Complete baseline
Create a reliable application and infrastructure baseline that goes beyond pure quantities.
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Discovery
Discovery turns early quantity baselines into a decision-ready view of applications, dependencies, and organizational readiness for realistic migration planning.
Understanding Discovery
Section titled “Understanding Discovery”Discovery is one of the first and most critical modules in the Design and Mobilize phase. It refines Rapid Discovery results and adds the depth needed to make architecture and migration-wave decisions with confidence.
The primary objective is to establish a realistic, evidence-based understanding of the current IT landscape, business priorities, and organizational readiness before detailed target design and migration planning are finalized.
Core Objectives
Section titled “Core Objectives”Dependency transparency
Identify technical and process dependencies to avoid hidden migration blockers.
Business alignment
Link technical findings with business criticality, timelines, and risk tolerance.
Planning readiness
Produce decision-ready input for target design and migration-wave planning.
What Discovery Typically Captures
Section titled “What Discovery Typically Captures”Inventory
Comprehensive capture of servers, virtual machines, databases, middleware, and applications.
Dependency analysis
Mapping of communication paths and runtime dependencies between systems and applications.
Resource utilization
Analysis of actual CPU, memory, storage, and I/O behavior over a representative period.
Operational context
Collection of backup, patching, SLA, compliance, and operational constraints.
Application owner input
Structured questionnaires and interviews to validate assumptions and close data gaps.
Typical Delivery Setup
Section titled “Typical Delivery Setup”In practice, Discovery is often run together with STACKIT partners. Partners typically use their own tooling landscape to collect and normalize technical data into a central repository. Many programs also trigger targeted questionnaires for application owners directly from these tools to enrich technical findings with business and operational context.
This combined model improves speed and consistency while keeping stakeholder validation built into the process.
Two Evidence Streams: Technical vs. Human-Driven
Section titled “Two Evidence Streams: Technical vs. Human-Driven”Discovery intentionally combines two evidence streams that complement each other:
- Technically derived evidence: Tooling-generated findings from inventory exports, runtime metrics, and dependency signals. This stream provides scale, consistency, and repeatability.
- Application-owner enrichment (human-driven): Validated business criticality, lifecycle intent, release constraints, and operational realities from owner interviews and questionnaires.
Neither stream is sufficient on its own. Technical evidence without owner context can misclassify critical workloads, while human input without technical grounding can hide coupling and capacity risks. Discovery quality depends on reconciling both streams into one decision-ready view.
Discovery Process at a Glance
Section titled “Discovery Process at a Glance”The following diagram shows how Discovery transforms technical and stakeholder input into decision-ready outputs for the downstream modules.
Typical Analysis Patterns in Discovery Tooling
Section titled “Typical Analysis Patterns in Discovery Tooling”During Discovery, tooling commonly applies the following analysis patterns:
- Record normalization: Merge heterogeneous exports into one coherent application model.
- Dependency mapping: Detect communication paths, data exchange, and coupling patterns.
- Criticality and risk scoring: Evaluate business impact, failure domain, and compliance exposure.
- Utilization profiling: Build workload demand baselines for right-sizing and target planning.
- Segmentation analysis: Cluster applications by readiness, constraints, and migration strategy fit.
- Wave simulation: Model move groups and sequence options under dependency constraints.
- Gap and assumption tracking: Keep unresolved findings transparent with confidence levels.
These analyses establish the technical fact base. The human-driven stream then validates, prioritizes, and contextualizes these findings for executable migration decisions.
AI-assisted discovery assets
Section titled “AI-assisted discovery assets”Use AI-assisted discovery assets to structure workload inputs, service mapping, readiness findings, and R-strategy signals before architects validate the resulting discovery baseline.
Asset title
Framework
Asset type
Migration
Software
Adoption
Service
Method in Practice
Section titled “Method in Practice”- Aggregate source data from CMDBs, hypervisors, cloud inventories, monitoring, and export files.
- Normalize and consolidate records into a common application-centric model.
- Discover and validate dependencies (network, data, identity, integration, and batch flows).
- Enrich with owner input on criticality, lifecycle, constraints, and migration feasibility.
- Classify workloads for migration strategy options and wave sequencing.
- Validate findings with architecture, security, platform, and business stakeholders.
Why Discovery Is Critical
Section titled “Why Discovery Is Critical”- Reduces migration risk: Early visibility of hidden dependencies lowers outage and rollback risk.
- Improves wave planning: Workloads can be grouped realistically by coupling, criticality, and readiness.
- Prevents over/under-sizing: Measured utilization replaces assumptions in target capacity planning.
- Supports governance: Security, compliance, and operational constraints are addressed before rollout.
- Strengthens stakeholder buy-in: Shared facts improve decision quality across business and IT.
Relevance for Follow-On Modules
Section titled “Relevance for Follow-On Modules”Discovery outputs are directly reused by the next modules in Design and Mobilize:
Design
Uses dependency, capacity, and risk insights to shape target architecture options.
Security and Compliance
Uses data classification and control gaps to define prioritized security requirements.
Landing Zone
Uses platform and governance constraints to define foundational setup decisions.
Migration Plan
Uses move groups, criticality, and sequencing constraints for realistic wave planning.
Operating Model and Business Case
Uses ownership, process impact, and value/risk signals for staffing and investment priorities.
Discovery Outputs
Section titled “Discovery Outputs”At minimum, Discovery should produce the following outputs:
- Consolidated application baseline: Mapped inventory by domain, environment, and criticality.
- Dependency map: Verified upstream/downstream relationships and integration touchpoints.
- Utilization profile: Evidence-based resource behavior and sizing assumptions.
- Constraint register: Security, compliance, licensing, and operational constraints.
- Migration readiness view: Prioritized candidates, risks, and sequencing recommendations.
These outputs are essential prerequisites for continuing with detailed design work and a credible migration plan.