Unified Data Management That Connects Every System You Run

Unified data management turns fragmented cloud, on-prem, and SaaS sources into one governed, real-time data layer your teams can trust. We design, build, and operate data integration services so analytics, AI, and operations run on the same clean, consistent data.

Stop paying for five disconnected platforms and reports nobody trusts. Run your business on a single version of the truth.

  • Batch, streaming, and CDC pipelines across cloud, on-prem, and SaaS
  • Native connectors for Snowflake, Databricks, BigQuery, Kafka, SAP, Salesforce, and 200+ systems
  • Schema evolution, data contracts, and automated lineage out of the box
  • Governance, RBAC, and audit built into every integration layer
  • Cloud-native, event-driven architecture with sub-second latency where it matters
Book a 30-minute data integration assessment
Batch, streaming & CDC
200+ native connectors
Data contracts & lineage
Governance built in
Sub-second latency
/ Problem

Why Is Your Data Still Trapped in Silos?

Most enterprises run 80 to 400 systems, but their data does not flow between them. Integrations get built ad-hoc by different teams, pipelines break silently, definitions drift across domains, and every new analytics or AI project starts by rebuilding the same plumbing. The cost is slow decisions, duplicated work, and AI models trained on broken data.

Point-to-point integrations nobody owns
CRM, ERP, billing, and data warehouse connected end-to-end with no clear owner.
Overnight legacy ETL
Batch jobs running while the business needs real-time data integration and management.
Multiple sources of truth
Customer, product, and transaction data defined differently across departments.
Stalled cloud migrations
On-prem systems that cannot be reliably connected to cloud data integration solutions.
Engineers stuck on maintenance
Data engineers spending 60 to 70% of their time maintaining pipelines instead of delivering new value.
Blocked analytics and AI
Teams held up by data quality, missing fields, and undocumented schema changes.
/ What We Deliver

Reference Architecture for Cloud and Data Integration

Ingestion layer
Storage & compute
Transformation
Orchestration
Governance
Observability
Delivery
Ingestion layer

Batch, streaming, and CDC connectors for 200+ systems, including SAP, Oracle, Salesforce, Workday, Kafka, and file-based sources.

Storage & compute

Lakehouse on Snowflake, Databricks, or BigQuery with medallion architecture across bronze, silver, and gold layers.

Transformation

dbt for SQL transformations and Spark or Flink for streaming, all version-controlled in Git.

Orchestration

Airflow or Dagster with full lineage, retries, SLAs, and dependency-aware scheduling.

Governance

Collibra, Unity Catalog, or Purview for catalog, lineage, RBAC, and data contracts.

Observability

Pipeline-level monitoring, data freshness SLAs, anomaly detection, and cost controls.

Delivery

Reverse ETL, APIs, and event streams that push governed data back into operational systems.

/ How it Works

How We Deliver Unified Data Management in 90 Days

Step 1
Discovery & Data Landscape Assessment

We map your sources, targets, existing pipelines, data owners, and priority use cases. Output: integration inventory, pain-point heatmap, and prioritized roadmap. (1 to 2 weeks)

Step 2
Target Architecture & Data Contracts

We design the reference architecture, select cloud data integration tools, and define data contracts for critical domains. Output: architecture decision record, tool stack, and contract templates. (2 to 3 weeks)

Step 3
Build MVP Integration Platform

We implement the first production pipelines, governance layer, CI/CD, and observability, covering two to three high-value use cases end-to-end. Output: live platform with monitored pipelines and onboarded domain teams. (4 to 6 weeks)

Step 4
Scale & Migrate

We migrate legacy pipelines, onboard new domains, and decommission redundant tools. Output: consolidated platform, reduced license spend, and documented operating model. (8 to 16 weeks)

Step 5
Operate & Optimize

We run the platform or enable your team with SRE practices, cost optimization, and continuous improvement. Output: SLAs met, cost per pipeline declining, new sources onboarded in days. (ongoing)

/ Business Impact

Measurable Impact of Unified Data Management

Global insurer
European retailer

50 to 70% reduction in time to onboard a new data source, from weeks to days.

40 to 60% lower total cost of ownership versus maintaining multiple ETL and iPaaS tools.

30 to 50% faster delivery of analytics and AI use cases on the new platform.

90%+ pipeline reliability with automated monitoring and data contracts.

Sub-second latency for streaming use cases, with under 15-minute freshness for operational analytics.

Single audit trail across every data movement, with GDPR and HIPAA requests answered in hours, not weeks.

/ Who This is For

Who Gets the Most Value From Unified Data Management

Chief Data Officer / Head of Data
Needs one governed platform for all data integration and management, measurable data quality, and a roadmap that survives reorganizations.
CTO / VP Engineering
Wants to consolidate a sprawling stack of ETL and iPaaS tools, reduce maintenance load on engineers, and enable product teams with self-service data.
Head of Data Platform / Data Engineering
Needs a reference architecture, CI/CD, and observability so the team can onboard new sources in days, not quarters, without sacrificing reliability.
Chief Analytics / AI Officer
Needs clean, timely, well-documented data to make analytics and AI models work in production, not just in notebooks.
CIO / CISO
Needs secure, auditable, compliant data movement across cloud and on-prem, with clear ownership and no shadow integrations.
/ Use Cases

A Unified Data Platform, Not Another Point Tool

We deliver full-lifecycle data integration services across every source and target you run: ingestion, transformation, orchestration, quality, and delivery. One team, one reference architecture, one operating model, replacing a patchwork of ETL, iPaaS, and custom scripts.

End-to-end data integration services
Modern cloud data integration tools
Real-time and batch integration
Data quality, contracts, and master data
Governed by design
/ FAQ

Frequently Asked Questions

What is unified data management and how is it different from traditional ETL?

Unified data management is an operating model where ingestion, transformation, governance, quality, and delivery run on one integrated platform instead of separate tools. Traditional ETL focuses only on moving data. Unified data management adds data contracts, lineage, master data, observability, and self-service, so the platform scales across domains without breaking.

Do you replace our existing cloud data integration tools or work with them?

We work with them first. Most clients already have Fivetran, Airflow, dbt, Informatica, or similar tools in place. We assess what works, consolidate what overlaps, and replace only what is clearly underperforming or redundant. The goal is a coherent platform, not a vendor rip-and-replace.

How long until we see value from a data integration services engagement?

Typically 6 to 8 weeks to first production use case. Our 90-day delivery model gets an MVP integration platform live with two to three high-value use cases, full governance, and observability. Scaling to additional domains runs in parallel over the following quarters.

Can you handle both cloud and on-prem sources in cloud data integration solutions?

Yes. Our reference architecture is hybrid by default. We use secure agents, private connectivity (VPN, PrivateLink, ExpressRoute), and CDC tooling to integrate on-prem systems like SAP, Oracle, and mainframes with cloud warehouses and lakehouses, without forcing a full migration first.

How do you ensure data quality across data management and integration pipelines?

Through data contracts, automated tests, and observability. Every critical dataset has a schema contract enforced in CI/CD, freshness and volume SLAs monitored in production, and anomaly detection on key metrics. Failures alert the data owner, not a central team, so quality stays close to the source.

What does cloud and data integration cost at enterprise scale?

It depends on data volume, source complexity, and latency requirements, but most clients reduce total integration cost by 40 to 60% compared to maintaining multiple legacy ETL and iPaaS tools. We provide a cost model during discovery so you see platform, license, and run-cost projections before committing.

Who owns the platform after go-live, your team or ours?

Your choice. We offer three models: fully managed (we run it), co-managed (shared SRE), or enablement (we build, your team runs). Most clients start co-managed and move to enablement within 12 months, once their team is trained on the reference architecture.

Ready to Unify Your Data?

Book a 30-minute, no-obligation data integration assessment. We map your current landscape, identify the top three consolidation opportunities, and give you a costed roadmap for unified data management, whether you engage us or not.

Book a call
FIRST STEP

Discovery call

We map your current landscape and the systems you most need connected.

SECOND STEP

Consolidation review

We identify the top three opportunities to consolidate tools and cut integration spend.

THIRD STEP

Costed roadmap

You get a costed roadmap for unified data management, whether you engage us or not.