To be nice, most business data is suboptimal. Do you want to test this statement in your company? Just ask where the registered customer data resides. If you ask someone from four different departments, you will get four very different answers.
This problem is the natural by-product of 20 to 30 years spent creating new databases using whatever database was popular at the time. This includes databases for mainframes, large relational databases, open source SQL, object databases, and now special purpose databases.
Heterogeneity and complexity issues are an undeniable reality for those looking to move terabytes of data to the cloud. You should find an analog database in the cloud that is an exact match for the brand or one that requires a minimal amount of restructuring and conversion. Unfortunately, this approach perpetuates the database silo problem. It’s a classic and seemingly endless example of kicking the next generation of IT in the way.
The problem is that the “kick the can” way is relatively cheap. The “fix everything” way? Not so much. Those with a short-term view often find that migrating data to a public cloud does not provide real gains in cost savings, agility, or productivity. In fact, the problem that resided in your data center is now a problem that resides in the cloud.
The pandemic led many organizations to create a larger role for the public cloud within the enterprise. Most companies just want their move to the cloud to be fast and inexpensive. That means they take a lift and shift approach to data migration. At first, this method may make budget sense. However, having a long-term view, lift and change means that you will have to migrate your data twice: once, the wrong way, and second, the right way.
Here’s the bad news: The most effective data migration efforts take years, not months.
Today, there are those who see data migration to the cloud as an opportunity to finally fix their business data, to make data a first-class citizen, and to do wonderful things with all the data their business has collected from over the years.
The best migration efforts are focused on normalizing and improving all data as it moves to the public cloud. Here are three critical aspects of a more efficient data migration:
Single source of truth. A database should manage data on customers, inventory, sales, etc. You shouldn’t have to collect data from 20 different locations and deal with the resulting data quality issues.
This may mean major surgery on your data and perhaps the normalization of your database after 30 years. However, this is a basic step that makes business data more usable and more valuable to the business.
Heterogeneous metadata management. There is an abstraction layer in all local and cloud databases that allows us to alter the structure and meaning of the data and do it from a single interface.
Data virtualization. A common architecture trick is to take advantage of data virtualization. This allows you to view any number of physical databases that you can virtually combine or divide to meet your existing needs. The power of data virtualization is that it does not require changes to the physical back-end database to restructure the data. It is a fast way to move databases to the cloud and continue to handle data in a much more efficient and agile way.
If this sounds like new technology, it is not. Data virtualization has been around since the 1990s and can now be had in public clouds. Some see data virtualization as a trap. It is actually a sensible compromise if there is only a small budget to increase and improve the transfer of data to the cloud.
If you want to block the failure, relocating your database as it is to a public cloud will ensure that. Let’s face the facts; your data is probably a mess. There comes a time when band-aids can no longer hold decades of data bits and pieces together. It’s about time that most business data underwent the necessary surgery to fix the underlying problems.
Simply moving the problem to the cloud simply creates a bigger problem. Do you really want to be that company?
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