Varanasi, UP- IN
Data & Analytics
Turn raw data into reliable decisions—fast, governed, and cost-aware.
Data & Analytics
We build end-to-end data platforms that your teams trust: clear event models, dependable pipelines, a warehouse/lakehouse you won’t outgrow, and dashboards tied to real business questions. From product analytics and marketing attribution to finance ops and supply-chain visibility, we design for accuracy, latency, and explainability—so decisions aren’t just faster, they’re safer.
Under the hood: event and batch ingestion, ELT with tests, a metrics layer for shared definitions, and governance that satisfies audits without slowing teams down. We instrument web/app analytics (GA4 with server-side tagging), connect ERP/CRM/payments, and wire observability so freshness, success rates, and costs are visible at a glance. Virtunetic helps produce clear data docs, metric definitions, and narrative summaries; Ektasi Labs adds consented messaging and ERP integrations to activate insights where work happens.
Architecture, Pipelines & Governance
We start by mapping sources and defining a crisp event/Entity model (customers, orders, subscriptions, payouts, inventory). Ingestion uses resilient connectors (stream or batch), then ELT transforms (e.g., dbt) apply tests, dedupe, and SCD logic into a conformed warehouse/lakehouse (BigQuery/Snowflake/Redshift/Databricks, or Postgres where lean fits). We add a metrics layer so “Revenue,” “Active Users,” and “Churn” mean one thing everywhere. Data contracts, schema validation, and lineage keep upstream changes from breaking reports. Governance is pragmatic: PII tagging, role-based access, row-level/column-level security, encryption, retention, and consent capture aligned with India’s DPDP Act and GDPR. Observability tracks freshness, row counts, distribution shifts, and pipeline SLOs; FinOps guardrails watch compute/storage so spend stays predictable.
Analytics, Experimentation & Activation
We design decision-ready dashboards for product, growth, finance, and operations—built on the shared metrics layer, not ad-hoc SQL. Experiment frameworks (A/B, holdouts) quantify lift in CVR, AOV/LTV, and ROAS, while anomaly detection alerts you before KPIs drift. Marketing teams get clean attribution and audiences; RevOps gets revenue recognition and cohort health; Ops gets OTIF, dwell time, and cost-to-serve. Reverse ETL/CDP pushes segments to ads, CRM, email/SMS, and in-app surfaces with consent and frequency control. For ML use cases, we prepare feature stores, baseline models, and monitoring for drift and performance—so pilots graduate to production without mystery.
Frequently Asked Questions
Discovery and a thin-slice data pipeline typically land in 3–4 weeks, delivering a first dashboard with verified metrics and data tests.
Cloud warehouses (BigQuery/Snowflake/Redshift), dbt, Airflow/Cloud schedulers, Kafka/Kinesis for streams, GA4 server-side, Looker/Power BI/Metabase/Superset—plus Postgres for lean stacks.
Yes. We audit sources, repair schemas, add tests and lineage, then refactor toward a conformed model—without breaking current reporting.
Contracted schemas, column tests, outlier checks, reconciliation to source systems, freshness SLOs, and alerts. Fail-closed where appropriate to avoid bad data in decisions.
PII classification, masking, RLS/CLS, consent tracking, and policies aligned with DPDP/GDPR; audit trails show who accessed what and when.
Performance tuning, partitioning/clustering, query caching, job caps, storage lifecycle policies, and FinOps dashboards so teams see cost per query/report.
Yes—clean event design, GA4 with server-side tagging, ecommerce schemas, and multi-touch models that reflect your channels and sales cycle.
We establish a governed feature layer, versioned datasets, and monitoring. Start with pragmatic models (propensity, demand) before scaling complexity.
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