Simply put, data migration is the process of moving data from one location to another. While the concept is easy to understand by itself, things become a bit more complicated when actually trying to implement data migration within a business.  

In the ever-changing business landscape, data is becoming more important than ever. In fact, in the new data-driven business world, understanding data migration and learning how to implement it is becoming vital among businesses.  While data migration is becoming essential, it’s also a very difficult task to take on alone. 

According to Gartner and Bloor Group:

“More than 80% of data migration projects run over time and/or over budget. Cost overruns average 30%. Time overruns average 41%.” - Bloor Group

“83% of data migration projects either fail or exceed their budgets and schedules.” - Gartner 

While data migration projects can prove challenging, having the right tools makes all the difference. Read on to learn more about data migration, its role in the new world of data-driven business, and how the tools at can help you with all of your data migration needs.

Table of Contents 

Types of Data Migration

There are three primary types of data migration: application migration, storage migration or database migration, and cloud migration. Let's dive into these use cases for data migration here.

Application Migration

Application migration is when you move an application from one storage/server location to a different one. You could be migrating an application from an onsite server to a cloud-based server, from a cloud-based server to another cloud-based server, or you could be moving data from one application to a new application that only accepts data in a specific format.

Storage Migration or Database Migration

Storage migration is when you migrate data from legacy systems or databases into a new target database. Often, these are isolated systems that have become walled-off into data silos (more about those below), and they are moving to storage systems that permit better consolidation and integration across all the information systems that belong to an organization. Migrating data into a more integrated database or data warehousing system offers dramatically improved processing, flexible, and cost-effective scaling. It could also provide advanced data management features like snapshots, cloning, disaster recovery, backups, and more.

Cloud Migration

Cloud migration is the process of transferring data from an onsite server to a cloud-based data warehouse. Of all the use cases, cloud migration might be the most important for large corporate data systems. In fact, Cisco reports that 94% of all workloads "will run in some form of cloud environment buy 2021."

Some of the reasons businesses are leaving their onsite servers for cloud-based data management systems include:

  • Reduce the overhead required to maintain onsite data systems
  • Charge only for the data services that companies need when they need them
  • Offer greater flexibility and scalability
  • Allow corporations to extract cutting-edge machine learning business insights from their data

The Challenges of Data Migration

It's essential to understand the complicated challenges that come with the process of data migration. The main issues you'll encounter relate to:

  • Data gravity and data silos
  • Data security and compliance
  • Data complexity
  • Data loss and corruption
  • Dealing with impatient stakeholders

Data Gravity and Data Silos

One of the biggest challenges of data migration stems from data gravity. Data gravity happens when data attracts other data and applications to it. It refers to the way large data systems become “heavy” (in a figurative sense) and difficult to move.

In some ways, data gravity is good because the more integrated applications are with their data, the more efficiently they run. However, when it's time to move the data to a new target system, it's difficult to disentangle the data from the applications that are using it.

Another gravity-related challenge is data silos. Data silos are isolated, incompatible data formats within a large data system. They develop when an application works with unique data structures that don't communicate with the rest of the system. In many cases, data silos remain isolated but sometimes data engineers resort to jerry-rigged workarounds (i.e., inefficient data pipelines) to integrate a data silo with the rest of the system.

When migrating data in a data silo, you have to:

  • Undo the jerry-rigged workarounds, which can be time-consuming.
  • Figure out new solutions to get the data to integrate with the target system or application.

Data silos have ruined many data migration projects. However, ETL (extract, transform, load) technologies like's data integration tools can help you overcome these hurdles.

Data Security and Compliance

Another difficulty in data migration relates to legal compliance and security. For one, you need to understand and adhere to all compliance-related requirements that apply to data security and data storage in your industry. The GDPR (General Data Protection Regulation) for the EU and the HIPAA (Health Insurance Portability and Accountability Act) are two prominent cases, for example. Legal compliance and data security mean additional headaches when it comes to the data migration process.

To overcome the security risks associated with data migration you may want to work with a migration and storage expert for your industry. These professionals can help with:

  • Data encryption: Ideally, you can store legacy backup tapes—and encrypt and migrate the data to a new media format simultaneously.
  • Chain of custody: Secure unencrypted historical data from point of pickup through completed migration with one documented process.
  • Offsite tape vaulting: Store your legacy source media and newly migrated archive tapes in your vendor's secure facility.

Also, consider the implementation of advanced user management strategies to make sure your data isn't accessible to the wrong people during and after migration. According to a Ponemon study, 62% of employees claim that they have access to data they shouldn't be able to see. Don't let that happen to your information.

Another note on data security and compliance - makes security its number one priority, featuring SSL/TTS encryption, an SOC 2 audit and security penetration test, and compliance to HIPAA, CCPA, and EU Data Privacy and GDPR standards.

Data Complexity

The more data you need to migrate, the more types and varieties of incompatible data you'll encounter. For example, imagine dealing with an old information source system that stored 40-digit long claim numbers in one field, but the new system won't accept numbers this long. 

Dealing with incongruent data like this will require you to transform it into a compatible format. This might involve separating the numbers into smaller chunks that divide them into various parts for the client code, date, region, etc. Of course, you'll have to develop the code—or use an automatic data integration solution—to transform the data like this.

Another complexity happens with an old system that has duplicate information stored in multiple places. Migrating this data requires you to locate all of the copies to make sure you only migrate one copy, and to make sure you store it in the right location. This process is called "data normalization."

To overcome data normalization challenges, you want to have the best data migration tools, like, at your disposal.

Data Loss Or Corruption

Losing even a single record could be catastrophic for your organization. One strategy for preventing data loss and corruption is to know the exact number of records you're migrating to the new system. If the migrated data doesn't match this number, you'll need to investigate why. 

Was it simply because you eliminated duplicate data and everything is fine? Or, did a record get lost, and how do you prevent it from happening again?

Another way to prevent data loss and corruption is to use the automated data validation tools provided by to sample data outputs and ensure correctness and validity. For example, can check whether the client code fields in the new system have the right number of characters and whether the new field types match with the old ones.

When testing and validating data during a data migration, you should:

  • Consider any incidents that might have resulted in corrupt data in the past: Maybe there was a system failure at some point in the past that may have impacted the data quality of certain records. Make sure to test these records specifically during your data migration process.
  • Use large samples of data for testing: In a massive data system, you might not be able to test and validate every piece of information, but you should strive to validate at least 10% to 20% of the information.
  • Start testing immediately and don't stop: Testing is not something to do at the end of the migration. You should verify the accuracy of the migration as soon as possible and continue testing throughout the migration process.

Dealing with Impatient Stakeholders

All the above challenges can seriously delay your project, so it's not uncommon for stakeholders to get impatient. Therefore, CTOs and developers should explain to stakeholders that data migration is infinitely more involved than simply switching a few hard drives or pushing a button to upload data to the cloud. By educating stakeholders about these complexities, they'll be more patient when the inevitable challenges and delays crop up.

Related Reading: 7 Data Migration Best Practices

How Does the Data Migration Process Work?

You may have heard about ETL platforms like which offer data migration and data integration services. ETL stands for the three stages in data migration: extract, transform, load.

Extract Data

Extraction is one of the most delicate parts of data transformation. If you fail to do it correctly, the rest of the processes will fail. During extraction, you may pull data in a variety of formats from the source. These formats might include relational formats like XML, RDBMS, JSON, and flat files. They may also include non-relational formats and more.

During the data extraction phase, you will convert these formats into a new format that will permit you to transform it, which is Step #2. Another element of extraction involves the verification that the extracted data is correct and accurate.

As a laborious and error-prone process, manual data extraction slowed down data developers for many years. Now, with automated solutions like, you can bypass these bottlenecks by automating data extraction. Here’s what a G2Crowd user said about’s data extraction tools:

“ solves the problem of manual data extraction and insertion, and the errors that occur in this process. After configuration, which is a very important step, we realized a large time savings from this manual process. Any errors with our data that arose we knew were on our end, and this allowed for faster problem identification and resolution.”

Transform Data

The transformation process applies specific rules that transform the extracted data. This serves to normalize the data in order to load it into the new target structure (often a data warehouse).

Part of data transformation involves “data cleansing” to ensure that only the right data gets loaded into the target structure. For example, you might set rules that:

  • Ensure that no duplicate columns or duplicate sets of data get loaded into the target.
  • Choose only specific information to load.
  • Divide certain columns into more than one column.
  • Change coded values.
  • Instruct how to sort the information.
  • Perform a wide variety of other functions.

Finally, you might use data mapping to join columns and fields of data together from different sources.

An example of when you might need to transform data would be in the case of migrating data from one application to a new application that requires information in a different schema. You’ll need to transform the data into the right schema before it can integrate with the new application.

Manual data transformations can expend a lot of resources, but automated ETL solutions like offer instant transformations between a vast array of data structures belonging to popular information systems like Salesforce, Facebook, Survey Monkey, and hundreds more. 

Here’s what a G2Crowd reviewer said about’s automated transformation features:

“We needed to connect a number of sources, transform data, and load them into one centralized location fairly quickly with limited bandwidth from our DBAs. enabled me, and one other analyst, to pick up that bit of the workload without a ton of trainingso we were able to meet our deadlines. The speed and consistency of is impressive, and it more than makes up for what a few tools in our kit may be lacking.”

Load Data

In the final stage, you’ll load the data into the target data warehouse or delimited file. This entire sequence could be repeated by automated software multiple times per hour, day, week, month, or year. Certain data warehouses will have rules for organizing the information they are exposed to. For example, some data systems will overwrite existing data with cumulative data at specific intervals. Therefore, make sure you understand how you want your data warehouse to treat new data beforehand, so you can develop an appropriate strategy.

Related Reading: Salesforce Data Migration: Best Practices & Strategies To Make It Successful

How to Prevent a Data Migration Catastrophe

During your data migration process, there will be a lot of opportunities for things to go horribly wrong. For example, imagine accidentally deleting decades worth of data relating to your company. To avoid a catastrophe like this, keep these final tips in mind

  • Backup Your Data

Remember when you forgot to back up your 7th-grade research paper and it got zapped by a power outage? You can't afford a mishap like this when it's your company's valuable data. 

Therefore, whenever you're performing ETL operations, create backups of your resources—and test the accuracy of the backups—before moving forward with the data migration procedure. If a problem crops up, you'll be glad you took the time.

  • Test All Project Phases

You'll have many opportunities to test the various stages of your data migration plan before implementing them. Make sure to do this as it will limit the risk of a data system meltdown.

Data Migration: Only the First Step

Data migration is an integral step in the process - but companies everywhere should know that it's definitely not the final one. Migration is key, but to ensure that you don't fall behind your competition in the overall data picture, companies must leverage their migration success and take things to the next level.

What does that mean? Ongoing, effective, and comprehensive data management. Try using your data migration services as a "jumping-off point" for an entire data strategy by ensuring regular, consistent updates of your new database or data warehouse.

Related Reading: Data Engineering: What is a Data Engineer and How Do I Become One?

How Can Help 

As time progresses, data migration will increasingly become more important to your business. That’s why it essential to equip yourself with the right tools today. Ultimately, the toolkit provides all the tools you need to begin reaping the benefits of data migration today. 

Here's what one user said about the speed of the platform:

"Building ETL pipelines with the speed of light. We could write integrations using python but saved us a lot of time. We wanted to spend more time understanding data; not how to get to it and did that for us."

" helps to speed up the whole process of ETL. Allowing me to do more within the same amount of time, and it supports lots of different platforms."

Are you ready to discover how the platform can help you with your data migration needs?  Contact our team today to schedule a 7-day demo or pilot and see how we can help you reach your goals.