Our Process
Practical, Incremental Engagements for Reliable Data Foundations
IDM helps organizations build reliable data foundations in a way that's low-risk, pragmatic, and fully under your control. Our three-phase approach starts with clarity, then builds durable infrastructure, and continues with long-term support — giving you confidence at every step.
Get in TouchIDM's Three-Phase Playbook
IDM's playbook ensures each step strengthens your data foundations, reduces manual work, and prepares your organization for analytics and AI.
Stage 1: Discovery & Readiness Mapping (tiered, hourly billing)
- Inventory your operational systems and data sources.
- Map existing workflows, identify manual processes and bottlenecks.
- Assess analytics and AI readiness.
Outcome: Clarity on your current state, what's missing, and realistic next steps — with no obligation to proceed.
Stage 2: Foundation Build (project based, with defined gates)
- Automate data ingestion and build structured databases or warehouses.
- Develop clean, documented data models and operational dashboards.
- Prepare AI pipelines once core data quality is ensured.
Outcome: Reliable, structured data flows that reduce manual work and enable advanced use cases.
Stage 3: Product Maintenance, Support & Access to Fractional Engineers (subscription, retainer)
- Ongoing, fractional data and AI support to maintain and extend what's been built.
- Clients decide on optimal mix between in-house vs outsource to IDM.
- Typical activities: long-term technical partner, monitoring pipelines, adding new data flows, or help with hiring engineers.
Outcome: A durable data foundation without needing to hire and manage in-house engineers.
Practical Data Improvements
Examples of Early-Phase Work We Deliver
ERP data ingestion
Automated extraction of production, inventory, sales, or purchasing data into a structured environment
Spreadsheet elimination
Replacing manual Excel-based workflows with automated, auditable pipelines, without making in-house applications overly technical
Operational reporting pipelines
Reliable, repeatable reporting for operations, finance, or leadership teams
Data normalization
Resolving inconsistent identifiers, units, and formats across systems
ETL Automation
Triggered workflows and data ingestion routines to reduce repetitive manual tasks
Analytics readiness
Build clean and self-maintaining dataflows to support forecasting, optimization, CMS and AI applications