The Power of Automated Anomaly Detection: Unlocking Insights From Big Data

As digital transformation accelerates across industries, the volume and velocity of data are growing rapidly, making it nearly impossible for organizations to manually detect anomalies.

The solution? Automating your anomaly detection to recognize trends before they materially impact performance.

Unlocking Insights and Getting Recommendations

Today’s data ecosystems are highly complex. Sifting through data manually or even diligently watching dashboards can obscure deviations, especially if they happen gradually over time. Things might look fine day-to-day, but hide troubling trends.

Automated anomaly detection transforms monitoring from reactive to proactive, flagging items when they fall above or below pre-set benchmarks. However, you need to go beyond just getting notifications. You need a solution that works behind the scenes to uncover underlying patterns and find the correlations that impact outcomes. It’s this level of detail that helps you identify the root cause of trends so you can take action.

The best BI solution will recognize anomalies, pinpoint root causes, and also suggest specific actions you can take to change outcomes. For example, a drop in sales might be traced back to an adjustment in your pricing strategy. This provides you with a way to review the specific action that led to the change.

Then, you need to assess the impact of making changes by adjusting variables within your BI platform to gauge the impact.

IDA does all of this. Analyzing both historical and real-time data, you can get notified about anomalies. You can run checks daily, weekly, or on any schedule you want — automating the process using Trip Wire.

Prescriptive Analytics Provides Actionable Recommendations

When an anomaly is detected, Trip Wire launches prescriptive analytics.

Prescriptive analytics leverages artificial intelligence and machine learning tools, running various what-if scenarios to look for the best options to resolve anomalies. Based on current and historical data, the algorithm models performance based on changes to underlying variables. Based on an analysis is likely outcomes, you then get a tailored recommendation about the best course of action to impact the outcome.

By simulating the probability of outcomes and assessing the probability of each, you can better understand the level of risk involved with potential changes or inaction.

Challenges in Implementing Automated Anomaly Detection

There are a few challenges you have to overcome. For example:

Data Quality

The output you get is directly related to the quality of the data you ingest. You need strong data governance policies to ensure data quality and accuracy. Data must be cleansed to eliminate rogue data, unmatched fields, and missing information that could otherwise skew results.

Data Quality

For companies with large data sets, you need an easy way to scale automated systems to handle data volume.

Shifting Norms

Anomalies are typically detected by deviations from baselines. In fast-changing environments, customer behavior can shift rapidly. Systems need to constantly monitor evolving data to avoid false positives and incorrect analysis.

Your system also has to be “smart” enough to distinguish between true anomalies and recurring trends, for example, the ability to identify seasonal trends or spikes during holiday seasons for retail sellers.

Human-Enabled AI

While anomaly detection and prescriptive analytics are powerful, you still need a human-in-the-loop approach. We like to think of IDA as human-powered AI, giving you the tools you need to make better decisions. It’s the combination of human intuition, experience, and AI that produces the best results in business.

Correlation Analysis

Overly simplistic BI programs can also miss the bigger picture. In most cases, anomalies are not isolated incidents. They are a combination of interconnected events that impact outcomes. Systems must be sophisticated enough to consider multiple variables and understand the interactions between variables.

A drop in B2B sales in one region might be as simple as your top salesperson being on vacation during the period or as complicated as a mix of high interest rates and cost of capital, a decrease in marketing spend, and new competitors with lower prices who are increasing advertising. You need a program that does multivariate analysis to truly understand root-cause correlations and suggest the appropriate action.

A No-Code BI Platform

Intuitive Data Analytics is a no-code BI platform that produces speed to insight — instantly discovering anomalies, problems, and opportunities in your data. IDA identifies the red flags, hunts for root causes and opportunities, and provides actionable solutions, so you can make data-centric decisions more quickly.

Test drive IDA today and see the difference for yourself.

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September 17, 2024