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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.

What Discovery Is

Section titled “What Discovery Is”

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”

Complete baseline

Create a reliable application and infrastructure baseline that goes beyond pure quantities.

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.

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.

Discovery Source-to-Decision Flow Discovery separates technical and human-driven inputs, runs technical-first and human-enriched analyses, and hands over both insight streams to follow-on modules. Discovery Inputs Discovery Analysis Tooling Handover Outputs Technical and automated discovery Assessment-driven human input Technical-first analyses Human-enriched analyses Tool-derived outputs Assessment-validated outputs Infrastructure Inventory CMDB, VM, database, middleware, storage Runtime and Utilization Data CPU, memory, I/O, network and seasonality Integration and Flow Signals Network paths, APIs, identity, data movement Security and Compliance Context Data classes, controls, audit requirements Owner and Business Input Criticality, release windows, lifecycle plans Normalize and Correlate Technical-first: unify records and technical identities Dependency Mapping Technical-first: infer communication and coupling Utilization and Sizing Analysis Technical-first: estimate baseline demand corridors Preliminary Segmentation Technical-first: cluster by stack and environment Criticality and Risk Calibration Human-enriched: validate business impact and constraints Wave Feasibility and Sequencing Human-enriched: reconcile dependencies with release windows Assumption and Gap Register Human-enriched: track open points and confidence Design Target architecture options and sizing facts Landing Zone Platform guardrails and account structure needs Migration Plan Wave backlog, sequencing, and cutover windows Security and Compliance Control needs, data classes, remediation points Operating Model Role model, ownership boundaries, process impact Business Case Value/risk profile and modernization priorities

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 fact base needed for design and mobilization decisions.

Method in Practice

Section titled “Method in Practice”
  1. Aggregate source data from CMDBs, hypervisors, cloud inventories, monitoring, and export files.
  2. Normalize and consolidate records into a common application-centric model.
  3. Discover and validate dependencies (network, data, identity, integration, and batch flows).
  4. Enrich with owner input on criticality, lifecycle, constraints, and migration feasibility.
  5. Classify workloads for migration strategy options and wave sequencing.
  6. 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.