BI Integration - Product Dashboards and Alerts

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💡 18+. Material for platforms/operators, studios (RGS/live), payments and analytical teams. Not a call to play. By platform we mean PAM/wallet/cashier/bonuses/RG.

1) Why product BI in iGaming

Data solutions: prioritization of content, ad places, bonuses and payment routing.

Operational control: SLA live games, box office, webhooks, JP/tournaments.

RG/compliance: brake lights and out-of-the-box reporting.

A single metric language: from CEO to desk operator - one definition.

2) Integration architecture: from events to panel


OLTP/Events (Kafka, Webhooks, CDC)
│
├─Lakehouse Bronze (raw, append-only)
├─Silver (clean, dedup, SCD2, masking PII)
└─Gold (March-facts and measurements) ──BI semantic layer (LookML/dbt metrics/semantic models)
BI └─Dashbordy/Alerty/Embedded

Lakehouse formats: Delta/Iceberg/Hudi; Parquet files, compressing "small."

Semantic layer: unified definitions of metrics (LookML, dbt Metrics, MetricFlow).

Update channels:

Real-time (stream) - live SLA, box office, webhooks, alerts.
Microbatchi (5-15 min) - bets/settlement, bonuses, JP.
T + 1 - PSP/bank/chargeback reports.

3) Gold Standard Cases and Metrics Dictionary

Actual tables (minimum set)

'fact _ bets' - bet/settlement (stake, win, RTP, in_bonus, provider).

'fact _ wallet _ entries' - debits/credits (reason, reference_id, latency).

'fact _ payments' - deposits/outputs/returns (method, PSP, success, cost).

'fact _ bonus _ wager '- issue, progress, burn.

'fact _ live _ sla '- latency/table/show errors.

'fact _ jackpot '- contributions/triggers/payments.

Measurements

'dim _ player '(pseudo-ID, channels, geo, RG statuses without PII),' dim _ game ',' dim _ provider ',' dim _ psp ',' dim _ brand ',' dim _ region ',' dim _ date '.

KPI-card (reference)

Monetization: GGR/NGR, deposit-conversion, ARPU/ARPDAU, RTP by game/provider.

Payments: success-rate by PSP/geo, p95 'authorize/capture', cost-per-success, refund/chargeback rate.

Operations: webhook-lag, queue/consumer lag, settle lag, error-rate by code.

Live games: uptime, fps/latency, table failures, fullness.

Marketing: cohort retention/LTV, ROI by campaign, promotional codes, cuts by channel/geo.

RG/AML: share of blocked bets, reality-check coverage, velocity-response.

Jackpot/Tournaments: contribution-rate, time-to-drop, prize distribution.

4) Product dashboards (references)

A. "Platform health" (NOC/hourly)

SLO card: p95 authorizations, settle-lag, webhook-lag, error-rate (http/business).

Top degradation by region/brand/provider/PSP.

Triggers: breach SLO, growth'IDEMPOTENCY _ MISMATCH ', DLQ> 0.

B. "Money and Payments"

Deposit funnel: intent→auth→3 - DS→capture→credit, conversion by PSP/geo/method.

Transaction cost and 'cost _ per _ success'.

Reconciliation KPI: `match/timing/missing/amount_mismatch`.

C. "Content and RTP"

GGR/RTP by game/provider/script, heatmap by device/geo/clock.

Hit rate, session length, bonus phases/burnouts.

D. Marketing and Bonuses

Cohorts 1/7/30, vager progress, break-even promo, traffic channels.

A/B experiments (metric guardrails and effect).

E. RG/Compliance

Self-exclusions/limits, reality-checks, velocity-flags, sledge-matches.

Turnkey control panels with export (PII-safe).

5) Alerts: How to make useful (not noise)

Types

SLO alerts: exceeding p95 latency/lag, error-rate, webhook delivery.

Business alerts: drawdown deposit success, surge in 3-DS/AVS failures, provider/table in degradation, RTP outlier.

Data/SLA downloads: delay in window updates, growth in the share of 'mismatch' on reconciliations, watermark violations.

Rules and hygiene

Guardrails: at least 2 indicators per incident (for example, latency + error-rate).

Mailings: Slack/Teams, e-mail, PagerDuty; without "all-to-all."

Deadup/suppression: grouping by root of the problem (PSP/region).

Runbook: link to playbook/dashboard part, owner and SLO target.

Auto-silence: for planned activities/cut-off (banks).

6) Real-time vs batch: when what

Use	Updating	Why
SLA live/box office/webhooks	Stream (seconds)	Here and Now Operational Solutions
Product/Marketing Panels	5-15 min	Balance of freshness and cost
Finance/Regulatory	T+1	Include external reports/reconciliations

Antipattern: "all realtime." Expensive, noisy, unstable. Use the freshness level for solution value.

7) Embedding BI into a product (Embedded)

Approaches: iFrame/URL signed embedding, JS-SDK, API-visas.

Access control: row-level security (brand/region/player_scope), JWT-claims, partial camouflage of fields.

UX patterns: mini-widgets KPI, "drill-through" in the part, buttons "create an incident ticket."

Caching/quotas: result-cache, prepared extracts for heavy storefronts.

8) Security and privacy

PII isolation: individual circuits/buckets; in BI - pseudo-ID, hashes/tokens.

Residency: banning cross-region readings; segmentation per brand/region.

RBAC/ABAC: roles (exec/ops/finance/support/marketing), OPA policies.

Audit (WORM): metric/dashboard changes, data exports, accesses.

Secrets/Keys: KMS/Vault, SSO/OIDC + MFA.

9) Data quality and reliability for BI

Data Contracts: schemas, required fields, semantics of metrics.

DQ tests: key uniqueness, referential integrity, ranges, wallet balance.

Watermarks: late windows and incremental recalculations.

Linage/catalog: who is the owner, SLA freshness, window dependencies.

Cost monitoring: requests/scan bytes, "hot" windows - in DWH, cold - in Lake.

10) CI/CD for dashboards and metrics

Git-as-source: dashboards/explorers/metrics in the repository (LookML/dbt/Superset YAML).

Preview/review: sandboxes/preview environments, visual screen tests.

Compatibility control: schema/metric breaking-changes tests.

Catalogue of releases: versions, changelog, Deprecation/Sunset for metrics.

11) SLO/SLI for BI

Freshness: Gold displays on time (for example, p95 ≤ 15 min; T + 1 reports ≤ 09:00 region).

Availability - ≥ 99 BI Console 9%, embedded widgets ≥ 99. 95%.

Performance: p95 render time of key panels ≤ 2-5 s.

Data Quality: DQ errors of class' ERROR '= 0;' WARN '≤ threshold.

Alert Quality: precision/recall alerts (≥ 0. 7/0. 8 as a benchmark).

12) Checklists

Platform/Data

Gold storefronts for money/payments/content/RG/transactions.
Semantic layer with a single GGR/NGR/retention/PCI-safe metric.
Stream for SLA/cash register; microbatches for bets/bonuses; T + 1 for PSP.
DQ tests, watermarks and reprocess; linage and catalogue with SLA.
RBAC/ABAC + PII isolation and residency.
Reconciliation of panels and mismatch alerts.
CI/CD dashboards, review of metric changes.

Product/Operations

NOC panel with SLO and "one click in part."
Payment funnel and cost-per-success by PSP/geo.
Live-SLA monitoring and degradation alerts.
RG/AML control panels with regr export.
Embedded widgets in admin/CRM, cache and quotas.

13) Red flags (anti-patterns)

BI hits OLTP directly; no Lakehouse/Gold.

Different teams consider GGR/NGR differently; no semantic layer.

Showcases without watermarks and deduplication → double transactions.

Real time "everywhere," although T + 1 solutions.

Absence of RBAC/PII isolation; cross-regional readings.

Dashboards in manual, without versioning/review.

Noisy alerts without guardrails, "alert fatigue."

14) The bottom line

Integration with BI is not just about beautiful graphics. This is a manageable chain: lakehouse showcases and a common vocabulary of metrics, reasonable frequency of updates, strict security and residency, alerts that help to act, not interfere. By building a semantic layer, SLO monitoring and CI/CD dashboards, you turn data into an operational advantage: the product accelerates, costs fall, incidents are detected before complaints, and regulatory reporting is collected without "manual Excel."