Dynatrace has added a new raft of analytics to its software intelligence offering.
Known as Digital Business Analytics, the software itself is made of code (it’s digital), it’s intended for enterprise usage (that’s business) and it performs analytical functions on data already flowing through Dynatrace’s application and digital experience monitoring modules (yep, that’s the analytics part).
The company says it has brought together real-time AI-powered analysis to bind together user experience, customer behaviour and application performance data with business metrics.
The coming together of key indicators is intended to shed more light on sales conversions, orders, customer churn, release validation, customer segmentation and other areas.
Digital Business Analytics joins Application Performance Management (APM), Cloud Infrastructure Monitoring (CIM), Digital Experience Management (DEM) and AIOps as part of the Dynatrace all-in-one Software Intelligence Platform.
The company’s AI-engine is called Davis.
In operation, Davis continually learns what ‘expected normal’ business performance looks like and provides proactive answers to issues for optimisation of compute resources.
“Digital transformation projects are spurring companies to create multidisciplinary line of business teams that run the business with a product mindset and are demanding answers to questions that were previously difficult, slow or impossible to obtain,” said Steve Tack, SVP of Product Management at Dynatrace. “Digital Business Analytics complements existing web analytics tools to deliver real-time and complete results, by combining existing customer facing channels with application and user experience data.
Dynatrace says that as data volume and velocity accelerates, organisations are struggling to make sense of disparate dashboards from traditional IT monitoring tools, web analytics and ad hoc reporting.
The company insists that its Digital Business Analytics product automatically captures business data and analyses it in context with user experience and application performance data.
Key pillars of Digital Business Analytics include: Transactions: Automatic tracing, segmentation and data extraction from business transactions; Analytics: AI-powered analysis, exploration/querying and extraction of business-relevant insights from Dynatrace application and user experience data; Conversions: Visualisation of and collaboration on business-relevant metrics such as conversions and revenue performance by product, customer segment, geo etc. ; and Automation: AI-powered anomaly detection, alerting and root cause determination for business processes, with programmable APIs to trigger business workflows and change events.