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Data migrations are too often thought about as just a simple 鈥渓ift and shift鈥� of data from one system to another. In many cases, organizations are grappling with what鈥檚 called an 鈥淣 to 1鈥� migration 鈥� 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鈥檙e going from one system to many systems. Still others face an 鈥淣:N鈥� challenge, where they鈥檙e 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鈥檙e striving for a uniform output. It鈥檚 almost like MTV鈥檚 鈥淩eal World鈥� – 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鈥檚 hope there鈥檚 a lot less drama in our attempts to 鈥渟tart getting real.鈥�

The need for N to 1 migrations

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鈥檙e going to have regional and local disparate systems that they will want to bring together.

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鈥檚 rare for people to stick with the same kind of target landscape. When they go into the cloud, of course they鈥檙e evaluating transformation, but it鈥檚 also typically about evaluating the new set of technology solutions. This means you鈥檙e going to be moving to what could be a whole new landscape, and there鈥檚 not just a whole new system but a whole new way of working.

The challenges of N to 1 migrations

Let鈥檚 say you鈥檙e 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鈥檚 a fairly easy transfer procedure. But what if it鈥檚 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鈥檚 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 鈥� you must have flexibility, and there鈥檚 a lot of change management involved. It鈥檚 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 鈥� the more people, the more drama. 

In many situations, it鈥檚 not just a 鈥渕any to 1鈥� migration; it鈥檚 a 鈥渕any to many鈥� 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 鈥淩eal World鈥� metaphor, you鈥檝e got these people (in our case, systems) who have grown up and been parented with very different styles 鈥� and now you鈥檙e trying to make them work together. Accomplishing this also typically leads to many micro-projects that aren鈥檛 always expected. It鈥檚 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鈥� 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鈥檚 data migrations are much more complex than a 鈥渓ift and shift鈥� approach can manage. It requires people and technology that work together to make the migration a success. This includes an agile migration solution that helps your data face reality and deal with growth in a way that transforms your business.