How do you build a business case for data platform migration?

Building a business case for a data platform migration means quantifying the total cost of your current platform, projecting the cost and risk reduction of the target state, and presenting both to the stakeholders who control the budget. The strongest cases combine hard financial numbers with documented operational and compliance risks. Below, we work through the questions that typically come up when putting that case together.

Many of the teams we work with at Stackable are doing exactly this – evaluating whether moving to a Kubernetes-native, open-source platform justifies the effort. Here’s how the Stackable Data Platform fits into that decision.

What costs should you quantify when migrating a data platform?

When building a migration business case, you need to quantify both the costs of staying on your current platform and the costs of the migration itself. On the current-state side, that means licensing fees, support contracts, cloud egress charges, and the engineering time consumed by workarounds. On the migration side, it means project labor, tooling, testing, and any temporary dual-running costs.

A practical cost inventory for your current platform should cover:

Licensing and subscription fees – including any per-node, per-core, or consumption-based pricing that scales with your data volumes
Vendor support contracts – annual fees for access to patches, CVE fixes, and technical support
Cloud egress and lock-in costs – the price of moving data out of a specific cloud provider’s managed service
Engineering overhead – time spent on manual upgrades, configuration drift, and workarounds that a more automated platform would eliminate
Opportunity cost – features or integrations your team cannot build because the current tooling constrains the architecture

On the migration side, be honest about labor. A phased migration of a production data platform typically involves data engineers, platform engineers, QA, and security review. Underestimating this is the most common reason migration business cases fall apart during execution.

How do you calculate ROI for a data platform migration?

Data migration ROI is calculated by subtracting the total cost of migration from the net savings generated over a defined time horizon, then dividing by the migration cost. A three-year horizon is standard for infrastructure projects. The key inputs are license savings, reduced support spend, lower cloud costs, and engineering time recovered – offset against one-time migration costs and any ongoing platform maintenance.

A simplified formula looks like this:

ROI = (Total savings over 3 years – Migration cost) / Migration cost × 100

In practice, the savings side is easier to defend when it is broken into categories:

Direct cost reduction – eliminated license fees, reduced support contracts, lower infrastructure spend
Operational efficiency – engineering hours recovered through automation, infrastructure-as-code provisioning, and self-service tooling
Risk reduction – avoided costs associated with vendor price increases, end-of-support events, or compliance failures
Strategic optionality – the ability to move workloads between environments without renegotiating contracts

Be conservative with efficiency gains. Finance teams will scrutinize any line that translates “hours saved” into monetary value. Anchor those estimates to actual ticket volumes, incident response times, or upgrade cycles you can document from your current environment.

What business risks justify migrating away from a proprietary platform?

The business risks that most commonly justify a data platform migration are vendor dependency, escalating licensing costs, end-of-support timelines, and compliance exposure. When a single vendor controls your data infrastructure, they also control your upgrade path, your pricing, and your ability to move workloads elsewhere. That dependency becomes a risk when the vendor changes their licensing model, discontinues a product, or cannot meet your regulatory requirements.

Specific risk triggers worth documenting in a business case include:

Licensing model changes – some vendors in this space have changed pricing structures with limited notice, which many teams report creates unplanned budget pressure
End-of-support events – running unsupported software in regulated industries creates direct compliance risk under frameworks like the Digital Operational Resilience Act (DORA) and the NIS-2 Directive
Data sovereignty constraints – certain managed services may require data to reside in specific regions or pass through vendor-controlled infrastructure, which can conflict with data sovereignty requirements in regulated sectors
Supply chain opacity – closed-source platforms can make it difficult to verify what is running in your environment, which matters when demonstrating compliance with the Cyber Resilience Act (CRA)
Architectural ceiling – some platforms may not support the data mesh, lakehouse, or streaming architectures your organisation needs to build

Each of these risks has a probability and a financial exposure. Documenting both – even with rough estimates – gives decision-makers a basis for comparing migration cost against the cost of inaction.

Who needs to approve a data platform migration business case?

A data platform migration business case typically requires approval from the Chief Data Officer or equivalent data leadership, the Chief Technology Officer or IT Director, and Finance. In regulated industries, the Chief Information Security Officer and legal or compliance teams are also stakeholders, particularly where data sovereignty, audit trails, or regulatory frameworks are involved.

The approval chain matters because different stakeholders evaluate the case on different criteria:

Finance focuses on the cost model, payback period, and how migration costs are classified (capital versus operational expenditure)
CTO and platform leadership evaluate technical feasibility, migration risk, and long-term architectural fit
CDO and data leadership assess whether the target platform supports the data products, governance model, and access patterns the business needs
Security and compliance review how the new platform handles access control, auditability, and regulatory obligations

Structuring the business case so that each section speaks to a specific stakeholder’s concerns – rather than presenting a single undifferentiated document – significantly improves approval rates.

What does a strong data platform migration business case include?

A strong data platform migration business case includes a documented current-state cost and risk analysis, a clear description of the target architecture, a realistic migration plan with phased costs, a quantified ROI projection, and an explicit risk comparison between migrating and staying. It should be specific enough that a non-technical finance reviewer can follow the cost logic, and technically detailed enough that engineering leadership can validate the approach.

The core sections of a well-structured migration business case are:

Executive summary – the key numbers and the recommendation, in under one page
Current-state analysis – costs, risks, and limitations of the existing platform, documented with evidence
Target architecture – what the new platform looks like, what components it uses, and how it maps to business requirements
Migration approach – phases, timelines, resource requirements, and how risk is managed during transition
Financial model – total cost of migration, projected savings, and ROI over a defined horizon
Risk register – migration risks with mitigations, and the risks of not migrating
Decision criteria – how the target platform was selected and why alternatives were ruled out

The decision criteria section is often skipped, but it is worth including. Showing that you evaluated alternatives and chose based on documented requirements builds credibility with both technical and financial reviewers. It also protects the project if questions arise later about why a particular platform was selected.

How long does a data platform migration typically take?

A data platform migration for a medium to large enterprise typically takes between six months and two years, depending on the complexity of the existing environment, the number of workloads being migrated, and how much of the migration can be parallelized. Simple migrations of well-documented, lower-volume environments can complete faster. Migrations involving legacy systems, complex data lineage, or strict regulatory requirements take longer.

The main factors that extend timelines are:

Data volume and pipeline complexity – the more pipelines, the more testing required before cutover
Undocumented dependencies – some platforms have implicit dependencies that only surface during migration
Dual-running periods – running old and new environments in parallel for validation adds time but reduces cutover risk
Stakeholder coordination – migrations that touch multiple business units require more alignment and sign-off cycles
Regulatory validation – in regulated industries, compliance sign-off on the new environment adds a distinct phase

A phased approach – migrating lower-risk workloads first, validating, then moving critical pipelines – is consistently more reliable than a big-bang migration. It also gives you real data to update the business case mid-project, which finance teams appreciate.

How Stackable helps with data platform migration

The Stackable Data Platform (SDP) is a modular, Kubernetes-native data platform built entirely on open-source components. It is designed to address the specific cost, risk, and sovereignty concerns that typically drive a migration business case toward a more open, vendor-neutral platform.

Specifically, the SDP supports migration business cases in these ways:

Transparent cost model – the SDP is 100% open source with no per-node or per-core licensing fees. The community edition is freely available, and commercial support is priced on fair, predictable terms – not consumption-based pricing that scales unpredictably with data growth.
Cloud-agnostic deployment – because the SDP runs on Kubernetes, it runs on-premises, in any cloud, at the edge, or in a hybrid environment. This directly eliminates cloud egress risk and vendor dependency as line items in your cost model.
Infrastructure-as-code provisioning – the SDP uses Kubernetes Operators to automate the provisioning, configuration, and lifecycle management of components including Apache Kafka®, Apache Druid™, Trino, and Apache Spark™. This reduces the engineering overhead that typically appears as a cost saving in migration ROI calculations.
Data sovereignty by design – the SDP keeps your data in your environment, under your control, with a fully traceable software supply chain. This directly addresses the compliance and sovereignty risks that appear in the risk register of most migration business cases in regulated industries.
Modular architecture – components can be added or removed independently, which supports a phased migration approach rather than requiring a full cutover from day one.

If you are currently building a migration business case and want to understand how the SDP maps to your specific environment and requirements, talk to our team – we can work through the cost and architecture questions with you directly.