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With the rapid development of digitalization, data has acquired the status of the most valuable asset. Working with data is an integral part of information collection for business intelligence, customer transaction processing, and application support. 

But data never stops evolving and developing along with customer and software demands. Data migration is employed to maintain work continuity and the use of up-to-date technologies. Well-configured data management systems provide reliable information processing solutions and boost overall business success in data-driven companies.

However, incorrect implementation of the database (DB) migration process leads to critical data deprivation and decreased productivity. This is especially relevant when simplifying a data migration plan. This article reviews key stages, processes, and approaches of how to perform data migration from one system to another.

What is Data Migration?

Data migration refers to the process of transferring data from its original environment (such as databases, data warehouses, or other storage systems) to a new destination. This procedure is critical when implementing new software solutions, systems, or storage facilities. Typically, data migration is necessitated for system upgrades, replacing outdated infrastructures, or augmenting existing data storage with advanced tools and enhanced capacities—for example, data science. You can learn more about data science services here.

Today, data migration projects begin with companies transitioning from local infrastructure to cloud structures, as well as a focus on application optimization. The most common data migration examples include the need to update and create new storage, freshen up data, perform a complete overhaul of the data system, replace and upgrade servers, migrate to the cloud, relocate a data process center, update software, etc.

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Data Migration Strategy

This requires a robust data relocation strategy with minimum waste. A successful transfer decreases the possible risk of redundant and inaccurate data. Such problems are probable even when the original components are viable. But with the wrong approach, source data issues can be exacerbated. This is why it is so important to define your business metrics and select the proper data migration methodology.

There are two basic data migration strategies: “Big Bang” and “Trickle.”. Let’s look at these two approaches.

Data Migration: Strategy, Process, Types, and Key Steps

Big Bang Method

This method is about transferring all the data in one go. Such a strategy is affordable and offers fast, facilitated migrations. But all production systems must be turned off when the specialists migrate the data. There is also a risk of malfunction when relocating a large volume of components. 

The combination of these factors makes this strategy the best choice for small organizations with little data to move and the ability to run workflows offline for a certain time during the transfer.

Trickle Method

This strategy performs a gradual migration, with both the old and the new systems operating simultaneously until the transfer is complete. A step-by-step transition breaks the process down into phases, and components are transferred in small portions. This method lowers the risk of malfunctions and errors and does not provoke entire system downtime.

The Trickle strategy takes time to accomplish all the movement steps and is generally more complex. This approach is appropriate for large businesses that operate big data and cannot afford any delay or downtime.

Data Migration Process

The transfer and data integration should be seamless and fine-tuned since low-quality data seriously affects primary work operations, which leads to undesirable results.

Our migration team works according to the general scheme, but the process is developed based on your business specifics. With a competent data migration plan, companies will not go beyond budget and will not face an overload of data processing operations.

Data Migration: Strategy, Process, Types, and Key Steps

1. Estimating the Current Environment and Transfer Format

Before planning your migration, our team helps define the existing data format, prioritize types of data to be moved, and settle with the post-migration format. This pre-planning phase allows you to identify probable risks, which our specialists help to anticipate before the transfer and provide the necessary security measures. This step is important as it keeps your data free from critical errors during the actual transfer.

2. Planning

An essential stage that defines a data migration plan and outlines all the underlying specifics. Experts assess the scale of your project. To do this, they match the existing system with the new one and define the places that should be adapted and optimized. They also designate the inferior limit data amount required for the new system to start working productively. After that, IT specialists determine the transfer strategy that suits your specific needs.

Our team establishes a real budget and timeline. This step helps set the project’s practicability. Knowledge of data amount and network bandwidth assists in determining proper project terms. NIX engineers help to plan migration time, namely by starting the process during non-working hours to completely protect your business from downtime.

There is also the question of transfer tool choice. When moving big data, efficient automation must be achieved. Usually, experts help you make a proper decision whether to use a readymade tool with pinpointed functionality or implement a custom tool with tailored features.

3. Data Validation and Preparation

Next, our team starts auditing data to make sure it’s ready for migration. The selected data is checked in detail for duplicates, collisions, missing fragments, incoherence, and data quality issues. This step of the data migration process is laborious and is frequently covered by automation.

However, all the problems that can damage data during the transfer or lead to component loss cannot be foreseen. For this reason, our engineers provide additional security protection and backup of all data, especially portable components. In case of any issues, you can fully restore your data.

NIX specialists assist in configuring and setting access rights and roles. This is how your data management structure is created. Every employee in your company will know their role, thereby avoiding delays and safety issues.

4. Migration Engineering

The transfer scheme defines the rules for data migration and verification. Few technologies can be used for data relocation, but the preferred option is to Extract, Transfer, and Load (ETL). This technology is especially important with a complicated, intense data flow. Our engineers create custom component transfer scripts for such cases.

The scheme also determines whether the transition will be content-driven or metadata-driven. Metadata depicts source data location (file and column name) and each location type’s characteristics.

5. Migration

Our team extracts data from the old environment with the proper system permissions. They determine the components that should be cleaned up to secure the target system. After that, they convert them to the needed format. The deduplicated and cleaned-out data is then uploaded to the new environment. Specialists monitor the data migration process to identify and repair any issues.

Components are extracted, converted, and loaded. Using the Big Bang strategy, the transfer can last a couple of days. With the Trickle method, it takes much longer, but with minimal risk and no downtime.

6. Test

Testing is performed at all planning, migration, and post-transition phases. With the Trickle method, our engineers check each piece of data migrated to fix issues. Constant testing guarantees secure data transfer as well as compliance with the demands of a new system and high-quality data standards.

7. Post Analysis and Maintenance

Data migration is not the end point of a transfer to a new system. Once the host system is up and running, our experts check and monitor performance over the long term. They run a full system check to make sure the data is of good quality. Such audits are carried out frequently to understand whether the system covers the entire data volume and meets the desired quality and customer satisfaction levels.

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Data Migration Types

Optimizing systems through migration helps businesses accelerate growth, decrease capital expenditures, pay only for the services used, and increase business agility. There are six types of data migration and often data of several kinds are moved together. Let’s take a closer look at all relevant data migration types.

Data Migration: Strategy, Process, Types, and Key Steps

Storage Transfer

Storage migration involves data transfer between various storages while the paperwork is digitized. The ultimate goal of moving storage is to improve the hardware and scale the technological base. The data is first moved to hard drives, then to hardware, and then to virtual storage.

This is sort of an “express modernization” in storage technology where data formats or contents are left unchanged. Keep in mind, however, that for large companies, it takes a long time to move storage.

Data Center Transfer

Data center (DC) transfer is when the whole data infrastructure is migrated along with all important applications to a new local location. It can also be a transfer from the old infrastructure to new hardware in the same local location. DC includes storage devices, network routers, data imaging equipment, switches, servers, and computers.

It’s important to take into account data migration timing at the planning stage since a large-scale transfer can take up to several weeks even with fast-moving networks. When migrating a data center, precautions must be taken in cases such as replacements being expensive and being very sensitive to changes.

Database Transfer

In DB transfers, the elements are structured, and the DB management system (DBMS) governs database organization and storage. In a nutshell, a database transfer is a transition from a current DBMS to a target one or a DB update. Migration to a new environment is more compound, especially if the old and new environments use various structures.

Cloud Transfer

Cloud migration is when applications and data are moved from a local environment to the cloud or between varied cloud structures. This can be referred to as storage transition to the cloud. More and more enterprises are beginning to work in the cloud due to reinforced security, convenience, flexibility, cloud data analytics capabilities, and the opportunity to pay as they go. Moving to a virtual system lowers hardware and maintenance expenses.

Data transfer periods depend on data volume and differences between the old and new storage locations. The migration period can take as little as 30 minutes or as much as a year. It all depends on the project’s complexity and scope.

Applications Transfer

Applications are moved to upgrade the software or switch vendors. Data is moved from the old computing system to the new one. However, the new system usually demands a lot of transformation. This is where the migration problem comes into play. Both infrastructures have their out-and-outer models and support various data formats. This is where web interfaces help to facilitate migration processes and APIs help to maintain data integrity.

Organizations are constantly updating software and increasingly modernizing applications to maintain and improve their competitive edge through data migration plans.

Business Operation Transfer

This transfer type is used in cases of business acquisitions and mergers, as well as reorganization or modernization to reach out to new markets, solve relevant business issues, or remain competitive. Business process migration relocates operations, services, and client data—as well as applications and metrics—to the new system.

Data Migration Tools

Let’s look below at specific solutions with which you can perform data migrations—we’ve divided them into groups to demonstrate the possibility of implementing different approaches to this procedure, from traditional database management to modern no-code cloud platforms.

  • Relational database management systems (DBMS). To implement this approach, tools such as Microsoft SQL can be used to manage relational databases and facilitate access to them.
  • Data integration and ETL services. Here, you can use tools like Oracle Data Service Integrator, Xplenty, Talend Open Studio, and Matillion, which take on the task of extracting data from various sources, transforming it according to operational needs, and transferring it to the final digital solution.
  • Cloud-based migration services. Popular platforms such as AWS Data Migration and Azure DocumentDB deserve attention here—with their help, you can migrate databases and workloads to cloud environments.
  • NoSQL and JSON support services. Most importantly Azure DocumentDB, which lets you get the most out of NoSQL and native JSON support.
  • Unified data integration platforms. These include Astera and CloverDX, which are comprehensive no-code and low-code environments for data integration.
  • Operational ETL and data movement platforms. These include and Fivetran, which can scale business processes using ETL operational capabilities.
  • Data pipeline and warehousing services. Leaders in this group are Hevo Data and AWS Database Migration Service (DMS), which provide integration of data from multiple sources into data warehouses.
  • Specialized migration and conversion tools. As an option, we suggest you consider IRI NextForm, which allows you to implement various data migration scenarios and databases with conversion capabilities.

Data Transfer Key Steps

Data migration steps point out important transfer aspects that help avoid the risks of data integrity deprivation or redundant data appearance. If you don’t consider these migration stages, your business may experience corrupted files, costly downtime, compatibility issues, etc.

Data Migration: Strategy, Process, Types, and Key Steps

Assessing Data Volume

The complexity, timing, and migration budget rely on the amount of data to be moved. It’s necessary to define the resources and technologies that will be used. To do this, an extensive analysis of the old and new systems is carried out to find out whether the transfer interferes with operations or there is downtime. This defines migration time.

Your enterprise needs control and understanding of processes with the involvement of experienced employees. Our team performs a high-quality data transfer and preservation of post-migration data integrity.

Selecting a Proper Strategy

As described above, there are two main strategies, and you should make your choice based on your business characteristics. Small companies can withstand downtime and usually need to migrate less data, so they benefit from a quick migration. Large companies should migrate gradually using the Trickle data migration steps. This allows for avoiding downtime and damaging workloads.

Bringing Data to the Required Formats and Models

When moving to another system, you need to take into account its features. Based on common data migration examples, only data can be moved from local storage to the cloud or hosting applications can be switched. For a holistic transition of all types, you need to change and transform the data so that it fits the new environment and functions properly.

Creating a Backup Before Migration

Our specialists copy your data to the backup storage regardless of the transfer method. Such insurance does not slow down the data migration project and does not put your business at risk.

Testing Each Stage

Some companies treat data migration lightly, with little due diligence. However, the transfer is carried out once, so it’s important to do everything correctly. Engineers break the data into subcategories and build one category at a time, and then test each one. When an enterprise conducts a large-scale transfer, specialists execute category creation and its testing at the same time.

Data Cleansing

A large amount of data requires a high budget and a long time to transfer. Our team assists to review your data and determine which ones need to be migrated. This is how the data is cleaned and it includes the removal of outdated information that is no longer useful—our specialists correct incomplete entries and delete duplicate entries.

Engineers also review the data architecture. They remove unnecessary tags, fields, objects, and other elements. In addition, specialists fully test the new system so that the shortcomings and unproductiveness of the old system are not transferred to it.

Exploring Source and Target Environments

Our team determines how to integrate the components into the new system and checks the data types, quantity, and structure. After that, experts review the new system and determine which entity and field fit into the target environment. After that, it’s already possible to confidently carry out migration through properly selected tools.

Data Migration Risks and Challenges

Generally speaking, the subject of our article’s research is quite complex, so businesses that have only heard about it in basic terms are afraid of its implementation. Specifically, there are a number of risks and challenges that they may face (which will be discussed below), but with the cooperation of an experienced data migration team, all of them will be overcome.

Data Loss

Cases when some part of the data is lost during the transfer are not so rare, and this often happens irrevocably. Because of this, businesses have to plan backup strategies in advance so that, if something goes wrong, data can be restored from migration-independent storage.

Errors in Data Semantics

Even if you don’t encounter the previous problem, you’re still at risk of errors in the semantics of the migrated data—that is, it may end up being placed in columns used for other purposes. To prevent this, you will need a comprehensive testing strategy to ensure that your business data is being used correctly after migration.

Extended Downtime

Some migration approaches involve downtime of business-critical services and applications, which can ultimately result in large losses for companies. Moreover, due to this, the collection of new data may be suspended, which can also cause significant harm to business operations. A fairly obvious solution to this problem is to choose a migration strategy that would eliminate or minimize downtime.

Data Corruption

This can happen when you migrate unwanted data types to new storage. To eliminate this risk, you’ll need to first identify data types and sources to ensure that the migration process does not use those that could be harmful to your system.

Reduced Performance of Services and Applications

This risk is usually associated with the use of ineffective migration tools. If you’re looking for reliable ones, you can choose them from the list that we provided above under “Data Migration Tools.”

Data Integration

You may lose previous connections with applications and services that used your business data. To prevent this, your migration team must know exactly which integrations they need to keep.

Data Security

Because companies usually hire third-party teams to perform the migration, they may expose the privacy of their data to certain risks. To get around this problem, you’ll need to specify the security requirements so that the contractor can meet them when performing the transfer. You can learn more about data security in cloud computing here.

Transfer of Poor-quality Data

Since some of your legacy data may be of poor quality (incomplete records, duplicate records, outdated information, etc.), this may ultimately adversely affect the performance of the updated system and lead to errors in its operation. To prevent this problem, you’ll need to develop a comprehensive data cleansing strategy that precedes migration.

Data Migration Best Practices

In addition to recommendations that allow you to circumvent the problems described in the previous paragraph, we’d like to share with you five additional insights that we’ve obtained from our own experience:

  • Combine migration with the ability to improve data quality by holding it to higher standards and compliance requirements
  • Instead of entrusting the migration procedure to an in-house team, choose third-party specialists with extensive experience in it
  • Try to independently minimize the amount of data to be transferred
  • To make better use of the migrated data, consider re-architecting your IT system 
  • To avoid downtime, make sure that the previous version of the system continues to function during the migration 


Data retention is a top priority when migrating to another environment or upgrading it. Companies need to migrate data to ensure the stability of workflow operations and introduce advanced technologies into the business.

The migration process can be complex, involve many aspects, and range in types of data migration tools—which, if misconfigured, will result in data loss and lower productivity. Our engineers help you choose the right strategy for your enterprise, outline an adequate budget, and estimate the migration timeframe by analyzing the state of your system and the target system you plan to migrate to.

The NIX team offers data migration services, following the industry’s best practices and global standards. Our expertise encompasses a wide range of tasks, including setting up new storage spaces, seamlessly transferring data across diverse environments, updating software, optimizing source systems on a large scale, and ensuring compatibility between different data formats and models. Contact us now for details.

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