Big data insights

1) What is insight and why it matters

• data → domain context → correct methods → validated interpretation → implementation into product/process.

• Reducing uncertainty and reaction time.
• Optimization of funnels and costs, increasing LTV/ARPPU/retention (for any industry).
• Early detection of risks, fraud, degradation.
• New sources of income (data products, APIs, reporting services).

2) Architectural contour: data path to insights

1. Sources: application events, logs, transactions, external APIs, partner data, open sets.
2. Engineering and streaming: CDC/ETL/ELT, queues (Kafka/Kinesis/PubSub), schemes and contract tests.
3. Storage: Data Lake (raw and cleaned areas) + DWH/OLAP display cases, HTAP as needed.
4. Semantic layer: uniform definitions of metrics and dimensions, catalog, lineage.
5. Feature platform: reused features, offline/online consistency.
6. Analytics and models: batch/stream calculations, ML/statistics, graphs, NLP, geo, time series.
7. Delivery of insights: dashboards, alerts, recommendations, API, webhooks, built-in analytics.
8. Observability and quality: data tests, freshness/drift monitoring, alerts for anomalies.

Principle: we separate metric/feature calculations from visualization and interfaces - this accelerates evolution.

3) Types of analytics and when to apply them

Descriptive: "what happened?" - aggregates, sections, seasonality, cohort reports.
Diagnostic: "why?" - factor analysis, segmentation, attribution, causal graphs.
Predictive: "what will happen?" - classification/regression, time-series, survival/charge models.
Prescriptive: "what to do?" - optimization, bandits, RL, recommendations, prioritization of actions.

4) Basic methodological blocks

4. 1 Time series: seasonality/trends, Prophet/ARIMA/ETS, regressors (promo/events), hierarchical forcasting, nowcasting.
4. 2 Segmentation: k-means/DBSCAN/HDBSCAN, RFM/behavioral clusters, profiles by channel/geo/device.
4. 3 Anomalies and risk: STL-decomposition + IQR/ESD, isolation forest, robust PCA; scoring fraud.
4. 4 Recommendations: collaborative filtering, matrix factorization, graph embeddings, seq2rec.
4. 5 NLP: topics, entity extraction, sentiment/intent, ticket/recall classification, RAG/LLM assistants.
4. 6 Graph analytics: centrality, community, fraud paths, node influence, network stickiness metrics.
4. 7 Causality: A/B tests, difference-in-differences, propensity score, instrumental variables, DoWhy/causal ML.

5) From data to characteristics: feature engineering

Aggregates by window: moving amounts/averages, frequencies, uniqueness.
Hourly/daily/weekly lags: capture short-term dynamics.
Cohort characteristics: time since X, user/object life cycle.
Geo-traits: location clusters, heat maps, availability.
Graph features: degree, process closure, PageRank, node/edge embeddings.
Textual signs: TF-IDF/embeddings, tonality, toxicity, themes.
Online/offline consistency: one transformation logic for training and production.

6) Experiments and causality

Design: hypothesis → success metric (s) → minimal effect → sample size → randomization/stratification.
Analysis: p-values/confidence interval effect, CUPED, correction of multiple checks.
Quasi-experiments: if RCT is not possible - DiD, synthetic controls, matchings.
Online optimization: multi-armed bandit, UCB/TS, contextual bandits, early stop.
Coding solutions: experiments are integrated into the feature-flag platform, version tracking.

7) Data quality and trust

Schemes and contracts: evolution of schemes, backward compatibility, schema registry.
Data tests: freshness, completeness, uniqueness, integrity, ranges/rules.
Linage and Catalog: Source to Metric; owners, SLAs, validity statuses.
Handling passes/emissions: Policies that are documented and automated.
Insight reproducibility check: the same request → the same result (window/formula versioning).

8) Privacy, security, ethics

PII/PCI/PHI: masking, tokenization, differential privacy, minimization.
RLS/CLS: row/column level access by role/tenant/region.
Audit: who saw/exported what, traces of access, retention policies.
Model ethics: biases and equity, explainability (SHAP), safe application of LLM.
Localization: storage areas and cross-border transfer according to jurisdictional requirements.

9) MLOps and operational analytics

Pipelines: training DAG'i (Airflow/Argo/DBT/Prefect), reaction to new games/stream.
Model releases: register (Model Registry), canary calculations, blue-green.
Monitoring: latency, freshness of features, drift of data/predictions, quality (AUC/MAE/BS).
Rollbacks and runbooks: automatic rollback to the previous version, degradation procedures.
Cost-to-serve: profiling the costs of calculating insights and storing features.

10) Delivery of insights: where and how to show

Adaptive dashboards: priority KPI tape, explanations of metrics, drill-through to events.
Built-in analytics: JS-SDK/iframe/Headless API, context filters, e-mail/PDF snapshots.
Alerts and recommendations: "next action," thresholds, anomalies, SLA violations; snooze/deduplication.
Operational circuit: integration with CRM/ticket systems/orchestrators for auto-actions.
Data products for partners: reporting portals, uploads, API endpoints with quotas and audits.

11) Insight Program Success Metrics

Adoption: share of active analytics/models users (WAU/MAU, frequency).
Impact: uplift of key business KPIs (conversion, retention, fraud risk, COGS).
Insight speed: time from event to available output/alert.
Reliability: uptime, p95 latency of calculations and rendering, share of folbacks.
Trust: complaints about discrepancies, time to resolution, coverage with data tests.
Economics: cost per insight, ROI on initiatives, payback on data products.

12) Monetization of insights

Internal: revenue/savings growth, marketing/inventory/risk management optimization.
External: paid reports/panels, white-label for partners, access to API/showcases.
Tariffs: basic KPIs are free, advanced segments/exports/real-time - Pro/Enterprise.
Data Marketplace: exchange of aggregated sets subject to privacy and rights.

13) Antipatterns

"The data itself will say everything" without hypotheses and domain context.
Jump definitions of metrics in different reports (lack of a semantic layer).
Cumbersome live requests in OLTP, which drop the product.
Oracle models without feedback and business owner.
Alert spam without prioritization, deduplication and explainability.

Lack of experimentation - making decisions on correlations and "intuition."

14) Implementation Roadmap

1. Discovery: solution map (JTBD), critical KPIs, sources, risks and limitations (legal/those).
2. Data and semantics: catalogs, schemas, quality tests, unified KPI definitions.
3. MVP insights: 3-5 sighting cases (for example, demand forecast, anomaly detection, charn scoring), simple delivery (dashboard + alert).
4. Automation: Headless API, integration with operations, experiments, causal analysis.
5. Scaling: feature platform, online/offline consistency, canary releases of models.
6. Monetization and Ecosystem: External Panels/APIs, Tariffs, Affiliate Reports.

15) Pre-release checklist

• KPI glossary and owners approved, formula versions documented.
• Data tests (freshness/completeness/uniqueness/ranges) take place in CI.
• RLS/CLS and sensitive field masking tested in staging.
• p95 calculation and rendering latency complies with SLO; there is cash/bills.
• Alerts are prioritized, there is snooze and deduplication; activity audit is stored.
• Experiments and causal methods are ready to evaluate the effect.
• Runbooks on model/data degradation and automatic rollback are configured.
• Retention/DSAR policies and storage localization agreed with Legal.

16) Examples of typical insights (templates)

Commercial: conversion drivers by segment and channel; price elasticity; demand forecast.
Operating rooms: SLA bottlenecks; Load/capacity forecast anomalies by process steps.
Risk/Fraud: chains of suspicious accounts; bursts of chargeback; evaluation of the source of funds.
Client: outflow probabilities; NBO/recommendations; segments by motive/behavior.
Product quality: reasons for the fall in NPS/CSAT; topics from reviews; post-release regression map.

Bottom line: big data insights are a systems discipline where architecture, methodology and operational execution are combined into a decision-making circuit. Success is measured not by data volume or number of models, but by impact on business metrics, process robustness, and user trust in data.