Understanding Data Migration and its 4 common challenges  

Today, businesses generate data in a very high volume. These data are crucial for an organization to gain insights and make the right decisions.
Most often these data are found in different forms on different platforms within the organization, making it difficult for people to access and utilize it productively. This is where data migration plays an important role.
In this blog we will look into what is data migration, its importance in businesses, and the key challenges that causes delay in data migration projects.

Data Migration and its importance

Data migration is the process of moving data from one storage system to another computing platform. The process involves basic steps, which are selecting the data, preparing it, extracting it, transforming it, and finally loading it to a newer platform.

Businesses are looking to migrate their data for various reasons, among that one of the main reasons is for data analytics. Migrating data from different sources to a common computing platform is necessary before processing it further. Data migration helps organizations to look into data more closely which is often overlooked because of the data dispersed across various platform.

Other reasons include 

a. Reducing storage/operational costs.

b.Making data integration with modern technologies easy.

c.Improving the overall operational efficiency.

d. Upgrading database management system for better security & scalability
e. Making data more accessible and secure.

While there can be many reasons for data migration, the approach may vary from business to business on how they want their data to be migrated. For example, either choosing a fully cloud-based solution, keeping the data on-premises, or a combination of both, called hybrid solution.

According to a recent study, over 67% of decision makers prefer hybrid solution, because of the challenges caused in cloud solution, i.e unexpected costs, security challenge, slow data transfer etc.

Source-intelligentcio

The tools or solutions that support the data migration process includes Cloud Based solutions, where the data is migrated to the cloud storage. The data is stored and processed remotely through a third-party provider. Some of the examples are AWS, microsoft Azure, Google cloud Platform, etc. The other one is Hybrid solution – Hybrid solution includes both cloud and on-premises data storage.

4 Key Challenges Faced During Data Migration

Data migration involves 4 major steps, which are planning, implementing, and validating it. Before starting with the data migration, it is important to lay down a plan that involves assessing the data quality, the impact on business, etc. A clear approach of assigning roles, setting the budget, implementing tools, designing etc needs to be dome. Once the planning is sorted the very next step is to implement this via different solutions according to the needs. The last and the final step is validating. Validation phase checks if the data migrated accurate before deleting the old system.

Despite following these steps, a successful data migration may run into different challenges causing delays in the migration process. Grossly there are four major challenges in data migration process. These are

1.Volume Of Data

2.Velocity

3.Variety of datasets

4.Veracity

Large Volume of Datasets: Data quality while dealing with large volumes of data can be a major challenge. During data migration these extremely large quantity of data can lead to performance slowdown, leading to an increased migration time. Moreover, while transfer this large volume of data can overwhelm the system receiving it. It can cause system crash, data mess up etc.
As a solution using data migration tools that can handle large datasets would be a viable way.  Another way is to plan the data migration in increments, called as “incremental migration strategy”. This helps in transferring data in smaller batches over time.

Velocity of Data – This refers to speed at which data is generated. With the integration of real-time analytics and Internet of Things (IoT), data is generated at an high speed. This continuous generation of data will cause interruptions during data migration. When large volume of datasets is migrated at a high speed it may lead to system slowdowns and crashes. Additionally, it is important to maintain a sync between the old and new system while migrating data. If there are new updates occurring in the source system, while the migration is in process it might lead to discrepancies

For example, if the bank is transferring the customer transaction data while the transactions are still processed, there is a high risk of data duplication.

Variety of Datasets – This is another challenge with big data, there is diverse and variety of data that is generated. It could be of various forms such as, structured, (data stored in databases), semi-structured (XML, JSON) and unstructured data (text, images, videos etc). When migrating data from different sources with varieties in data, may lead to incompatibilities between data. For instance, if the data is migrated from two different systems, one using XML and other JSON can become problematic. Additionally, it leads to increased migration time due to collecting data from various sources, mapping, and then migrating it.

Veracity of data – Veracity refers to accuracy and reliability in data. There are several challenges posed due to data quality while migration. If the quality of data is low, inconsistent, inaccurate in the source system, then this will be affected to the new system as well. The inconsistency in data needs to be validated before migrating it to new system, as it may result in errors.

As a solution, the organizations need to validate and conduct a thorough data quality assessment. If the wrong data are transferred to the new system it will cause delay for any projects to get started. Robust data validation rules and data verification tools need to be implemented before planning to migrate data.

Data Migration Tool

There are several data migration tools available in market, both cloud and open source:

Conclusion

Data migration is not just a technical necessity but has a key mandate for business success. As data is generated largely from startups to evolved businesses, they are looking for solutions to keep their data intact. Data migration has its own benefits, such as easy accessibility, security, cost-effectiveness, etc., but it also comes with its own challenges. Keeping these challenges in mind, organizations can create a well-structured plan before thinking of migrating data. A strategic data migration approach not only helps in successful transition but also becomes a long-term investment for business.

Prescience Team

Prescience Team