What is data migration and how to get it right?

Upgrading or replacing legacy, often decades-old, software and database systems

Consolidating business data from multiple, disparate sources into a centralized repository to eliminate data silos and gain a single 360-degree view of enterprise-wide information

Business restructuring and expansion, such as mergers, acquisitions, or divestitures, that might require data consolidation or segregation

Moving to a cloud-based storage to achieve scalability and security and reduce costs related to on-premises data storage

Adopting new technology, such as big data analytics, IoT, ML, and the like, that require different data storage and processing capabilities

Maintaining compliance with an ever-increasing number of data privacy laws and regulations, for example, localizing regulated data before it leaves its home nation according to the data localization law, or relocating data due to changing residency rules

Thorough strategic planning is key to a successful data migration project. It usually starts with assessing the existing datasets and putting together a clear plan — you should have a precise understanding of what data needs to be migrated, where it needs to go, and how you’ll get it there. The planning stage might also involve the following steps:

• Examine the source data and identify the data format, its location, structure, and attributes

• Opt for a fitting target storage solution and analyze the destination system to figure out whether the source data fits in with the new environment and what needs to be restructured to fit the specification of the destination

• Choose the most suitable data migration approach (Big Bang or Trickle)

• Allocate best-fit resources, set budget, and define data transfer timescales

Prior to data migration, it’s mission-critical that you perform a complete audit of the data to be moved. Data auditing is aimed at detecting data quality issues, such as duplicate records, inaccuracies, or inconsistencies, and troubleshooting them before going ahead to ensure that only high-quality data is transferred to the new system. This is where turnkey data quality solutions may come in pretty handy.

Identify and delete unused or outdated objects that do not need to be in the new system. Removing stale data can make your migration smoother, while also allowing your team to work with a clean dataset post-migration.

Though technically not obligatory, backing up your data, preferably in multiple locations, represents the best practice when implementing migration. This will provide an extra layer of protection in the event of a migration failure.

Here is where you detail the migration process — i.e., set up the destination environment, perform thorough data mapping, define the migration and testing rules, write acceptance criteria, assign migration roles and responsibilities, and specify data migration technologies and methods.

As for the latter, there are several data migration methods that allow transferring data from the source to the target system. Examples are physical storage migration, backup and restoration, 1:1 copy (batch EL) or the ETL technology (in short, standing for Extract, Transform, Load), and others. As for data migration tools, some of the most common ones are AWS Database Migration Service, Azure Data Box, Apache NiFi, or custom Python scripts for specific and complex migration needs.

This is where the migration actually takes place. A robust data migration process requires regular testing to ensure that the data is being transformed and loaded as per the specifications. As the data moves, it is critical to test and retest the migrated data to verify its completeness, accuracy, and reliability. Frequent or continuous testing is absolutely necessary to see if there is any sign of failure and downtime to the source system and rectify issues ASAP.

After the implementation has been completed, it is crucial to run an audit of the migration results to validate that the data has been safely moved to the target infrastructure and is complete and viable. Once the new system is live and running faultlessly, you can safely decommission the old environment.

It can be quite challenging to achieve business continuity when it comes to data migration, as organizations have to balance the need for data integrity and the requirement to keep systems up and running. This is particularly true for companies generating large amounts of data that can’t afford any downtime. While there is unavoidable yet planned downtime, as is the case with the Big Bang data migration approach, your business processes can unexpectedly come to a halt due to transmission failures, application performance issues, or a host of other emergencies you failed to plan for at the initial stage.

Budgeting has the potential to make or break your data migration initiative. It is the underestimation of costs that places data migration projects at risk. If you fail to factor in all aspects of data migration implementation, including hidden indirect costs, such as those associated with unplanned downtime or emergency, you may find yourself in a situation where you are unexpectedly moving far beyond the specified budget. As Gartner states, for data migration projects, cost overruns average 30%.

Data fields in a legacy system may not be in sync with those in the new system due to differences in database architecture. So, simply trying to map the fields and jam the data into the target system may take its toll. Incomplete or inaccurate data mapping may lead to certain data elements being placed in incorrect fields, which might require significant time and effort for regular updates and field remapping.

Ensuring legal compliance and securing sensitive data during migration adds complexity to the project. When dealing with clients’ personal data, you have to understand and seek ways to adhere to privacy and data protection regulations that vary across regions. The thing is, in the United States, there is no comprehensive federal data protection legislation. Instead, regulations differ widely across states and industries. In contrast, in the European Union data is protected by the General Data Protection Regulation (GDPR). This unified framework of data privacy rules imposes strict obligations on data holders and prohibits the transfer of personal data to third countries lacking adequate data protection measures. These transfers can only occur if the European Commission has issued an adequacy decision.

Consequently, seeking ways to prevent GDPR violations becomes a top concern when it comes to transatlantic data flow since these violations can incur sanctions, as was the case with the tech giant Meta, which was issued a record-breaking GDPR fine of 1.3 billion US dollars – the largest in GDPR history.

Large-scale data migrations create a whole universe of change all at once, which is always frustrating for system users. Being accustomed to running queries on existing databases, users may have a hard time adapting to the new environment and changes in data formats, while also showing resistance to change.

Plan for disruption to minimize downtime or mitigate its impact in case it happens. Yes, you heard it right. You certainly want to know how you can keep going under any circumstances, don’t you? Hence, building a robust disruption-ready strategy is key. Coming up with a concrete business continuity plan outlining a range of disaster scenarios and ways to recover is a surefire way to protect your business operations from prolonged disruptions and get back on the rails in the shortest possible time. As regards inevitable downtime, scheduling it properly at a time convenient to the organization is a great way to ensure seamless data migration, while minimizing the probability of unexpected issues or unplanned slowdown.

Estimate data migration costs accurately, while laying emphasis on potential hidden costs. These include costs of managing application dependencies, hiring external contractors, running additional testing cycles, and addressing data quality issues. Running duplicate versions of the same system, as well as productivity losses and post-migration issues can also significantly contribute to the costs. Collectively, these factors add up to budget overruns in the long haul.

Before writing mapping scripts, it is essential that you profile all source data to identify its structure, quality, and relationships. Performing comprehensive source-to-destination data mapping before data loading is a critical step to ensure that all data is accurately placed.

When migrating sensitive data, prioritizing data security and privacy considerations becomes mission-critical. Ensure that sensitive data is handled securely both in transit and in its new environment. You may want to apply data encryption, anonymization, or masking techniques to safeguard sensitive data throughout the migration process. Furthermore, make sure you align data migration with relevant data protection regulations, such as GDPR or industry-specific guidelines.

Though often overlooked, customized user training based on roles and responsibilities can make a world of difference in your data migration process and results. Allocating adequate time and budget for reskilling existing teams contributes to a smoother transition during and after data migration, ensures user acceptance, and helps minimize operational disruptions. It is a good practice to initiate communication about the upcoming data migration and hands-on training sessions early on to give users an opportunity to embrace change well before actual data migration takes place and become well-equipped to better understand and operate within the new environment.

Assess, understand, and justify the need for migration to a new tech, rather than joining the bandwagon in a rush — you should have a clear vision of what you want and why you want it. What are the benefits you get?

Create a proof of concept (PoC) — try on a small scale first and test the waters before fully committing to data migration

Explore alternatives and assess the risks and benefits associated with each option. What are other technologies that do the same job? Why did you decide on this one?

Assess the limitations of the new technology. For example, stored procedures, common to Oracle and many other relational database management systems (RDBMS), may not be available in the same form in cloud-based massively parallel processing (MPP) data warehouses.

Assess the need to rewrite the data processing logic

Assess how your users can be affected and consider creating a single point of contact for your customers and employees to help deal with whatever challenges come their way