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Frictionless retail: A pandemic imperative for IT

Dynatrace

For most who work in the retail sector, the pandemic has been an unwelcome test of our ability to cope with disruption. In eight months, retailers offering curbside pickup increased from 7% to 44%, reflecting rapidly changing consumer preferences. Let’s illustrate a simple use case for a retail outlet. Dynatrace news.

Retail 133
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Intelligent, context-aware AI analytics for all your custom metrics

Dynatrace

Dynatrace recently opened up the enterprise-grade functionalities of Dynatrace OneAgent to all the data needed for observability, including metrics, events, logs, traces, and topology data. Davis topology-aware anomaly detection and alerting for your custom metrics. Seamlessly report and be alerted on topology-related custom metrics.

Metrics 245
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Data Mining Problems in Retail

Highly Scalable

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods. However, many of these models are highly parametric (i.e.

Retail 152
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Extend business observability: Extract business events from online databases (Part 1)

Dynatrace

But existing business intelligence (BI) tools often lack the broad context, ease of data access, and real-time insights needed to understand and improve customer experience and complex business processes. However, in the real world, business-related data isn’t limited to metrics.

Database 226
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Element Timing: One true metric to rule them all?

Speed Curve

forEach((entry) => { // process entry here; }); }); observer.observe({ entryTypes: ["element"] }); How Element Timing can fill the gaps that Largest Contentful Paint leaves. Element Timings can be collected in both Synthetic and RUM by configuring them in the Custom Metrics section of your settings: Adding a custom metric in SpeedCurve.

Metrics 141
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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

As a result, IT organizations are overwhelmed as they strive to balance cost control processes with ensuring that their respective organizations have access to all the data required for their various use cases. Consequently, the company’s mean time to identify (MTTI) and mean time to resolve (MTTR) during peak retail seasons was too slow.

Retail 237
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How multicloud observability boosts cloud performance at Tractor Supply Co.

Dynatrace

Rural lifestyle retail giant Tractor Supply Co. Rural lifestyle retail giant Tractor Supply Co. discussed the 85-year-old retailer’s cloud migration journey and the importance of multicloud observability at Dynatrace Perform 2023. “At one point, we saw a process that was causing a lot of CPU contention.

Cloud 171