Anomaly Detection


Using Elastic machine learning rare analysis to hunt for the unusual

It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.


Only Autonomous Anomaly Detection Scales

Say you’re looking for a smart product to detect anomalies in your organization’s IT environment. A sales rep drops by and shows you all kinds of great artificial intelligence (AI) features with fancy-sounding algorithms. It sounds very impressive and seems like there is a lot of very valuable AI in the product. But, in fact, the opposite is true. This is a manual AI product wrapped in a deceiving jacket. Let me tell you more.


Introducing: Business Impact Alerts

Anodot is the only monitoring solution built from the ground up to find and fix key business incidents, as they’re happening. As opposed to most monitoring solutions, which focus on machine and system data to track performance, Anodot also monitors the more volatile and less predictable business metrics that directly impact your company’s bottom line. Now there’s an easy way to measure the business impact of every incident.

My best growth experiment: Nitesh Sharoff, growth marketing & analytics consultant

The rise and rise of data analytics has brought with it superior digital experiences, personalisation and a golden era of hyper-growth companies, driven to success by a constantly-experimental mindset. We’re speaking to digital experts about the most effective growth experiments they’ve conducted and their tricks for improving customer experience, boosting ROI and increasing revenue, to help teams develop a more experimental approach to business growth.

Metric Matters: Nitesh Sharoff, growth marketing & analytics consultant

Data analytics is critical for any businesses hoping to be more competitive today. After all, data-driven organisations outperform their non data-driven peers to such a remarkable extent, they’re 23x more likely to acquire customers. From improved efficiency to better financial performance, there are countless benefits to using your data correctly.

Outlier Detection: The Different Types of Outliers

Time series anomaly detection is a tool that detects unusual behavior, whether it's hurtful or advantageous for the business. In either case, quick outlier detection and outlier analysis can enable you to adjust your course quickly, before you lose customers, revenue, or an opportunity. The first step is knowing what types of outliers you’re up against. Chief Data Scientist Ira Cohen, co-founder of Autonomous Business Monitoring platform Anodot, covers the three main categories of outliers and how you'll see them arise in a business context.

Supercharging omnichannel marketing with AI KPI analysis

In today’s uber-connected world, omnichannel marketing is one of the most effective ways of capturing consumers’ attention. With competition skyrocketing and digital transformation supercharging customer expectations, 90% of people now demand seamless, consistent interactions across all digital channels. As a result, businesses that use some kind of omnichannel strategy achieve 91% greater year-over-year customer retention rates than business that don’t.

Tackling shopping cart abandonment with data analytics

Today, the average shopping cart abandonment rate in online retail hovers at a disheartening 69.57%. To put that into an even more ominous perspective, that’s $18bn lost every single year. All because customers simply didn’t want to check out. What’s going on? There are dozens of reasons why you could be experiencing a high shopping cart abandonment rate. Yet understanding the exact reason isn’t an easy task.