Skip the learning curve: rethinking data migration for real outcomes
Data migration is often viewed as a technical project, but successful migrations are really about achieving better business outcomes. In an article published by Databricks‘ Global Partner Migration Program Leader Vijay Anala, they explain why organizations should move beyond simply transferring data and instead focus on creating a modern, AI-ready data foundation that delivers measurable value. One of the article’s key messages is that migrations shouldn’t follow a “lift-and-shift” approach. Simply recreating legacy systems in a new environment often carries over old inefficiencies. Instead, organizations should use migration as an opportunity to simplify architectures, improve governance, and modernize data pipelines so they’re easier to manage and scale. Anala also emphasizes reducing uncertainty before migration begins. By assessing workloads, dependencies, and data quality upfront, organizations can prioritize projects that deliver the greatest business impact while avoiding costly surprises later. AI-assisted tools can further streamline tasks like code conversion, workload analysis, and migration planning, helping teams accelerate projects without sacrificing accuracy. Perhaps the biggest takeaway is that success shouldn’t be measured by how much data has been migrated, but by the value the migration creates. Metrics like improved analytics, faster decision-making, stronger governance, and AI readiness are far more meaningful than simply tracking the percentage of completed workloads. Ultimately, Anala encourages organizations to think of data migration as a business transformation rather than an IT project. With thoughtful planning and a focus on long-term outcomes, companies can build a data platform that supports innovation well beyond the migration itself.
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