Data is our superpower, but are we able to use this data effectively? Can we overcome the difficulties associated with the processing of big data? To make rapid, profitable operational decisions, we need to understand the insights hidden in our data sets. If we cannot intelligently process the data that we have, we cannot identify issues that impact our business or spot trends that affect us to take corrective actions in time.

The traditional data approach sends alerts when a critical parameter or parameters that we consider important reach a threshold value. At this stage, the effects of the problem (s) on our work have already started to manifest themselves. If we do not effectively track the pulse of the data in real-time, our forecasts will be inaccurate. It will cause both monetary loss and other inefficiencies. More importantly, if we don’t get the right information at the right time, we will miss out on profitable business opportunities.

CrunchMetrics - Anomaly Detection powered by Artificial Intelligence and Machine Learning

Today, I will take you through CrunchMetrics - an advanced anomaly detection system. By combining AI-ML based techniques and statistical methods, CrunchMetrics carefully analyzes large data sets and informs us of business-critical incidents in real-time. It helps us to know the unknown, to identify hard-to-detect anomalies, to discover new areas to invest, to stay profitable, to focus on relevant trends, and to find critical events related to your business – all in real-time.

The CrunchMetrics Advantage

Strengths of the CrunchMetrics platform include automatic real-time detection, AI and ML-powered, self-learning algorithms, complete integration, and flexible exit points.

The telecom industry can benefit by using anomaly detection to remain competitive despite decreasing margins and increasing cost pressures. The advantages, however, are not restricted to just the Telecom vertical. Advanced anomaly detection can be used by retailers and e-commerce businesses to track trends and stay profitable. Likewise, it is very important to detect abnormalities for FinTech companies. Although the platform mainly addresses these three sectors (telecom, retail/e-commerce, Fintech), it is a vertical agnostic and solves numerous use cases in many other industries as well.

Instead of threshold-based alerts to measure the health and effectiveness of a business, it would be much more accurate to look at the contextual abnormalities that allow timely and effective identification of the hidden trends in our data.

Before anomalies affect our business, it should be our goal to identify abnormalities almost in real-time and do the needful to minimize the impact of those abnormalities on the business.

It is also possible to increase profitability by discovering hidden opportunities from your data. You can discover new possibilities with automatic data analysis supported by artificial intelligence.

Get powerful insights from raw data in real-time

As a result of the rapid digitalization of industries, the volume of data that is generated on a day-to-day basis has increased tremendously. The amount of data collected in the past decade is now created in almost a few hours. Firms invest a lot of effort to capture and store this data. However, when we investigate what has been done with this data, we see that the mechanisms used to process such a large amount of data are not efficient. While there are complex BI tools available to turn historical data into meaningful insights, there is still a need for AI-driven analytics to dynamically and automatically monitor huge volumes of data at a granular level.

CrunchMetrics is a platform that provides ‘Business Discovery’ through anomaly detection mechanisms powered by artificial intelligence (AI) and machine learning. It helps to pinpoint the opportunities and incidents that are almost impossible to identify with traditional analytical methods.

CrunchMetrics learns from historical data to understand and determine what ‘normal’ behavior is. It then constantly monitors data flows to detect ‘abnormal’ patterns, which it describes as anomalies. It also contextually analyzes these anomalies and correlates them with different data signals in the organization to see if it’s really a business-critical event. Identified events are generated in real-time and flagged quickly to ensure that the stakeholders are informed instantly, thereby enabling a faster response time.

With CrunchMetrics automatic, real-time anomaly detection capabilities, large and complex data sets can be processed without errors, enabling real-time anomaly detection.

Equipped with AI and ML capabilities, the platform interprets existing data and obtains meaningful and thematic information, which can be applied to different use cases.

Here are some of the key advantages of the platform:

1. Powered by self-learning algorithms

Powered by self-learning algorithms, CrunchMetrics distinguishes itself in its ability to automatically evolve and evolve according to insights from each new event. As the database grows, the number of patterns discovered also increases, resulting in more accurate and consistent outputs.

2. Seamless integration

With the seamless integration feature of CrunchMetrics, it integrates seamlessly with your existing systems, whether it is a data warehouse or a complex BI system. This integration is achieved through web services or APIs.

3. Flexible output channels

With flexible output channels, CrunchMetrics can feed your existing workflow systems with its insights. You can take advantage of the built-in, intuitive, easy-to-use GUI, as well as direct the output to your existing workflow systems or call systems. As a complementary solution to fraud detection, you can also integrate it with existing fraud detection systems.

4. Vertical agnostic

Whether it is Telecom, Retail, FinTech, or any other industry vertical, CrunchMetrics can provide sufficient use cases to help you discover high-value business incidents through anomaly detection.

Anomaly detection approach of CrunchMetrics

Figure 1. Anomaly detection approach of CrunchMetrics

In Figure 1, we can see the approach used by CrunchMetrics for anomaly detection. As we mentioned earlier, normal behavior is determined first, anomalies are then discovered based on historical data, correlations are made with other data sources for a more consistent result and necessary information is given about the anomalies that will affect the business as a result.

About Gantek and CrunchMetrics

We, at Gantek have been working with Subex for years for income assurance, fraud management, and interconnection billing solutions in the telecom industry. Our trust in Subex combined with the strength of the product drove our efforts to add CrunchMetrics to our existing portfolio. At Gantek, we can give you a customized demo of CrunchMetrics, and initiate PoC studies specific to the usage areas that you determine.

Gantek Technology is one of the oldest and most reputable systems integrators within ICT (Information & Communication Technologies) industry and a global company with operations in more than 10 countries.

CrunchMetrics is an advanced analytics software that dynamically and proactively detects anomalies in business data. CrunchMetrics is part of Subex Digital LLP, a wholly-owned subsidiary of Subex Limited.

Originally published in Turkish in https://www.linkedin.com/pulse/crunchmetrics-anomali-tespit-platformu-ai-ml-ile-asiye-yigit/

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