Ecosystem Analytics

1) The role of analytics in the network ecosystem

Ecosystem analytics is the end-to-end ability to collect, normalize and interpret signals from all participants (operators, studios/RGS, PSP/APM, KYC/AML, affiliates/media, streamers, SRE, security), turning them into solutions: payment routing, content recommendations, guardrails RG, limits, feature flags, cross-campaigns, capacity planning, and DR..
The goal is a single source of truth, predictable SLO/KPIs, and a fast improvement cycle.

2) Sources, events and ontology

2. 1 Event model (minimum domain)

`click`, `session_start/stop`, `bet/spin`, `round_start/result`, `deposit/withdrawal`, `psp_auth`, `kyc_status`, `fraud_signal`, `reward_granted`, `leaderboard_update`, `stream_interaction`.

2. 2 Identifiers and connectivity

`playerId` (псевдоним), `operatorId`, `providerId`, `contentId`, `campaignId`, `paymentRouteId`, `tableId`, `traceId`.
All IDs are tokenized, PII is stored in safe zones.

2. 3 Ontology and Data Contracts

Schema Registry and domain dictionaries.
Data Contracts: owner, destination, freshness/completeness SLA, metric formulas, allowed values.
Versioning: semver for schemas and formulas.

3) Analytics architecture

3. 1 Flows and storages

Streaming (≤1 -5 s): event bus → materialized views (operational dashboards, SRE, real-time solutions).
Batch (5-15 min/d): CDC/ETL → DWH/Lakehouse (Finance, Reporting, Compliance).
Hot/Warm/Cold layers, S3-compatible archiving, vacuum/retention.

3. 2 Data layers

Raw (unchangeable, cipher, lineage).
Staging (clearing/normalization).
Semantic (stars/noodles, blizzards, metrics).
Feature Store (online/offline characteristics).
Knowledge Graph (entity/relationship graph for recommendations and anti-fraud).

3. 3 Access and security

RBAC + ABAC + ReBAC, mTLS/JWS, tokenization, jurisdictional filters, SoD (separation of duties), WORM audit.

4) Catalog of metrics (canon)

4. 1 Product and growth

CR funnels: login → KYC → deposit → active game.
Retention D1/D7/D30, ARPU/ARPPU, LTV (cumulative/model).
Engagement: sessions/DAU/WAU/MAU, average duration, missions/tournaments.

4. 2 Payments/PSP/APM

Conversion Rate (AWS × region × device), p95 authorization, chargeback risk, route fault tolerance, cut-over time.

4. 3 KYC/AML

Pass-rate and SLA stages, FP/FN, impact on CR deposit, queue manual review.

4. 4 Content/Studios

Sessions/engagement/retention by game, RTP/volatility, live-SLI (e2e-delay, packet loss).

4. 5 Infra/SRE

p95/p99 API, broker lag, uptime integrations, headroom, DR flips, error budget.

4. 6 Finance

GGR/Net Revenue, rake/fee, Cost-to-Serve (per rps/txn/stream/event), credits/penalties (SLO-related).

5) Attribution and experimentation

5. 1 Attribution

Rule: "last optional touch" with windows by jurisdiction, anti-duplicates of postbacks, cross-device stitching by agreed tokens.
Checks: sanity tests, coordination with finance/law.

5. 2 Experiments

A/B/C, stratification (jurisdiction, risk segments, device), guardrails (SLO, RG, compliance).
Single counting platform: effects, confidence intervals, CUPED/CPP to reduce variance.
Feature-flags/Progressive delivery with auto-rollback on error budget.

6) Feature Store и Knowledge Graph

6. 1 Feature Store

Online signs (reaction ≤ 20-50 ms): propensity, risk, payment routines, content tastes.
Offline characteristics (batch/training).
SLA freshness/consistency, drift control, PD leakage tests.

6. 2 Knowledge Graph

Nodes: player, segment, game, provider, APM/PSP, region, campaign, risk event.

Ribs: "played," "deposit through APM," "verified," "campaigner," "anti-fraud pattern worked."

Use-cases: recommendations, look-alike, collusions, implicit dependencies in payments and routes.

7) Federated Analytics, Privacy and Compliance

Federated Learning (FL): training models on these partners without transferring personal data; secure aggregation and differential privacy (DP).
DPA/DPIA: objectives, retention periods, cross-border flows.
PII minimization: tokenization, masking, separate safe zones.
Audit: queries and calculations with WORM logs and traceId.

8) MLOps and BIOps (analytics as a product)

8. 1 MLOps

Model cards (goal, data, metrics, risks), automatic training/dispatch, monitoring drift/latency, Canary/Shadow.
Metrics: AUC/PR, lift, KS, fairness, latency of inference, frequency of retraining.

8. 2 BIOps (panels/displays)

Versioning of formulas/widgets, changelogs, sandboxes and demo data, conformance tests of panels.
SLO panels: freshness of data, p95 renders, availability, share of cache hits.

9) Analytics Economics: Cost-to-Serve and ROI

Cost per rps/txn/stream/event, cost of inference/1000 requests, storage of features and stream aggregations.
Value Map: contribution of models/rules to CR deposits, ARPU/LTV, chargeback and incident reduction.
ROI of experiments: uplift, payback time, impact on SLOs/penalties/credits.
Optimization: caching hot slices, partitioning, pruning columns, adaptive windows.

10) Observability of data and quality

Data-SLO: completeness, freshness, uniqueness, consistency.
Schema-violations/Lineage: alerts at the junction of schemes, visual path of origin.
Reconciliation: reconciliation of aggregates (finance, attribution), control of doubles/losses.
Trace correlation: 'traceId' from event to panels and actions.

11) Change management and versions

Semantic versions of schemes and formulas, add-only migrations, adapters between versions.
Change-windows, auto-rollback, compatibility "flags," deviation-plan with parallel windows.

12) Anti-patterns

Many "truths": different formulas of the same metric in different teams.
Raw PD in BI: no tokenization/masking.
Events without Schema Registry: Storefront and Model Drives.
Experiments without guardrails: rise in incidents/fines.
Retreats without idempotency in pipelines: doubles/offset.
SLO "on paper": No alert/stop buttons.
Lack of lineage: The controversial figure is impossible to prove.
SPOF gateway at data input, no N + 1.

13) Implementation checklists

13. 1 Data and diagrams

• Ontology and dictionaries are approved.
• Schema Registry + Data Contracts (owner, SLA, version).
• PD tokenization/masking, DPIA formalized.

13. 2 Pipelines and quality

• Stream + Batch pipelines, SLAs freshness/completeness.
• Data-tests (including attribution/finance), reconciliation jobs.
• Alerts on drift/violations/bus lag.

13. 3 Metrics and panels

• Metrics catalog with formulas and owners.
• Widget versions, sandbox, conformance set.
• SLO panels (freshness, render, availability).

13. 4 Models and solutions

• Model cards, monitoring, canary/shadow.
• Feature Store (online/offline), drift control.
• Guardrails RG/compliance, stop buttons.

13. 5 Economy

• Cost-to-Serve карта (per rps/txn/event/stream).
• Value Map and ROI process.
• Co-funding/credits/penalties tied to metrics.

14) Maturity Roadmap

v1 (Foundation): events/ontology, Schema Registry, base panels and batch reports, data-tests.
v2 (Integration): stream storefronts, metrics catalog, A/B platform, Feature Store, scorecards partners.
v3 (Automation): predictive SRE/payment/content models, auto-dosing by SLI, BIOps, auto-alerts and auto-rollback.
v4 (Networked Intelligence): federated models (FL/DP), knowledge graph as the core of recommendations and anti-fraud, inter-partner showcases and collaborative solutions.

15) Brief summary

Ecosystem analytics is semantics + threads + solutions. Standardize events and formulas, provide high-quality stream/batch pipelines, maintain a metrics catalog, use the Feature Store and knowledge graph, protect privacy (DP/FL), manage versions and SLOs. Connect everything to the economy (Cost-to-Serve and ROI) - and your network of participants will learn every day and make decisions faster than the market.