The Migration Factory Approach: Best Practices for Delivering Repeatable Business Benefits

There are many reasons why organizations are undertaking data migration projects — including a merger, acquisition, or divestiture (MA&D) event or rolling out new applications across multiple divisions within a global company. But as they try to implement these projects, they're battling a combination of on-prem and hybrid cloud deployments, legacy systems, dark data, and siloed information. The biggest problem of all is that they've got poor quality data. And if you're using bad data for your migration, it's akin to putting old gas in a new Ferrari. It's not going to cut it.

What if there was a different way to do this that would not only ensure high-quality data but could also be used for multiple migrations? A comprehensive, repeatable process offers many benefits for an enterprise with enormous volumes of data and systems that need to be consolidated or moved to new applications. It'd be especially helpful for companies facing MA&D events or any multinational digital transformation, which tends to happen in waves, and country by country.

Enter the migration factory concept. Anyone who's been involved in complex data migrations knows a factory mindset is ideal. It enables you to learn, leverage, and repeat — leading to faster and better results for a lower cost.

Related:5 Considerations for Migrating Data to the Cloud

A migration factory involves setting up a common migration pattern that can be used to synchronize and merge data assets to meet business process requirements and the company's operating model. But you've got to have the right implementation process to make this successful, which includes having software that facilitates repeatability and the right delegation of roles and responsibilities.

A Factory Approach for Data Migration

A migration factory focuses on developing repeatable processes and rules for each migration to follow a common pattern with the least amount of configuration. Using this approach, you can create a team with complementary skills and a common methodology and tool set. This team is better equipped for success, whether they're faced with straightforward or difficult migrations, as well as assignments with short deadlines. Today's software can facilitate a comprehensive and repeatable approach and can serve as a valuable part of the team. All the knowledge gained from one migration can be used again; there's no need to re-create the wheel.

Being able to involve both business and technical knowledge employees throughout the process is another crucial component of a migration factory. Data ownership can't be just the responsibility of the technical team, but often, the tools at hand only cater to the requirements of people with technical expertise. A migration factory needs a mix of technical, business process, and business unit owners for its data ownership and team structures. 

Establishing Roles and Responsibilities

Having carefully delineated roles and responsibilities will make the migration factory approach more predictable and far more repeatable. This isn't just a technical topic with the need for technical roles; it must involve the business experts, too. Business process knowledge is equally as critical as data knowledge during a migration.

Organizations need to understand why they have the data in the first place, why it matters, and how it fits into the overall business picture. In other words, they need to know the context of the data, which is what the business people bring to the table. 

Some of the organizations have found success with an approach that breaks down the roles and responsibilities like this:

Role 1: The business experts, aka "the data owners"

Without the data owners, a data migration project wouldn't work. They need to be involved from the beginning. The migration isn't just moving from system A to B; it needs a lot of mapping, according to business requirements. The data owners are experts in procurement, marketing, and production. They know the process requirements well. They review and even provide data mappings, data quality requirements, and enrichment when data is required but missing from legacy applications. Sometimes this requires collecting additional data from suppliers or internally. They've got a functional view that supports the project to the end when the data is finally validated.

Role 2: The global data coordinator

These individuals don't have a purely functional view, as the business experts/data owners do. They have a global view and represent the business models within the organization, making sure the integration is consistent across the regions it operates in and specializing on the legal requirements inside the country.

This is especially important for organizations undergoing acquisitions. The acquiring business might have slightly different business processes. Changes may be required to align with how the acquired business runs today to fit the corporate standard. There could also be something unique in the acquired company's business processes that the acquirer may want to adopt globally. It's all about maintaining corporate standards while not overlooking something new that would provide further competitive differentiation.

Role 3: The data factory team

These people work closely with the business experts to execute the data migration. They have a technical understanding and a topic understanding; they know the tools, the developments, and what needs to be set up. This isn't a purely technical team; they understand business topics and align with the business objectives. That's because migration itself is just a means to an end. If organizations don't migrate data in the context of the business process, they're just moving ones and zeroes, which leads to poor data quality and business process failures.

Governance Council

None of these roles can operate in isolation, though. These various stakeholders must come together regularly to look at standards across the enterprise and take advantage of new lessons learned as new acquisitions arise. The business side gains a better understanding of IT requirements and data migration; the IT people gain specialization in business. This ultimately makes their work together more efficient.

Streamlining Data Migrations

Data migration can be an "all-hands-on-deck" task, but a migration factory approach can turn the process into a well-oiled machine. A key part of creating this well-oiled process is making sure that everyone's roles and responsibilities are clear. By adopting a migration factory approach and using repeatable templates, such as those that software offers, organizations can harmonize their data assets and streamline business processes. With the active involvement of business and technical knowledge staff, data migrations become more predictable, efficient, and aligned with the overall business objectives. The migration factory approach empowers organizations to optimize their data management strategies, drive digital transformation, and unlock the full potential of their data in a rapidly evolving business landscape.

Kevin Campbell is CEO of Syniti.