Short-term (0–3 months) — rapid wins to hit 40% without slowing time-to-insight:
- Technical
- Analyze cost drivers (dashboard usage, live queries, expensive joins). Run a 30-day usage audit to rank reports by query frequency, runtime, and consumer value.
- Convert top-heavy live dashboards to scheduled extracts/materialized views refreshed during off-peak hours (example: move daily exec dashboards from live queries to hourly extracts).
- Introduce query caching at the BI layer and optimize a small set of high-cost SQLs (add indexes, reduce SELECT * , push aggregation to the DB).
- Process
- Enforce a temporary “read vs. write” policy: new dashboards must show expected user and freshness requirements before provisioning live queries.
- Rapid retirement: deprecate zero-usage and low-value reports (communicate 2-week notice).
- Stakeholder trade-offs
- Accept slightly coarser freshness for low-frequency reports (hourly vs. real-time).
- Prioritize mission-critical dashboards for full performance.
Long-term (3–18 months) — sustainable cost model and capability improvements:
- Technical
- Migrate to a hybrid architecture: event-driven ingestion → hot/cold storage; hot data for real-time needs, cold for historical analysis (e.g., Snowflake time-travel + cheaper long-term storage).
- Implement centralized semantic layer (LookML/Power BI certified datasets) to avoid duplicated logic and redundant heavy queries.
- Automate query sampling, cost alerts, and auto-scaling policies; consider spot/commit capacity purchasing for expected workloads.
- Process
- Introduce report lifecycle, RICE-based prioritization, SLAs for freshness and latency, and a formal request/approval flow with cost estimates.
- Chargeback or showback model to make owners aware of costs.
- Regular training and templates to enforce efficient design patterns.
- Stakeholder trade-offs
- Some advanced ad-hoc workloads moved to sandbox environments or scheduled batch exports.
- Investment in tooling and data engineering upfront to lower recurring BI costs.
Communication plan to consumers:
- Share a concise one-page impact matrix: which dashboards change, expected freshness/latency changes, and alternatives.
- Publish a roadmap and timeline with phased deprecations and migration windows; offer weekly office hours and migration support.
- For high-impact changes, run small pilots and collect SLA feedback; measure time-to-insight before/after and report savings and performance metrics monthly.
- Use transparent metrics (costs saved, queries reduced, user satisfaction) to show trade-offs delivered and keep leadership/stakeholders aligned.
Expected outcomes:
- Immediate 30–50% cost reduction potential from extracts, deprecations, and query tuning; sustainable reductions thereafter through architecture and process.
