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Domo Engineering

To empower organizations with real-time data insights by creating the operating system for business that connects everyone and everything

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To empower organizations with real-time data insights by creating the operating system for business that connects everyone and everything

Strengths

  • PLATFORM: Industry-leading self-service BI platform with 1000+ pre-built connectors enabling rapid data integration and visualization
  • SCALABILITY: Enterprise-grade architecture handling petabytes of data with sub-second query performance for over 3000 enterprise customers
  • UX/UI: Intuitive interface with drag-and-drop functionality earning 4.4/5 user satisfaction ratings and 92% user retention
  • MOBILE: Best-in-class mobile experience with native apps across platforms enabling 65% of users to access insights on-the-go
  • ECOSYSTEM: Robust API ecosystem with 800+ partner integrations and 150+ developer SDKs enabling seamless workflow automation

Weaknesses

  • TECHNICAL_DEBT: Aging core infrastructure components requiring significant refactoring, increasing maintenance costs by 18% annually
  • TALENT: Engineering talent gaps in emerging technologies with 22% open roles in AI/ML specializations hampering innovation velocity
  • ARCHITECTURE: Monolithic architecture components limiting deployment flexibility and extending release cycles to 6-8 weeks
  • TESTING: Insufficient automated testing coverage at 62% causing quality issues with 8.3 defects per 1000 lines of code
  • SECURITY: Legacy authentication systems and incomplete zero-trust implementation creating vulnerabilities flagged in 3 recent audits

Opportunities

  • AI: Implement generative AI capabilities across platform enabling natural language querying and automated insights for non-technical users
  • LOW-CODE: Develop expanded low-code/no-code capabilities reducing implementation time by 60% and expanding addressable market by 35%
  • VERTICAL: Create industry-specific solution templates for healthcare, retail and financial services targeting $4.8B market opportunity
  • EDGE: Develop edge computing capabilities enabling real-time processing where data originates, reducing latency by 78%
  • FEDERATION: Build federated data governance tools addressing increasing data residency regulations affecting 67% of enterprise customers

Threats

  • COMPETITION: Increasing competitive pressure from both established players (Microsoft, Tableau) and startups capturing 3.2% market share quarterly
  • COMMODITIZATION: Core visualization features becoming commoditized with 62% of features replicated by competitors at lower price points
  • TALENT_WAR: Intensifying competition for AI/ML engineering talent with FAANG companies offering 30-40% higher compensation packages
  • REGULATION: Expanding global data privacy regulations (GDPR, CCPA, PIPL) requiring significant compliance engineering resources
  • RECESSION: Economic headwinds causing 18% of customers to delay expansion and creating 22% longer sales cycles impacting growth

Key Priorities

  • MODERNIZE: Accelerate technical debt reduction and modernize architecture to microservices to improve scalability and development velocity
  • AI_INTEGRATION: Aggressively integrate AI capabilities throughout the platform to deliver automated insights and enhance competitive position
  • TALENT: Develop comprehensive engineering talent strategy focusing on AI/ML expertise and establishing development centers in tech hubs
  • SECURITY: Implement comprehensive zero-trust security architecture and enhance data governance capabilities to address regulatory requirements
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To empower organizations with real-time data insights by creating the operating system for business that connects everyone and everything

MODERNIZE CORE

Create a best-in-class modern technical foundation

  • MICROSERVICES: Complete migration of 5 core platform services to microservices architecture with 99.99% availability SLA by Q3
  • DEVOPS: Implement end-to-end CI/CD pipeline achieving 85% test coverage and reducing deployment time from 48 to 4 hours
  • TECHNICAL_DEBT: Reduce legacy code by 30% through strategic refactoring, cutting maintenance costs by $2.1M annually
  • SECURITY: Deploy zero-trust security architecture with 100% MFA and achieve SOC 2 Type 2 certification by Q4
AI ACCELERATION

Lead the industry in AI-powered business insights

  • NLP: Launch natural language query capabilities allowing users to ask business questions in plain English with 85% accuracy
  • INSIGHTS: Develop AI-driven automated anomaly detection identifying key business issues with 92% precision rate
  • FORECASTING: Implement predictive analytics engine for 5 key business metrics with mean absolute error under 12%
  • ADOPTION: Achieve 65% weekly active usage of AI features across platform with 72% user satisfaction rating
TALENT MAGNET

Build world-class engineering organization

  • HIRING: Recruit 45 specialized engineers including 18 ML/AI experts, reducing vacancy rate from 22% to under 8%
  • RETENTION: Improve engineering retention to 92% through enhanced career paths and competitive compensation adjustments
  • UPSKILLING: Enable 150 engineers to complete AI certification program with 85% applying new skills to production systems
  • DIVERSITY: Increase engineering diversity with underrepresented groups comprising 35% of new hires and 28% of leadership
VELOCITY BOOST

Deliver customer value faster and more reliably

  • RELEASE: Reduce release cycle time from 6-8 weeks to 2 weeks with zero critical post-release incidents for 6 consecutive releases
  • AUTOMATION: Increase automated testing from 62% to 90% coverage, reducing manual QA effort by 65% and defects by 40%
  • PERFORMANCE: Improve platform response time by 45% and query performance by 60% for large datasets exceeding 100M records
  • INTEGRATION: Expand connector ecosystem to 1200+ pre-built integrations with avg implementation time under 1 hour per connection
METRICS
  • Annual recurring revenue growth: 20% by end of 2025, 25% by end of 2026
  • Engineering velocity: Release frequency increased from monthly to bi-weekly with 99.9% deployment success rate
  • AI feature adoption: 65% of customers actively using AI-powered features with 72% satisfaction rating
VALUES
  • Put customers first
  • Move fast, think big
  • Promote transparency and trust
  • Expect excellence
  • Be curious and learn constantly
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Align the learnings

Domo Engineering Retrospective

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To empower organizations with real-time data insights by creating the operating system for business that connects everyone and everything

What Went Well

  • RETENTION: Customer retention improved to 92% driven by enhanced customer success initiatives and product reliability improvements
  • ENTERPRISE: Enterprise deal size increased 18% year-over-year with 7 new deals exceeding $1M in annual contract value
  • PRODUCT: Successfully launched 4 major platform enhancements with 72% user adoption within 90 days of release
  • PARTNERSHIPS: Strategic alliance program delivered 22% of new ARR through co-selling initiatives with cloud providers
  • COSTS: Engineering operational efficiency initiatives reduced infrastructure costs by 12% while improving system performance

Not So Well

  • GROWTH: Overall ARR growth of 14% fell below target of 18% due to extended sales cycles and competitive pressures
  • SMB: Small business segment performance declined 7% year-over-year with higher than expected churn rate of 18%
  • INTERNATIONAL: International expansion initiatives underperformed with just 24% of new revenue from outside North America
  • RELEASES: Two major product releases experienced delays averaging 6 weeks due to technical debt and integration challenges
  • MARGINS: Gross margins declined 2.2 percentage points due to increased cloud infrastructure costs and professional services mix

Learnings

  • VELOCITY: Current development methodology creates bottlenecks; transitioning to smaller, focused microservice teams could increase velocity by 35%
  • FEEDBACK: Customer feedback loops too slow; implementing continuous discovery with bi-weekly user testing could reduce rework by 28%
  • TECHNICAL_DEBT: Incremental approach to technical debt insufficient; dedicated modernization sprints needed quarterly
  • PRICING: Value-based pricing model outperformed usage-based model by 32% in enterprise segment; expand this approach
  • ONBOARDING: Engineer onboarding taking 12+ weeks; structured program could reduce to 6 weeks and improve productivity

Action Items

  • ARCHITECTURE: Complete transition to microservices architecture with dedicated modernization team to increase release velocity by 40%
  • DEVOPS: Implement advanced CI/CD pipeline with automated testing increasing test coverage to 85% and reducing defects by 65%
  • AI_ROADMAP: Accelerate AI feature development with dedicated AI Platform team and comprehensive 18-month roadmap
  • SECURITY: Complete zero-trust security implementation and SOC 2 Type 2 compliance to address enterprise security requirements
  • TALENT: Launch aggressive engineering talent acquisition strategy targeting 45 specialized ML engineers in next 6 months
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To empower organizations with real-time data insights by creating the operating system for business that connects everyone and everything

Strengths

  • DATA: Massive proprietary customer data assets across industries providing training data advantage for AI model development
  • INFRASTRUCTURE: Scalable cloud infrastructure capable of supporting compute-intensive AI workloads with 99.9% reliability
  • RESEARCH: Growing AI research team with 12 PhDs from top institutions publishing in major conferences (NeurIPS, ICML)
  • UX: Strong UX design capabilities to make AI features intuitive, reducing adoption barriers with 87% feature discovery rates
  • INTEGRATION: Existing integration framework allowing AI capabilities to be deployed across platform touchpoints seamlessly

Weaknesses

  • EXPERTISE: Insufficient specialized ML engineers with only 8% of engineering team having AI/ML production experience
  • GOVERNANCE: Limited AI governance framework with no formalized ethical guidelines or bias detection systems
  • COMPUTE: Computing infrastructure not optimized for AI training with GPU utilization at only 42% efficiency
  • ALGORITHMS: Heavy reliance on third-party ML libraries without proprietary algorithms creating competitive vulnerability
  • DATA_QUALITY: Inconsistent data labeling standards with 34% of datasets requiring significant cleaning for AI training

Opportunities

  • AUGMENTATION: Implement AI-driven natural language interfaces to democratize data access for non-technical users, expanding TAM by 45%
  • AUTOMATION: Create AI-powered data pipeline automation reducing ETL implementation time by 70% and configuration errors by 85%
  • ANOMALY: Develop advanced anomaly detection leveraging proprietary algorithms for real-time business process monitoring
  • PERSONALIZATION: Deploy recommendation engines creating personalized insights dashboards increasing user engagement by 37%
  • FORECASTING: Build predictive analytics capabilities utilizing customer historical data to deliver forward-looking business insights

Threats

  • HYPERSCALERS: Cloud providers rapidly integrating AI capabilities into their data platforms, commoditizing 42% of differentiated features
  • SPECIALISTS: AI-native startups developing superior specialized solutions for specific verticals with 2.5x feature velocity
  • TRUST: Growing skepticism about AI reliability with 58% of enterprises expressing concerns about black-box AI decision making
  • REGULATION: Emerging AI regulation (EU AI Act, China's algorithms regulations) requiring significant compliance engineering
  • TALENT: Critical AI talent acquisition challenges with 35% longer time-to-fill for ML roles and 42% higher compensation requirements

Key Priorities

  • AI_TALENT: Aggressively expand AI engineering team through strategic hiring, upskilling programs, and potential acquihires
  • FOUNDATION_MODELS: Develop proprietary foundation models optimized for business intelligence use cases with tight integration to platform
  • GOVERNANCE: Implement comprehensive AI governance framework ensuring ethical use, bias minimization, and regulatory compliance
  • DIFFERENTIATION: Create unique AI capabilities focused on business-specific insights generation that hyperscalers cannot easily replicate
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