Why Real-Time, AI-Based Anomaly Detection Is a No-Brainer
Learn why real-time, AI-based anomaly detection is a no-brainer for the efficient enterprise.
Join the DZone community and get the full member experience.Join For Free
In the earliest days of big data, collection was the top priority. Business leaders needed to find innovative ways to collect as much information about customers and operations as possible.
Now that this goal has been accomplished, a new problem has arisen. There is enough data available to optimize user experience, network performance, business operations, and more, however, between 60 and 73 percent of that data never gets put to good use.
There is an overwhelming amount of different metrics and systems to track, making it increasingly difficult to evaluate business patterns and, more importantly, deviations.
This is why anomaly detection plays such a critical role in the modern, efficient enterprise.
Anomalies in your business can be either positive (sales spikes during the holiday season) or negative (application performance issues that hurt productivity and diminish revenue). In both cases, you need an efficient way to pinpoint the precise business incidents causing changes in data patterns.
But traditional, manual anomaly detection is no longer enough. Now, the only way to accurately scale to the demands of an efficient enterprise is to embrace automated, real-time anomaly detection.
Manual Thresholds Fall Short for Anomaly Detection
If you only had a handful of business metrics to track, you might be able to get by with manual thresholds for anomaly detection. Even so, speed and efficiency will prove challenging.
With manual thresholds in anomaly detection come an abundance of unknown unknowns. These are challenges that you never saw coming — so you clearly didn’t take any precautionary measures. In the case of cybersecurity incidents and application performance errors, time is of the essence. Quickly identifying and resolving these issues can make significant differences in your bottom line.
AppNexus, a company that helps brands optimize their programmatic online advertising, faced this exact problem. According to VP of Engineering Travis Johnson:
“We wanted to be able to reach out to our clients to work with them to resolve these issues faster. Every minute counts, every minute can be a missed impression. However, sifting through 10 billion daily transactions to try and find these signals was a difficult problem for us.”
For a company like AppNexus, where each processed transaction includes 40 different tracked metrics, manual thresholds aren’t a scalable solution to anomaly detection. Even with hundreds or thousands of analysts on the job, there’s a high likelihood that anomalies would go undetected — or, at the very least, the team would deal with many false positives.
But manual anomaly detection doesn’t just increase operational costs when pushed to scale. It can lead to significant revenue loss.
To make up for the limitations of human analysts and manual processes, you can introduce machine learning to anomaly detection processes, freeing up money and manpower in more strategic ways across your business.
Online Machine Learning Algorithms Elevate Anomaly Detection
Anomaly detection must be sophisticated enough to analyze the most complex datasets and identify the subtle changes in patterns that could positively or negatively impact your business.
The only way to meet this demand, at scale and in real time, is to take advantage of machine learning. An online machine learning algorithm can enhance anomaly detection to a level capable of keeping pace with today’s business.
An online machine learning algorithm processes each data point in a sequence just one time. Because it is not reanalyzing the same data points over and over, this kind of anomaly detection system can easily scale to handle endless amounts of business data. With this method, your algorithm is constantly defining what “business as usual” means before applying statistical tests that determine whether each data point is an anomaly.
As each data point in the time series is processed:
A model is created to fit the data
That model is used to predict the value of the next data point
If the next data point differs significantly from the predicted model, the data point is flagged as a potential anomaly.
As anomalies are detected, an online machine learning algorithm uncovers relationships between metrics and filters results down to a more manageable number of correlated incidents.
Taking this automated approach to anomaly detection has a number of significant benefits for your business.
Four Benefits of AI-Based Real-Time Anomaly Detection
Anomaly detection has countless use cases across your organization. From the backend technologies that support your workforce to internal applications, customer-facing services, and operational processes, understanding baseline activity and recognizing when you’re off track is essential to enterprise efficiency.
And when considering anomaly detection solutions, there are clear business benefits to having artificial intelligence in the form of online machine learning algorithms:
True real-time detection: Automating the backend processes of anomaly detection means you can receive insights at increasingly faster rates over time. Thanks to online machine learning algorithms, you get up-to-the-second insights that allow you to address anomalies immediately. And even if an immediate response isn’t necessary, you’ll have the insights necessary to prioritize your next steps.
Accurate insights: Pinpointing specific business incidents that cause anomalies takes the complex data science out of traditional detection processes. Opportunities are presented to business users beyond any human capabilities. Better yet, you receive insights that would never be identified by higher-level human analysis.
Limitless scalability: Data generation won’t slow down anytime soon. Online machine learning algorithms are your only chance to handle high volumes of metrics and the thousands (or millions) of data points that come along with them. Without an AI-based system, you’d miss out on the real-time decision-making that’s necessary to keep pace with business demands.
Holistic automation: Fully automating the detection, ranking, and grouping of anomalies isn’t just a factor in speeding up your responses. It also opens the door to analyzing your organization at a more holistic level. More advanced solutions and online machine learning algorithms can identify relationships between patterns across systems and functions in your organization, giving you deeper insights to optimize the business.
These benefits combine to create a more proactive enterprise. Regardless of industry, efficiency is all about limiting wasted processes while amplifying the positive anomalies across an organization. Using AI-based, real-time anomaly detection to analyze datasets gives you the foundation to increase efficiency continuously over time.
Bringing Real-Time Anomaly Detection to the Entire Enterprise
Real-time AI anomaly detection allows companies to get accurate feedback on the effectiveness of business initiatives. It helps you capitalize on new opportunities, fix costly problems and ensure that both money and manpower are used efficiently throughout your organization.
But anomaly detection shouldn’t be reserved for the engineers and data scientists who can grasp the backend complexities of how it works. With AI-based anomaly detection, any business user can take advantage of the valuable insights produced.
We can’t let the perceived value of big data outpace our ability to make the most of it. And it won’t, so long as our business metrics are continuously analyzed by automated machine learning algorithms that report incidents in real time.
The efficient enterprise is one that proactively optimizes all processes and takes advantage of every insight at hand — even if those insights come from questions that you never asked.
Published at DZone with permission of Gal Ben-Avinoam. See the original article here.
Opinions expressed by DZone contributors are their own.