Addressing expectations, retraining on new structures, and providing change support

Data migration is one of the most critical phases of any digital transformation project. While IT teams focus on moving and validating data, the long-term success of the migration depends heavily on how well users adapt to the new system. Without proper training and support, even the most technically flawless migration can lead to confusion, frustration, and resistance among end-users.

Here’s how organizations can ensure smooth adoption by addressing expectations, retraining on new structures, and providing change support.

System integrations are rarely just about technology—they’re about people, processes, and communication. When multiple systems, stakeholders, and business units need to work together, the technical challenges are only half the battle. The real success of an integration project lies in fostering a collaborative environment where everyone involved can contribute, adapt, and stay aligned.

In today’s fast-paced digital landscape, where organizations are modernizing legacy systems and stitching together new platforms, collaboration is no longer optional—it’s a requirement for delivering value quickly and effectively.

Data migration projects are high-stakes initiatives. They impact business continuity, decision-making, and often come with tight timelines. While technical teams live in the details—schemas, ETL jobs, error logs—executives need a different kind of visibility. Communicating effectively with them can mean the difference between a perceived success and a project in trouble.

When it comes to data migration, the temptation is to design the “perfect” architecture with the best tools money can buy. But in reality, migrations are short-term, high-intensity projects — you might only need the architecture for weeks or months. Overbuilding can mean wasting budget on tools that will gather dust once cutover is done.

A smart migration architecture is fit-for-purpose — it reuses what you already have, matches the project’s lifespan, and considers how the target environment will operate after migration.

Government agencies are under increasing pressure to modernize legacy systems, comply with evolving regulations, and provide better digital services. At the heart of many of these initiatives is data migration—a technically complex and often high-risk effort that requires careful planning and execution.

One of the first decisions in any data migration project is choosing the right project management approach. Should you use a structured, phased Waterfall methodology? Or adopt a more flexible, iterative Agile model? For many government projects, the answer may be a blend of both.

Let’s explore the pros and cons of Waterfall and Agile in the context of public sector data migration—and how hybrid models can offer the best of both worlds.

Last week we described how important it is to involve the business, and not just IT on your your data migration project.  This week we dive down a bit and discuss specific team roles that you should fill in order to make your project successful. 

When planning a data migration, choosing the right technology and tools is only part of the equation. The real success factor? Assembling the right team.

Data migration projects are complex, high-risk initiatives that touch nearly every part of a business. Done right, they set your organization up for streamlined operations, better insights, and future growth. Done wrong, they can result in lost data, business disruptions, and damaged reputations. That's why you need more than just technical expertise—you need a multidisciplinary team with clear roles, responsibilities, and a shared understanding of the mission.

Data migration is often viewed as a technical task—something for the IT department to handle quietly in the background. But treating data migration solely as an IT problem is one of the fastest ways to turn a project into a costly, delayed, and high-risk endeavor.

Whether moving to a new system, consolidating platforms after a merger, or modernizing legacy infrastructure, data migration must be recognized for what it truly is: a business-critical transformation. And that means the business must be actively involved.

When modernizing a legacy system, data migration is more than just a technical task — it’s a full-fledged program with deep dependencies on both legacy and target systems. Yet many teams overlook one of the most important resources for success:

A dedicated data migration sandbox for the new system.

While development environments are common, a migration-specific sandbox is often missing — and that’s a mistake. A data migration sandbox gives your team the space to test data loads, validate transformations, and work through the complexity of legacy-to-modern mappings — all without disrupting active development or risking production stability.

In this post, we’ll explain why having a dedicated migration sandbox is a best practice, what it should look like, and how to keep it aligned with your evolving target system.

One of the most common — and dangerous — shortcuts in legacy system modernization and data migration projects is working directly on the live production database.

Whether you’re profiling data, writing migration scripts, testing transformation logic, or validating mappings, using production data directly can introduce massive risk to your operations, compliance, and project timelines.

Best practice is simple but critical:

Always use a sanitized, secure, and up-to-date copy of the production database — never the production environment itself.

In this post, we explore the why behind this rule, the risks of violating it, and how to work safely and effectively with production data in a modern project.

When it comes to migrating data during a legacy system modernization, teams are often tempted to lean on what they know — manual exports, spreadsheets, or long chains of SQL scripts.

While these methods can work for small or one-off jobs, they don’t scale, lack transparency, and introduce substantial risk to complex migrations. If your modernization effort involves multiple tables, rules, and stakeholders, then it’s time to use a proper ETL (Extract, Transform, Load) tool.

In this post, we explore why using an ETL tool is a best practice for modern data migration projects and what you gain by choosing this structured, repeatable, and auditable approach over manual alternatives.

Data migration is one of the most complex and risk-prone aspects of any legacy system modernization project. Yet far too often, teams jump in without a clear strategy — relying on ad hoc methods, tribal knowledge, and hopeful thinking.

This is where projects go off the rails.

Whether you're migrating from a 30-year-old mainframe or a decade-old ERP, the best practice is clear:

Use a proven, repeatable, and structured approach to data migration.

In this article, we explain what a proven approach looks like, why it’s essential, and what can go wrong when you skip it.

It all begins with an idea.When organizations take on legacy system modernization, there’s one critical aspect that often gets underestimated or delayed: data migration. 

It’s common to see teams focus heavily on application architecture, UI/UX improvements, cloud infrastructure, and performance enhancements — all important components of modernization. But leaving data migration until later in the project is a costly mistake. 

Best practice dictates that data migration should start early — sometimes even before the main modernization project kicks off. Why? Because data is the lifeblood of your organization, and preparing it for a new system is far more complex than simply “moving it over.” 

In this post, we explore why early data migration planning and execution is essential, and what activities you can (and should) begin immediately to de-risk your modernization effort.