Our Methodology

Research Approach

Software Modernization Intelligence produces market intelligence by aggregating and analyzing data from real software modernization projects. Rather than surveying practitioners about future intentions or relying on vendor-supplied benchmarks, we focus on documented project outcomes — what organizations actually spent, how long migrations took, and what success rates were achieved in production environments. The cost, timeline, and success-rate benchmarks published across this site are aggregated from 3,733+ verified projects spanning cloud, mainframe, data, application, and security modernization.

Each migration path is researched independently. We identify the relevant source and target technologies, map the vendor landscape, collect outcome data from multiple organizations, and synthesize that data into cost ranges, timeline estimates, success rate benchmarks, and decision frameworks. The result is a structured profile that procurement teams and engineering leaders can use directly in business case development and vendor evaluation.

Data Sources

Our research draws from several categories of publicly available information:

• Case studies and post-implementation reviews published by organizations that have completed modernization projects, including conference presentations, blog posts, and technical documentation.
• Vendor disclosures including published pricing, customer references, implementation timelines, and success metrics shared in marketing materials, partner directories, and sales collateral.
• Analyst reports from independent research firms that track enterprise technology adoption, modernization spending, and vendor market share.
• Procurement records and contract data available through public disclosure requirements, government procurement databases, and industry benchmarking services.
• Technical documentation from open-source migration tools, cloud provider migration services, and automated code conversion platforms that publish performance and compatibility data.

We do not use data from paid surveys, self-reported maturity assessments, or vendor-funded research where the methodology is not independently verifiable. All sources are evaluated for recency, specificity, and potential bias before inclusion.

Review and Validation Process

Every migration path goes through a structured review before publication. The review process includes:

• Data sufficiency check: We require a minimum number of independent data points before publishing cost ranges or success rates. Paths with insufficient data are held in draft until additional sources are identified.
• Outlier analysis: Extreme values in cost or timeline data are flagged and investigated. Where outliers reflect genuine variation (e.g., very large or very small organizations), they are included with context. Where they reflect data quality issues, they are excluded.
• Cross-validation: Cost and timeline data is compared across sources to identify inconsistencies. When vendor-reported figures diverge significantly from independent assessments, we note the discrepancy and weight independent sources more heavily.
• Complexity calibration: Each migration path is assigned a complexity level (Low, Medium, High, or Critical) based on the technical scope, organizational change required, and typical risk profile observed across projects.

How Data Is Presented

All quantitative data is presented as ranges rather than point estimates. A migration path with a median cost of "$500K-$2M" reflects the central tendency across the projects we analyzed, not a single representative figure. This approach acknowledges that modernization costs vary significantly based on organization size, codebase complexity, team capability, and vendor selection.

Success rates represent the proportion of projects that achieved their stated objectives within acceptable cost and timeline overruns, as reported in post-implementation reviews. A success rate of 78% means that 78% of projects in our sample met their primary modernization goals, not that every aspect of those projects went as planned.

Vendor profiles are based on publicly observable information — published case studies, team size, geographic presence, technology specializations, and client references. We do not accept payment for vendor placement or recommendations. Featured vendors on migration pages reflect demonstrated expertise in that specific migration path, not commercial relationships.

Limitations

We publish these benchmarks because order-of-magnitude cost data is otherwise hard to find, but they carry real limitations that readers should weigh before relying on them:

• Selection bias toward published outcomes. Organizations are more likely to publicize successful, on-budget migrations than failed or overrun ones. Our success rates and cost ranges are therefore best read as an optimistic-to-central view, not a worst-case one. We partly counteract this by weighting independent post-implementation reviews and procurement records above vendor marketing.
• Ranges, not quotes. Every figure aggregates across organizations of different sizes, sectors, and codebase profiles. A published range is not a substitute for a scoped vendor estimate produced after a discovery phase. Two projects with the same line count can differ threefold in real cost.
• Sample sizes vary by path. Some migration paths draw on a few hundred documented projects; newer or more niche transitions draw on far fewer. Each path and each per-domain table reports its sample size (n=) so recency and depth can be judged independently. We do not sum per-path sample counts into a single headline metric, because the same underlying engagement can inform more than one path.
• Currency and drift. Cloud pricing, licensing terms, and labor rates move faster than any static benchmark. Figures reflect the review window noted on each path, not live market pricing. Treat anything older than its stated review date as directional.
• USD and English-language sourcing. Costs are normalized to US dollars and our sources skew toward North American and European disclosures. Regional labor-cost differences can shift real project economics materially from the ranges shown.

Update Cadence

Migration paths are reviewed and updated on a rolling basis. Each path displays its last review date and the number of projects analyzed, so readers can assess both recency and sample size independently. Aggregate pages such as the cost benchmarks are regenerated from the underlying path data on each publish, so they always reflect the most recently reviewed figures rather than a separately maintained snapshot.

Major updates — such as the addition of new migration paths, significant changes to cost benchmarks, or new vendor entries — are reflected in our research insights. Minor updates, such as the addition of new data points that do not materially change published ranges, are incorporated during routine reviews without separate announcements.

We prioritize keeping existing paths current over expanding coverage to new domains. A migration path is only added when we have sufficient real-world data to support actionable guidance, rather than speculative coverage of emerging transitions without track records.