Best Practices And Strategies Of Data Migration In Business

 Any company goes through the process of data migration at some stage or another for multiple purposes. The fundamental concept behind data transfer is to adjust the storage space; it can be moved from one server to another, or from one database to another, or to move data from one operating system to another. Businesses are following a simple procedure that involves three basic phases, (ETL) Extract/Transform/Load, irrespective of the data migration method. The last 2 steps will be taken during all migration phases. It is also clear that the data collected goes through several preparation steps, only after which it can be loaded to the target location.

The causes vary from a major overhaul of the overall structure, an upgrade of the existing database, a mixture of new data from a newly acquired source, or an extension of the current one. No matter the reason, a proper migration plan is essential to efficient Data Migration.

Data Migration Strategies are Crucial

Irrespective of the specific agendas, data migration is carried out to improve efficiency and productivity. But it’s a risky and cost-effective operation. If it is not carried properly, it would cost the company time and resources. Failure to plan properly will lead to more problems than resolve them, resulting in overabundance; the data migration process can fail entirely. Appropriate migration strategies are also important.

A number of key considerations for the strategy of an effective data migration plan are as follows:

Know the Data: a full data analysis should be carried out at the root for successful migration; this will help to prevent migration problems.

Absolute Data Clean-up: Because the migration process takes time and resources, it is often advisable to clean up simple and outdated data to make it more concise and manageable.

Data Security & Retention: Although data transition ensures that the transmitted data is exchanged by regulated, secure steps, data continues to be corrupt as time passes.

Data quality control: careful supervision and data monitoring must be carried out to help understand the accuracy of the data.

Right Migration Tools: It is important to consider the choice of the right software tools for a successful migration since not all tools are appropriate for all migration projects.

The above points are important for planning data migration step-by-step. Yet methods are essential to smooth, error-free, and efficient migration.

Data Migration Strategies

Since every enterprise is special and a one-size-fits-all solution would not be viable for all migration needs, migration strategies can also be divided into two categories.

‘Big Bang’ Migration: is known to be the quickest migration mode approach where the data construct is migrated over a limited window of time. This technique has its benefits and drawbacks. The transition can be a 1-time shift, but the risk factors are greater, post-migration issues are more acute.

The ‘Tickle’ migration: this approach is more famous and time-consuming. Here the transition takes place in phases where the source and host processes operate at the same time; it causes less downtime when continuous migration takes place in phases.

Data Migration Best Practices

Some of the best practices, if followed, can give the expected result of effective migration.

Data Backup: Data backup is always recommended as during migration if there is any data leakage or corruption, the data is always saved for backup.

Follow the Techniques: It is commonly seen that either there is a need to abandon a strategy when it doesn’t perform well or when it is too seamless, as both are not advised. Data migration can be time-consuming and tedious sometimes; thus, once a proposal is made and accepted, it should be followed seriously.

Continuous Testing: It is the key component for a successful relocation, so checks must be carried out over the planning, implementation, design and maintenance processes for error-free transition with the expected results.

Recognize the Development Scope By Migration: Migration is a mechanism for enhancing current data. It is a scope to find and develop usable content and transfer material from the source to the host system. The aim of the process should then be to upgrade the improvable areas of the material.

Clear Communication and Coordination within Teams: Data transition involves the IT teams; it affects the whole company and its objectives. It is therefore important to include the market viewpoints and to enable all teams to participate from the proposal, method, technical execution to the desired outcome of migration.

Implementation of the Risk Management Strategy: Risk management must be considered during the process of data migration. Several issues could potentially arise. Compiling them and coming up with ways to either solve them by going ahead will make the process effective. Consider depreciated data values, user testing, protection, app dependency, etc.

Using Right Tools: using the appropriate equipment during data transfer will fasten the procedure and make it more efficient. You could use data discovery, quality validation, profiling and testing tools.

Agile Mindset: Agile thinking during data migration will help you maintain high data quality due to frequent testing, remove errors as they occur, and make the development process more straightforward overall. It will make the costs & schedule more predictable, as it requires a clear division of tasks & responsibilities and timeframes.

Conclusion

Data migration is a crucial part of the development and growth of any business that contributes to the growth of the business process. However, in the absence of a strategic approach, there may be more destruction than benefits. Data is an essential asset for any business to expand; therefore, it needs to be carefully addressed.

In order to reap the benefits of this process, proper planning and certain techniques must be followed for risk-free migration that will be cost-effective and less clumsy. It’s a great way to upgrade, build, and gain more ROI, too.

Contact us today and get consulting services for better Data Migration to the cloud.

Comments

Post a Comment

Popular posts from this blog

Big Data: A Stairway To Heaven Or A Highway To Hell?

  Are you eager to get big data? Slow down and learn about the issues that can emerge when designing a big data project. As a data consulting services provider, Platingnum will help you overcome these issues and harness the full value of big data. Big data poses not only many challenges, but also severe threats. And here’s the difference between them. If you go to a store and a big puddle falls in the path, you might choose to walk past it or climb over it. If the puddle is too big, you would ask others to assist (turn to ‘puddle consultation’). After that, experts will clear the puddle. This is a challenge: an obstruction in the path that can be resolved very quickly. But later, you know that you need to walk through a bad neighbourhood to get to the store. Then it’d take too long to drive around so the store will close when you get here. It is a more basic topic that can trigger problems. Obviously, these walking issues cannot be compared to big data issues, but the idea is also the
Read more

Top 7 Big Data Challenges And How To Solve Them

Image
  Before going to war, each General has to research his opponents: how large their army is, what their weapons are, how many fights they have had, and what key tactics they use. This expertise will make it possible for the General to devise the correct plan and be ready for war. Much like that, before going to big data, any decision-maker needs to know what they’re working with. Here, Platingnum addresses 7 global big data problems and proposes their solutions. Using this ‘inside knowledge,’ you’ll be able to take down frightening big data monsters without letting them defeat you in the fight to create a data-driven enterprise. Obstacle #1: Insufficient Knowledge and Recognition of Big Data Companies often do not really know the basics: what big data is, what its advantages are, what technology is required, etc. Without a simple understanding, a large data acquisition project risks being doomed to failure. Companies can spend a lot of time and money on items they don’t really know how
Read more

What Is The Quality Of Your Big Data? Dirty, Clean, Or Cleanish!

Image
  If you think that with big data, you’re going to cast a spell and easily improve your company, take off your cloak and throw away your wand, because big data isn’t magic. But if you roll up your sleeves and do some sweeping, it will do a trick and help you produce a spectacular business outcome. Big data is strong and powerful, but not too perfect.  This article  reveals that it faces many problems and that data quality is one of them. Many companies understand these challenges and resort to big data services to deal with them. But why precisely do they do so, if big data is never 100% accurate? And how high is the output of big data? That you’re going to find out in this article. What if Your Big Data is of bad Quality? Relatively poor quality of the big data can be either incredibly dangerous or not as serious. Here’s an indication of this. If your big data platform analyses the behaviour of the user on your website, you would, of course, want to know the real state of things. And
Read more