Accelerated Innovation

Delivering Scalable and Performant Data Services

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Delivering Scalable and Performant Data Services

Description

This capability focuses on designing and operating enterprise data services that can reliably support GenAI workloads at scale. It includes architectural choices, optimization strategies, and service-level practices that ensure data access is fast, consistent, and resilient-even under fluctuating demand.

Why it's Important

As GenAI usage expands across business units, performance expectations and data volumes grow exponentially. Poorly tuned or brittle data services can quickly become bottlenecks, limiting GenAI solution speed, reliability, and adoption. Scalable and performant data services are essential to ensure GenAI solutions respond in real time, integrate seamlessly into workflows, and deliver consistently high-quality user experiences. These capabilities also reduce infrastructure waste, prevent downtime, and help organizations balance performance with cost efficiency.

Why it's Challenging @ Scale

  • Unpredictable GenAI Workloads: Spikes in usage and new applications can overwhelm data systems not designed for elastic scalability.
  • Cross-Domain Performance Variation: Data services may perform inconsistently across regions, clouds, or business units, leading to uneven user experiences.
  • Infrastructure Bottlenecks: Without ongoing optimization, storage, compute, or network constraints can limit responsiveness and availability.
  • Latency Sensitivity of GenAI Models: Even small delays in data delivery can negatively impact GenAI output quality or usability.
  • Difficulty Balancing Performance and Cost: Achieving low-latency performance at scale often comes with rising infrastructure spend unless carefully managed.

Complexity

High: Maturing this capability requires coordinated architecture design, real-time monitoring, automation, and cost-performance optimization across teams and platforms.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Enterprise GenAI Data Readiness workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Explore enterprise GenAI ready data key concepts.
  • Establishing effective data value and quality measures.
  • Exploring discoverability and understandability best practices.
  • Exploring accessibility, observability, and connectibility best practices.
  • Defining your GenAI data readiness roadmap.
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State.
  • Create an actionable enablement plan.
  • Define target timeline and measures of success.
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Pilot performance monitoring tools: Test lightweight observability tools that measure data pipeline latency and throughput.
  • Isolate and optimize critical workflows: Identify and streamline one or two data services that directly impact GenAI output quality.
  • Prototype elastic scaling policies: Deploy simple rules for autoscaling to handle peak GenAI usage scenarios without overprovisioning.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Implementing Value Scoring for In-Scope GenAI Data.
  • Ensuring Your GenAI Data is Trustworthy.
  • Enterprise GenAI Search Best Practices.
  • Enterprise GenAI Data Explorability Best Practices.
  • Enterprise Data Entitlements Management Best Practices.
  • GenAI Data Definition Best Practices.
  • GenAI Metadata Management Best Practices.
  • GenAI Data Ontology Best Practices.
  • GenAI Data Consumer Enablement Best Practices.
  • GenAI Data Accessibility Best Practices.
  • GenAI Data Lineage Best Practices.
  • GenAI Data Auditability Best Practices.
  • GenAI Data Explainability and Transparency Best Practices.
  • GenAI Data Monitoring & Alerting Best Practices.
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Evaluate the scalability of current data pipelines under high concurrency and demand.
  • Define in-scope Processes and Guardrails: Identify which performance SLAs, autoscaling policies, and failover mechanisms must be standardized.
  • Close any Data or Measurement Gaps: Ensure telemetry is in place to track response time, latency, throughput, and error rates.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Sequence your rollout across teams or services based on performance-criticality and readiness.
  • Build Awareness and Finalize Enablers: Ensure monitoring tools, autoscaling configs, documentation, and support models are ready for scale.
  • Operationalize Your Comms Plan: Share clear expectations for SLAs, escalation paths, and resource commitments across stakeholders.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Codify performance engineering standards: Establish enterprise-wide guidelines for GenAI data throughput, latency, and scalability.
  • Create reusable configuration templates: Standardize autoscaling rules, caching strategies, and failover configs to streamline team adoption.
  • Embed observability into pipelines: Ensure monitoring, tracing, and alerting are built into every GenAI data service by default.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand coverage of performance SLAs: Broaden enforcement of data access reliability and speed targets across all GenAI services.
  • Automate performance testing workflows: Integrate load testing and bottleneck detection into CI/CD pipelines.
  • Train distributed teams on optimization levers: Enable teams to manage and tune data workloads using scalable playbooks and tools.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Recognize teams hitting key performance milestones: Highlight those achieving best-in-class data responsiveness for GenAI.
  • Publish case studies on scaling success: Share how performance improvements have accelerated GenAI adoption or unlocked new use cases.
  • Use gamification to reward optimization: Offer visible recognition for teams that reduce latency, increase reliability, or cut costs.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Align GenAI data services with enterprise SOPs: Make performance monitoring and tuning a routine part of daily operations.
  • Simplify scaling controls for teams: Build self-service options for autoscaling, caching, and traffic shaping into platform tools.
  • Integrate SLA tracking into executive dashboards: Provide real-time visibility into GenAI data service performance across business units.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate root cause analysis of slowdowns: Use GenAI to detect and explain anomalies in data performance metrics.
  • Enable real-time scaling based on AI forecasts: Apply predictive models to adjust infrastructure dynamically ahead of load spikes.
  • Continuously test system boundaries: Run background synthetic load testing to proactively identify degradation risks.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Incorporate external benchmarks into planning: Compare your performance against industry leaders to set higher targets.
  • Extend data services to support multimodal GenAI: Tune systems for models that require higher throughput and larger payloads.
  • Invest in AI-driven observability platforms: Improve issue detection and resolution speed using intelligence-driven monitoring tools.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Over-optimizing for peak loads: Designing for extreme edge cases can lead to excessive infrastructure costs and unnecessary complexity.
  • Relying on manual performance tuning: Without automation, scaling and optimization efforts become time-consuming and error-prone.
  • Ignoring cross-platform variability: Performance bottlenecks can emerge when GenAI services run across clouds or infrastructure types with different capabilities.
  • Overlooking observability gaps: Without robust monitoring, subtle issues can accumulate and erode user trust in GenAI responsiveness.
  • Neglecting cost-performance tradeoffs: Pursuing performance at all costs may undermine GenAI’s long-term business case.

Targeted Benefits

While Delivering Scalable and Performant Data Services can be challenging, its benefits are clear and compelling, including:

  • High user satisfaction and engagement: Fast, reliable data services help GenAI solutions feel responsive and intuitive.
  • Increased operational efficiency: Streamlined, automated scaling reduces overhead and minimizes outages.
  • Stronger business case for GenAI investments: Better performance metrics help validate ROI and justify continued funding.
  • Faster time to value: Reliable data access accelerates development, testing, and deployment of new GenAI use cases.
  • Competitive advantage at scale: Consistently performant GenAI solutions can differentiate customer experiences and internal capabilities.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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