Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
Data migrations are too often thought about as just a simple “lift and shift” of data from one system to another. In many cases, organizations are grappling with what’s called an “N to 1” migration, in which they need to take information from multiple places and migrate it into one new place.
Others are dealing with what are known as 1:N migrations, in which they’re going from one system to many systems. Still others face an “N:N” challenge, where they’re trying to move from many systems to a different set of many systems.
This adds far more complexity. The data is often in different formats, and you’re striving for a uniform output. It’s almost like MTV’s The Real World: The story of seven strangers picked to live in a house and have their lives taped. In other words, coming together as one group that must function properly. When it comes to N to 1 migrations, let’s hope there’s a lot less drama in our attempts to “start getting real.”
The need for N to 1 migration
Mergers are an obvious example of the need for an N to 1 or an N to N migration. So is digital transformation. When organizations are taking the opportunity to reimagine their technology landscape, taking advantage of cloud technology or the latest ERP systems, they’re going to have regional and local disparate systems that they will want to bring together.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
The promise of digital transformation is that everything is talking to each other, and you have access to everything. Well, to make that happen, you need to break down silos. Perhaps you have one ERP running your South American business and another one running your North American business, but what you want to do is try to procure for your business across your entire landscape.
You want to unify your systems, not just in a reporting sense but in a real transactional, execution sense. It’s rare for people to stick with the same kind of target landscape. When they go into the cloud, of course, they’re evaluating transformation, but it’s also typically about evaluating the new set of technology solutions. This means you’re going to be moving to what could be a whole new landscape, and there’s not just a whole new system but a whole new way of working.
The challenges of N to 1 migration
Let’s say you’re migrating from an iPhone to an Android, and you need to get all of your contacts, photos and other information from one phone to the other. That’s a fairly easy transfer procedure. But what if it’s been a little while since you did a proper transfer or you never transferred information from your previous two phones? What if you now want to bring all the information from your 2011 BlackBerry, your old iPhone and that flip phone you originally had to the new phone? Well, then it becomes a bit more challenging.
For organizations looking to do data migrations, there’s a parallel. A simple 1:1 migration has its share of challenges, but those are multiplied as more legacy systems come into scope. Moving data from point A to point B is an already-solved technical problem. The business challenge becomes figuring out where to move the data and whether it is being moved in a way that can run your business how you want in the new system.
N to 1 becomes almost as big of a people challenge as it is a technical challenge: You must have flexibility, and there’s a lot of change management involved. It’s when you need to bring information into one uniform platform from a multitude of different sources that it really starts to get complicated. Just like in The Real World — the more people, the more drama.
In many situations, it’s not just a “many to 1” migration; it’s a “many to many” migration. The more stakeholders and the more potential issues there are, the greater the need for change management, agility and rapid simulation, but also the more potential payoff in terms of benefits at the end of the migration.
Bringing it all together
To continue the The Real World metaphor, you’ve got these people (in our case, systems) who have grown up and been parented with very different styles — and now you’re trying to make them work together. Accomplishing this also typically leads to many micro-projects that aren’t always expected. It’s really just about more people and more opinions involved, and that puts a greater emphasis on agility.
One of the first things is getting the stakeholders’ visibility and tactically touching their data in these new systems to see if their assumptions meet reality. You have to be prepared for change; expect iterative cycles. That means you need a migration solution that can handle iterations and work with agility in the real world.
Facing the data reality
The last thing you need during a data migration is a lot of drama. You want your data to get along, to integrate well and serve its purpose in its proper place. Today’s data migrations are much more complex than a “lift and shift” approach can manage. It requires people and technology that work together to make the migration a success.
Matt Wagnon is VP of product management at Syniti.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!