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Modern enterprises are not failing because of bad data.
They are failing because decisions still wait for dashboards and humans.

This meetup examines the structural cracks—technical and business—that are quietly limiting how fast and safely organizations can act.

Pain Points

  1. Deterministic data pipelines end in human interpretation. Even with accurate numbers, decisions become slow, biased, non-reproducible, and impossible to replay or audit.
  2. Batch-oriented analytics stacks cannot keep up with shrinking decision windows. Faster refresh rates hide architectural latency instead of removing it.
  3. Decision logic is fragmented across SQL, dashboards, application code, and people. There is no single executable, versioned, or observable decision path.
  4. Decisions are delayed by reviews and meetings, turning data-driven organizations into reactionary systems that act only after value is already lost.
  5. Accountability is diffused. When outcomes fail, organizations cannot clearly trace who decided, why it was decided, or which alternative was ignored.

What will be Dealt in this Meetup

  1. Why the BI → dashboard → human review pipeline is an architectural anti-pattern in low-latency, high-autonomy systems.
  2. How decision latency is unintentionally introduced by batch semantics, snapshot queries, and pull-based analytics interfaces.
  3. Where decision logic actually lives today—SQL CTEs, LookML/YAML, alert configs, application code, and tribal knowledge—and why this makes decisions non-replayable.
  4. Why embedding LLMs directly into analytics workflows breaks determinism, reproducibility, and numeric trust, even when the data layer is correct.
  5. How “human-in-the-loop” becomes a single point of failure once systems cross real-time operational thresholds.
  6. Why observability stops at pipelines and infrastructure, but never reaches decision execution paths.

What You will Learn

  1. How to think about decisions as executable system artifacts, not emergent behavior from dashboards and meetings.
  2. How to distinguish data correctness, metric correctness, and decision correctness—and why most stacks only solve the first.
  3. How modern enterprises are unintentionally running non-deterministic decision systems on deterministic data platforms.
  4. How to evaluate analytics and AI platforms using decision latency, replayability, and auditability instead of visualization depth.
  5. How to reason about agentic systems without surrendering numeric authority to LLMs.
  6. How to identify where your current stack leaks latency, ownership, and accountability—before scaling makes it irreversible.

Tech Stack

Azure Data warehouses, streaming pipelines, BI tools, semantic layers, alerting systems, LLM copilots, and human approvals stitched together, optimized for reporting accuracy, not timely, auditable decisions.

Who Should Attend

Architects, data leaders, product builders, founders, and anyone responsible for decisions made through dashboards, reports, or KPI reviews.

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