Data & Analytics at eCorpIT is the design, engineering, deployment, and ongoing operation of data infrastructure — pipelines, warehouses, lakehouses, BI tooling, predictive models, and AI-driven insight surfaces — for businesses that need their data to drive decisions rather than just accumulate in tables.
Data & Analytics Services
Data engineering, BI dashboards, and predictive analytics.
eCorpIT designs and builds data engineering pipelines, BI dashboards, predictive models, real-time analytics, and AI-driven insight platforms — turning raw data into reports your team actually opens and decisions your business actually acts on. CMMI Level 5 process discipline.
- CMMI Level 5
- AWS · Microsoft · Google Partner
- Real-time on Snowflake, Databricks, BigQuery
- AI-driven analytics integration
- NDA before any technical discussion
What Data & Analytics means at eCorpIT
Data infrastructure, BI, and predictions — built to drive decisions, not accumulate in tables.
Four sub-disciplines compose the practice
Most engagements draw on at least two. Common patterns:
- Pipelines BI Dashboards Source-to-dashboard with semantic governance
- Predictive Models ML Ops Forecasts that stay reliable in production
- Real-time Streaming Governance Sub-second analytics with audit-ready lineage
What we build
Four data sub-disciplines. Open one. See exactly what we ship.
Most data engagements draw on at least two — a BI dashboard always needs data engineering underneath; a predictive use case needs both engineering and ongoing model operations.
Sub-discipline 1 of 4
Data Engineering & ETL/ELT
Data Engineering at eCorpIT covers data pipelines, ETL and ELT processes, data lakes and lakehouses, and the warehouse architecture that turns scattered source systems into queryable, governed data assets.
What we deliver
- ETL and ELT pipeline development — source-system connectors, transformation logic, scheduling, monitoring. Fivetran, Airbyte, custom pipelines.
- Data warehouse architecture — Snowflake, BigQuery, Redshift, Databricks, Synapse. Dimensional modeling, star schemas, slowly changing dimensions.
- Data lakes and lakehouses — Delta Lake, Apache Iceberg, Apache Hudi. Bronze/silver/gold medallion architectures.
- Orchestration — Apache Airflow, Dagster, Prefect, dbt Cloud, AWS Step Functions, Azure Data Factory.
- Reverse ETL — push warehouse data into operational tools (CRMs, ad platforms, customer support) via Hightouch, Census, or custom connectors.
- Data quality and testing — Great Expectations, dbt tests, Monte Carlo data observability, freshness and volume monitoring.
Tech stack (17)
- Apache Airflow
- Dagster
- Prefect
- dbt
- Fivetran
- Airbyte
- Snowflake
- BigQuery
- Databricks
- Redshift
- Synapse
- Delta Lake
- Apache Iceberg
- Hightouch
- Census
- Great Expectations
- Monte Carlo
Use cases we've shipped
Editorial analytics pipeline for Global Banking and Finance Review — content performance, audience segmentation, and editorial workflow data on a cloud-deployed data architecture. Greenfield Snowflake and BigQuery warehouse builds for enterprise modernization.
Sub-discipline 2 of 4
BI & Dashboards
BI & Dashboards at eCorpIT covers business intelligence design and implementation — turning warehouse data into dashboards executives actually use to run the business, not vanity dashboards that nobody opens twice.
What we deliver
- Power BI implementation (Microsoft Partner) — end-to-end rollouts: semantic modeling, DAX, row-level security, embedded analytics, premium capacity sizing.
- Tableau implementation — Server and Cloud deployments, dashboard design, calculated fields, performance tuning.
- Looker implementation — LookML development, explores, dashboards, embedded analytics.
- Open-source BI (Metabase, Apache Superset, Redash) — for clients without Power BI / Tableau / Looker budget. Open-source BI with custom theming.
- Executive and operational dashboards — board decks, daily ops dashboards, customer-facing analytics, KPI scorecards.
- Self-service BI enablement — semantic layers, business glossaries, training, governance frameworks so business users can ask their own questions.
Tech stack (13)
- Power BI
- Tableau
- Looker
- Metabase
- Apache Superset
- Redash
- Sigma
- Mode
- Hex
- Streamlit
- dbt Semantic Layer
- Cube
- MetricFlow
Use cases we've shipped
Power BI enterprise rollouts for clients on Microsoft estates. Executive dashboards for energy and infrastructure clients. Embedded customer-facing analytics for SaaS platforms. Open-source BI on Metabase/Superset for budget-constrained early-stage clients.
Sub-discipline 3 of 4
Predictive Analytics & ML Models
Predictive Analytics at eCorpIT covers forecasting, classification, recommendation, anomaly detection, churn prediction, and the model operations infrastructure that keeps predictions reliable in production.
What we deliver
- Forecasting — sales, demand, traffic, inventory, financial forecasting. Time-series with Prophet, NeuralProphet, statsforecast, custom deep-learning architectures.
- Customer analytics — LTV modeling, churn prediction, propensity to convert, segmentation, recommendation engines.
- Anomaly detection — real-time anomaly detection for fraud, security, operations, and revenue monitoring.
- Recommendation systems — collaborative filtering, content-based, hybrid, and LLM-augmented recommendation engines.
- Model operations (ML Ops) — MLflow, Weights & Biases, Arize AI, Evidently AI. Deployment, monitoring, drift detection, retraining triggers.
- AI-driven analytics interfaces — natural-language analytics (ask-your-data with LLMs), automated insight generation, narrative analytics.
Tech stack (16)
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- LightGBM
- Prophet
- NeuralProphet
- MLflow
- Weights & Biases
- Arize AI
- Evidently AI
- AWS SageMaker
- Azure ML
- GCP Vertex AI
- Hugging Face
- Databricks ML
Use cases we've shipped
Customer LTV and churn prediction for D2C clients. Revenue forecasting models for retail. Editorial content recommendation for media. Real-time fraud and anomaly detection for finance clients. AI-augmented BI (ask-your-data with LLMs) on existing warehouses.
Sub-discipline 4 of 4
Data Governance & Real-Time Analytics
Data Governance & Real-Time Analytics at eCorpIT covers the policy, quality, and lineage discipline that makes data trustworthy — plus the streaming and event-driven analytics infrastructure for use cases that can't wait for batch jobs.
What we deliver
- Data governance frameworks — classification, ownership, stewardship, glossaries, lineage tracking, access controls. Aligned to ISO 8000, DAMA-DMBOK.
- Data quality and observability — Great Expectations, dbt tests, Monte Carlo, Acceldata, Soda. Schema drift detection, freshness SLAs, anomaly alerts.
- Master data management (MDM) — customer, product, supplier, employee master data. Deduplication, golden record creation, cross-system sync.
- Real-time streaming analytics — Apache Kafka, Apache Flink, Confluent Cloud, AWS Kinesis, Azure Event Hubs, ClickHouse for sub-second analytics.
- Customer data platforms (CDP) — Segment, mParticle, RudderStack, or custom CDP builds for clients with specific requirements.
- Compliance-aligned data architecture — GDPR, India DPDP, CCPA. Data mapping, retention, deletion, consent management.
Tech stack (14)
- Apache Kafka
- Confluent Cloud
- Apache Flink
- ClickHouse
- AWS Kinesis
- Azure Event Hubs
- Segment
- mParticle
- RudderStack
- Atlan
- Collibra
- Alation
- Monte Carlo
- Great Expectations
Use cases we've shipped
Real-time fraud and anomaly detection on Kafka + ClickHouse. GDPR and India DPDP data-mapping programs for regulated clients. Master data management for cross-system customer reconciliation. CDP rollouts on Segment or RudderStack for D2C and SaaS clients.
Full tech stack
The warehouses, pipelines, and BI tools we ship to production.
Warehouses & lakehouses
- Snowflake
- BigQuery
- Databricks
- Redshift
- Synapse
- Delta Lake
- Apache Iceberg
- Apache Hudi
Pipelines & orchestration
- Apache Airflow
- Dagster
- Prefect
- dbt
- Fivetran
- Airbyte
- Stitch
- AWS Glue
- Azure Data Factory
- GCP Dataflow
BI & visualization
- Power BI
- Tableau
- Looker
- Metabase
- Apache Superset
- Redash
- Sigma
- Mode
- Hex
- Streamlit
Streaming
- Apache Kafka
- Confluent
- Apache Flink
- Apache Spark Streaming
- ClickHouse
- Kinesis
- Event Hubs
- Pub/Sub
ML & predictive
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- MLflow
- Weights & Biases
- SageMaker
- Vertex AI
- Azure ML
- Databricks ML
- Hugging Face
Governance & quality
- Atlan
- Collibra
- Alation
- Monte Carlo
- Great Expectations
- Acceldata
- Soda
- dbt tests
CDPs & reverse-ETL
- Segment
- mParticle
- RudderStack
- Hightouch
- Census
How we deliver data engagements
A 5-step framework refined across every data project.
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Week 1
Discovery & Data Assessment
Free 30-minute call. Within 5 working days, you receive a one-page data strategy doc, source-system inventory, target architecture sketch, and a delivery roadmap. We audit your data — volume, quality, sensitivity, accessibility. Many data projects die here, and that's the right outcome when the underlying data doesn't support the use case.
- 30-min call
- Source inventory
- Strategy doc
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Weeks 1–3
Architecture & Modeling
Target warehouse, lakehouse, or lake architecture. Dimensional modeling for analytical use cases, normalized modeling for operational use cases. dbt project structure, naming conventions, semantic layer design, governance baseline.
- Dimensional models
- dbt structure
- Semantic layer
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Weeks 3–N
Build (Two-Week Sprints)
Pipelines built, tested, and deployed in increments. Every sprint ends with at least one usable dashboard or queryable data product on staging. dbt tests and data quality checks run on every pipeline.
- Two-week sprints
- Per-sprint dashboard
- Quality checks
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Pre-handover
Validation & Adoption
End-user training. Dashboard handover. Self-service enablement. Documentation that survives the engagement ending. We don't ship dashboards nobody opens — adoption is part of the delivery.
- End-user training
- Self-service enablement
- Adoption metrics
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Launch → ongoing
Operate & Optimize (Data Ops)
Transition into managed data services or hand off to your in-house team. Drift monitoring, freshness SLAs, cost optimization. Quarterly data-model reviews to catch the slow decay of analytical truth.
- Drift monitoring
- Freshness SLAs
- Quarterly model reviews
How to engage us
Six Engagement Models. Match the model to the work.
Staff augmentation is intentionally not on this list. Every engagement below is an outcome we take responsibility for, not a developer rented by the hour.
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Best for · First engagement · AI/concept validation
Discovery & Prototype Sprint
A short, fixed-fee sprint that de-risks a bigger decision. We deliver a working prototype, a clean technical architecture, and a costed roadmap — so you can commit to a larger build with evidence, not speculation. The most common entry point before a Fixed-Scope, Pod, or Partnership engagement.
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Best for · Clearly defined initiatives
Fixed-Scope Project
Software builds, AI implementations, infrastructure migrations, security audits. One quote, one deadline, one delivery.
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Best for · Ongoing product development
Dedicated Product Pod
Monthly retainer. A senior pod operates as your engagement team — same Slack, same standups, same OKRs.
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Best for · Category-defining products
Long-Term Product Partnership
Multi-quarter or multi-year roadmap. eCorpIT becomes your engineering partner organization.
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Best for · 24×7 operations
Managed Services
Cloud, security SOC, network NOC, data center, application support. Predictable monthly retainer; SLA-backed.
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Best for · Early-stage founders
Fractional CTO + Engineering Pod
Senior strategic and engineering leadership on a monthly retainer. For founders without a technical co-founder.
Real data work
Data systems that ship. Dashboards people actually open.
Where data shows up across our public client base — across finance media, sports governance, energy/infrastructure, and healthcare.
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Finance media · Live
Global Banking & Finance Review
Editorial analytics infrastructure on the Next.js + Sanity platform — content performance dashboards, audience segmentation, SEO data integration, and editorial workflow analytics.
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Sports governance · Live
Indian Golf Union — Handicap data
Digital handicap and scoring data infrastructure. SaaS analytics serving multiple clubs — member statistics, tournament performance data, and admin reporting.
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Energy & infrastructure · Live
Multi Solar · MTBPL · Multi Infrastructure
Operational and financial analytics for energy and infrastructure businesses — ERP integration, BI dashboards, executive reporting, and consolidated KPI scorecards.
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Healthtech · Live
Healthcare patient & doctor platform
Clinical analytics for appointment patterns, patient outcomes tracking, prescription analytics, and operational reporting on the patient/doctor digital health platform.
Reference patterns we ship across industries
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Customer LTV & churn
Prediction models for D2C clients.
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Real-time fraud detection
Anomaly detection for finance clients.
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Revenue forecasting
Sales + demand models for retail.
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Editorial recommendation
Content recommendation for media.
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Ask-your-data LLM analytics
AI-augmented BI on existing warehouses.
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Snowflake / BigQuery builds
Greenfield warehouse implementations.
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Power BI enterprise rollouts
For clients on Microsoft estates.
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DPDP / GDPR data programs
Mapping, retention, consent management.
Industries we serve
Deep Experience Across Business Verticals
We map our practice mix to the operating reality of each sector. Hover any card to see how we deliver in that vertical.
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Healthcare
Clinical platforms, telemedicine, patient apps, EHR integration, compliance-aligned design.
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Education
LMS, AI tutors, assessment platforms, school management.
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Manufacturing
IIoT platforms, predictive maintenance, computer-vision QA, MES.
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Sport and Event
Sports governance platforms, scoring and rankings, ticketing, event operations.
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Retail
D2C platforms, omnichannel commerce, AI personalization.
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Consumer & Distribution
Distributor portals, B2B catalogs, route-to-market platforms.
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Media
OTT, finance media, content recommendation, editorial systems.
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Startups & Enterprises
MVP delivery for founders; modernization and AI integration for enterprise teams.
Why eCorpIT for data & analytics
Nine commitments. Dashboards that still tell the truth six months later.
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Cloud-agnostic by default.
AWS Partner, Microsoft Partner, Google Partner — so we pick the warehouse, lakehouse, or lake architecture that fits your workload, not the one that maximizes our partner commission. Snowflake, BigQuery, Databricks, Redshift, Synapse — all viable depending on the use case.
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CMMI Level 5 process discipline applied to data delivery.
Data systems quietly degrade. Schemas drift. Source systems break. Dashboards lie. CMMI Level 5 process controls — drift monitoring, peer review, documented lineage, quarterly model reviews — are why our dashboards still tell the truth six months after launch.
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Data quality engineered in, not discovered when the dashboard goes wrong.
dbt tests on every model. Great Expectations checks on every pipeline. Monte Carlo or Soda for production observability. Freshness SLAs and volume alerts wired to ops channels — so schema drift and source-system breakage get flagged before an executive opens a dashboard and sees nothing.
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AI integration is a default, not an add-on.
Our AI & Machine Learning practice is in-house — so AI-driven analytics (natural-language data interfaces, automated insight generation, LLM-augmented dashboards) is woven into data engagements from day one rather than bolted on by a different vendor six months later.
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Data, models, dashboards, and pipelines are 100% yours.
All warehouse schemas, dbt models, dashboards, ML models, and pipeline code committed to your repository. Deployed into your cloud account. We never hold your data hostage or operate analytics workloads in our account on your behalf.
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Warehouse cost telemetry from day one.
Snowflake credit burn, BigQuery scan costs, Databricks DBU consumption — instrumented and dashboarded before workloads hit production. Query-cost budgets, slow-query alerts, and reserved-capacity right-sizing. The most expensive dashboards we audit cost 100× what they should — we engineer that out, not discover it on the invoice.
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Adoption is part of the delivery.
We don't ship dashboards nobody opens. Self-service enablement, business-user training, semantic-layer governance, and adoption monitoring are part of every BI engagement — because a dashboard with zero weekly active users isn't a delivered project.
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Compliance-aligned data architecture.
GDPR, India DPDP, CCPA, HIPAA, PCI DSS — data mapping, retention, deletion, consent management, encryption at rest and in transit. Designed for the audit before the audit shows up.
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NDA before any technical conversation.
Mutual NDA signed before we receive data samples, schemas, or sensitive business metric definitions.
Transparent data & analytics pricing
Typical engagement ranges. Real numbers, not "contact us" gates.
Final pricing depends on source-system count, data complexity, warehouse choice, and dashboard scope. These are indicative ranges based on our typical engagements. INR pricing and GST invoicing available for India-based clients.
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Data Strategy & Architecture
$5K – $20K
2–6 weeks
Source audit, target architecture, delivery roadmap.
Fixed-scope
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ETL/ELT Pipeline Build (per source)
$2K – $10K
Per source
Source connectors, transformations, scheduling. Volume discounts.
Per source
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Data Warehouse / Lakehouse
$15K – $80K
6–16 weeks
Snowflake, BigQuery, Databricks, Redshift, or Synapse.
Fixed-scope
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Most popular BI Dashboard Project
$5K – $30K
3–10 weeks
Power BI, Tableau, Looker, or open-source. Includes semantic modeling.
Fixed-scope
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Predictive ML Model
$5K – $30K
3–10 weeks
Forecasting, churn, recommendation, or anomaly detection.
Fixed-scope
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Real-Time Streaming Analytics
$10K – $50K
5–14 weeks
Kafka, Flink, ClickHouse architectures.
Fixed-scope
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Data Governance Program
$10K – $40K
Scope-dependent
Lineage, quality, classification, business glossary.
Fixed-scope
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Managed Data Services (Data Ops)
From $2.5K / month
Ongoing
Pipeline ops, dashboard maintenance, model monitoring, governance.
Monthly retainer
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Dedicated Data Pod
Monthly retainer
Ongoing
Data engineer + analytics engineer + BI dev + ML engineer.
Monthly retainer
INR pricing and GST invoicing available. Milestone-based payments standard across all data engagement types. NDA signed before any technical conversation.
Frequently asked questions
Data & analytics, answered.
No matches. Try a different keyword or contact us directly.
What data and analytics services does eCorpIT offer?
eCorpIT delivers data engineering, ETL/ELT pipelines, data warehouse and lakehouse architecture, BI dashboards (Power BI, Tableau, Looker), predictive analytics, ML model deployment and operations, real-time streaming analytics, data governance frameworks, and AI-driven insight platforms.
Which data warehouses does eCorpIT work with?
Snowflake, Google BigQuery, Databricks, Amazon Redshift, Azure Synapse, Delta Lake, Apache Iceberg, and Apache Hudi. We pick the warehouse or lakehouse architecture that fits your workload.
Does eCorpIT build BI dashboards?
Yes. Power BI (Microsoft Partner), Tableau, Looker, Metabase, Apache Superset, Sigma, Mode, and Hex. We deliver semantic modeling, governance, and self-service enablement — not just dashboards.
How much does a BI dashboard project cost?
Typical BI dashboard projects: USD $5,000–$30,000 over 3–10 weeks. Includes semantic modeling and self-service enablement. Standalone dashboards for narrow use cases land at the lower end; enterprise rollouts with governance and embedded analytics at the higher end.
Does eCorpIT do predictive analytics and machine learning?
Yes. Forecasting, customer LTV, churn prediction, recommendation systems, anomaly detection, and fraud detection — plus the ML Ops infrastructure (MLflow, Arize, Evidently) to keep models reliable in production.
Can eCorpIT integrate AI into our existing BI stack?
Yes. AI-driven analytics interfaces (ask-your-data with LLMs), automated insight generation, narrative analytics, and LLM-augmented dashboards on top of existing Power BI, Tableau, Looker, or warehouse deployments.
Does eCorpIT do real-time streaming analytics?
Yes. Apache Kafka, Confluent Cloud, Apache Flink, ClickHouse, AWS Kinesis, Azure Event Hubs — for use cases that need sub-second analytics rather than batch-job-driven reporting.
Does eCorpIT handle data governance and quality?
Yes. Data classification, ownership, lineage tracking, glossary management, data quality monitoring (Great Expectations, Monte Carlo, Soda), and master data management.
Can eCorpIT deliver compliance-aligned data architecture?
Yes. GDPR, India DPDP, CCPA, HIPAA, PCI DSS — data mapping, retention, deletion, consent management, encryption at rest and in transit. Compliance built in, not retrofitted.
Will the data warehouse, dashboards, and pipelines belong to us?
Yes. Warehouse schemas, dbt models, dashboards, pipelines, and ML models are committed to your repository and deployed into your cloud account. You can switch vendors at any time.
Is eCorpIT CMMI-certified for data delivery?
Yes. eCorpIT is CMMI Level 5 (Maturity Level 5). Every data engagement runs under CMMI Level 5 process controls — drift monitoring, peer review, documented lineage, quarterly model reviews.
How do I start a data project with eCorpIT?
Fill the project estimate form on this page or book a 30-minute discovery call. Within 24 working hours, a senior data architect responds with recommended architecture, indicative pricing range, and delivery timeline.
Drowning in data? Let's price the data stack you actually need.
Free 24-hour data estimate from a senior eCorpIT data architect. Warehouse build, BI rollout, predictive model, real-time pipeline, or full data transformation. You walk away with a recommended architecture, indicative cost, and realistic timeline.