Data Integration & Engineering Advisory.
Connect your systems, rationalize your data flows, and end the manual reconciliation and pipeline failures that are costing your team time and your organization accuracy.
Data Management Services
The Situation — When Clients Come to Us
Organizations come to us for data integration advisory when:
- Data lives in disconnected systems — ERP, CRM, operational databases, cloud services, external data sources — and must be manually reconciled before it can be used for reporting, operations, or analytics
- A new analytics platform, data warehouse, or cloud environment needs to be integrated with existing source systems — and the integration architecture must be designed before the technical implementation begins
- Existing data pipelines are brittle, undocumented, or performing poorly — creating downstream quality problems, latency issues, and operational risk
- An organization is designing its integration strategy for the first time and needs advisory on ETL/ELT patterns, API integration, real-time vs. batch approaches, and DataOps practices before committing to a technology stack
What We Do — Our Approach
// Phase 1
Integration Landscape Assessment
We inventory all data sources, integration patterns, pipeline architectures, and data flows across the organization’s current environment. We assess integration quality, reliability, documentation, and governance against the organization’s reporting, analytics, and operational requirements.
// Phase 2
Integration Strategy & Architecture Design
We develop the integration strategy — defining the target integration patterns (ETL, ELT, API, event-driven, or data fabric), the transformation and quality logic requirements, the real-time vs. batch approach for each data flow, and the DataOps practices that will govern pipeline development and operations.
// Phase 3
Pipeline Specifications & Design
We design the integration pipelines — source-to-target mappings, transformation logic, data quality controls, error handling, and monitoring requirements — in sufficient technical detail to enable implementation by the client team or technology partner.
// Phase 4
Integration Governance Design
We design the governance model for data integration — data lineage documentation standards, change management processes, data quality monitoring specifications, and the operational runbook for pipeline management.
// Phase 5
Technology Advisory & Implementation Guidance
We provide vendor-neutral evaluation of integration platforms and tooling against the integration architecture requirements. We guide implementation by the client or technology partner, providing design authority review and quality acceptance testing support.
What You Get — Deliverables
- Integration landscape assessment report — inventory, gap analysis, and risk identification
- Data integration strategy document
- Integration architecture design — patterns, flows, and governance
- Pipeline specifications — source-to-target mappings, transformation logic, quality controls, and error handling
- DataOps framework design for pipeline development and operations
- Data lineage documentation standards and framework
- Integration governance model and operational runbook specification
- Vendor-neutral integration platform evaluation and recommendation
- Implementation guidance package for the client or technology partner
Your data integration architecture determines whether your analytics are reliable or your pipelines are a liability.
Every Iknow integration advisory engagement is grounded in your specific systems, data flows, and organizational capacity.
