decision, it is important to weigh the benefits and challenges of each approach.

Mobile app business models are often built around advertising and cross-promotions. Yet with so many moving parts, there are many opportunities for something to break.