ServiceNow Media and Entertainment New York, New York

NBA

IT Operations Automation · Real-time Analytics

The NBA needed to protect the uptime and fan experience of its global app, streaming, and digital services while developers continued using diverse monitoring tools across cloud-native environments, creating more operational signal than people could correlate quickly.

20 to 30 alerts requiring investigation
200 million logs generated by the NBA App each day
99.999% reduction in noise
Two or three resulting in remedial action

Reported impact

Business outcome

The NBA now analyzes more than 200 million NBA App logs each day, cuts alert noise by 99.999%, narrows monitoring down to 20 to 30 alerts needing investigation with only two or three requiring remedial action, and strengthens incident management before fan experience is affected.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

The NBA layered ServiceNow AIOps across its digital ecosystem rather than forcing teams onto a single monitoring tool. Bespoke monitoring tools, cloud services, and application telemetry feed Health Log Analytics, which collects and correlates machine-generated log data in real time and uses machine learning to detect deviations without manually defined thresholds. IT Operations Management provides the operational foundation around that analysis, while ServiceNow AIOps correlates signals across services and data sources so the SRE team sees only the issues that matter. When a significant anomaly is confirmed, IT Service Management opens an incident, shares context with stakeholders through the collaboration workflow, and captures the resulting resolution and lessons learned back on the platform. The design lets the NBA analyze more than 200 million app logs per day, surface a small set of actionable alerts, and catch fan-impacting issues before they degrade the viewing experience.

Data flow

Logs and monitoring signals from the NBA's application and infrastructure estate feed Health Log Analytics and the wider ServiceNow AIOps layer. Correlated anomalies are narrowed to a small set of actionable alerts, and the important ones open IT Service Management incidents so teams can coordinate remediation and feed back what mattered for future detection.

Solution architecture

4 components · 2 layers
  1. Compute
    • ServiceNow AIOps Correlates insights across the NBA's services and data sources so engineers can predict and prioritize issues before users are affected.
    • Health Log Analytics Collects, correlates, and analyzes machine-generated log data in real time to detect anomalies and identify likely root causes.
  2. Orchestration
    • IT Operations Management Provides the operational technology layer the NBA uses with HLA to deliver AIOps capabilities across its environment.
    • IT Service Management Launches incidents, brings stakeholders together, and records resolutions and learning points when AIOps identifies a meaningful issue.

Architecture clues

  • ServiceNow AIOps is integrated with the NBA's bespoke monitoring tools across its ecosystem.
  • The NBA relies on public cloud resources and Kubernetes elastic auto-scaling to handle streaming demand.
  • The rollout is expanding from consumer apps to back-office and network functions.

Evidence from the source

An important dimension of the support we provide for our teams comes with our use of ServiceNow Health Log Analytics (HLA) and IT Operations Management technology to deliver AIOps capabilities.
HLA analyzes more than 200 million logs generated by the NBA App each day.
ServiceNow has seamlessly integrated into this environment, providing AIOps and case management capabilities realized in the native environments developers have chosen.
ServiceNow's AIOps sits across the NBA's entire ecosystem, integrated with all its bespoke monitoring tools, providing a crucial additional layer of intelligence by analyzing and correlating insights across different services and data sources.
That's a 99.999% reduction in noise, and those two or three items requiring additional investigation are always important.
Transparency Evidence & methodology 90/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. ServiceNow

    NBA customer story

    First-party
    https://www.servicenow.com/customers/nba-aiops.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 30, 2026 · ServiceNow · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.