What is Data Migration?
To begin with, let’s start by defining what data migration actually is. Simply put, it is the process of ensuring the smooth transferring of data from one environment to another. Whether you’re transferring that data between storage types, formats, or changing it over to a completely different system, data migration is most often times undertaken as part of a broader application—like if your company is changing over your CRM functions from Microsoft to Salesforce.
Why is Data Migration Important?
Modern companies rely on mining big data sources in order to identify trends in the market and to reveal patterns in consumer behavior, and while legacy systems often contain mountains of data that can be mined, the way that these end data points are stored are cumbersome and don’t easily integrate into more intuitive and effective analytical tools.
Hence the common need for data migration within companies that are transitioning from older systems to more dynamic ones. Even if you need to migrate stored data from your legacy Oracle system into a newer Oracle system such as their Big Data Appliance, the process of doing so can be time consuming and stressful if you’re not sufficiently equipped to take on the challenges.
Biggest Challenge - Ensuring data quality
At the end of the day data migration is typically an undervalued part of the process when it comes to adopting new systems. Because the new system or application you are adopting is often seen as the actual investment, data migration planning is more often than not viewed as a “necessary evil” that’s secondary in importance to acquiring new, more efficient technology. However, transferring data points from one system to another isn’t as simple as dumping information from one bucket into the next.
In actuality it’s the data that you already possess which maximizes (or complicates—if you’ve planned your data migration strategy poorly) the use of any newly acquired enterprise applications. That being said, common problems with the actual transference of data from one system to another can include redundancies, duplicates, and inaccuracies. Much of the time these data inequalities don’t rear their ugly heads until data migration happens because what more advanced systems may perceive as a fault, an older system may perceive as a suitable data point.
Data doesn’t match up perfectly from system to system, so when you’re planning your budget for new implementation make sure to factor in more than the cost of acquiring new software. Also plan for the time you think it will take to get your new system up and running, and budget accordingly. It takes man power and expertise to analyze variables such as data volume and data decay as well as overall data quality.
Data Migration Strategies
All told, there are two main strategies you can use for planning out your data migration efforts, we’ve listed them below.
A Big Bang Migration strategy can happen in a short amount of time, but because of the happen-all-at-once factor, it also comes with some risks. First and foremost is that for a Big Bang Strategy to work, you must first shut down all databases and applications immediately. Essentially you’ll need to stop all your company’s work for as long as it takes for the migration to complete.
Of course, not many companies or organizations can survive with a core system being out of service for long—which puts added pressure on the migration being pulled off seamlessly and accurately so that your organization can be operational again ASAP. The reality, however, is that with all the inherent complexities that entail a data migration strategy, few companies ever go this route.
The more prudent of migration strategies is definitely the Trickle Migration Strategy. This effectively allows you to take an incremental approach with it comes to migrating data from one system to another.
According to a recent whitepaper by Oracle the Trickle Migration Strategy, “involves running the old and new systems in parallel and migrating the data in phases. This method inherently provides the zero downtime that mission-critical applications requiring 24/7 operation need. A trickle migration can be implemented with real-time processes to move data, and these processes can also be used to maintain the data by passing future changes to the target system.”
To be sure, because the operating systems are working in parallel with each other as the data migration is happening, there are many added complexities to this method when compared to the Big Bang method. In this scenario it also becomes extremely important that you budget properly for the overhead expenses that come with either shifting your current staff’s duties towards data migration implementation (if you have a staff that’s capable of delivering on such a product) or going the route of outsourcing qualified IT personnel to oversee this process.