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Also known as: Automated analysis, automated business monitoring, automated reporting
Automated analytics is the analytical capability to automatically detect relevant anomalies, patterns and trends and deliver insights to business users in real-time, with no manual user-analysis or IT intervention required. Emerging technologies such as artificial intelligence (AI), natural language generation (NLG) and machine learning (ML) algorithms are used to continuously monitor operational performance, track user-defined metrics and search large datasets for critical factors which align with desired business outcomes. It then generates alerts of any notable changes at fixed intervals or triggers, and delivers analyzed findings to users.
Automated analytics leans toward time-series based analysis and concentrates on changes in certain categories, such as average and total, trend direction, volatility, step changes, and outliers such as spikes and drops. As an advanced function, search and alerting parameters can be configured by the user to focus on very specific metrics, faster and across many more dimensions than users can manually analyze using self-service reporting and dashboards.
Ultimately, automated analytics acts as the shortcut to insights that enables both analysts and business users to jump straight to conclusions. It provides richer and more dynamic analytical experiences and serves to complement rather than replace traditional self-service business intelligence analysis by offering another powerful avenue for insight discovery and action.
Providing users with the powerful ability to perform automated analysis and monitoring in addition to the tools they use for manual self-service BI is invaluable to their insight discovery and decision-making efforts. Automated analytics offers benefits for both software vendors and end-users, and it can be used for a variety of business scenarios, such as detecting patterns of fraud, tracking changes in customer behaviour, and sending alerts for key achievements.
Independent Software Vendors (ISVs) and enterprise organizations can lower costs and improve ROI with automated analytics, as their analytics platform can proactively detect issues so both users and admin can prevent or resolve them before they become potential problems, leading to less risk and unnecessary expenditure. Being able to respond to changes in data faster increases agility throughout the business, and allows analysts and business users to focus on other priorities with the time saved from automatic detection and delivery of insights.
For end-users, automated analytics delivers more relevant and personalized insights as it can be pre-configured to monitor, track and deliver results to users based on which metrics are most important to them in real-time and far faster than what could be manually accomplished with traditional self-service tools alone. The intelligent design offered via machine learning algorithms can also create a behavioral baseline for the most important business metrics, which improves accuracy over time and creates more relevant alerts that save time and effort in their discovery.