Table of Contents
  • The size of the data
  • The type of data 
  • The state of your big data environment
  • The source and target systems 
  • The operating systems
  • The database types and platforms, such as Microsoft Azure SQL Server, Oracle, or open-source options
  • The data center's location
  • Pricing and budget constraints
  • Scalability
  • Legacy system complications
  • The type of cloud environment

Data migration seems simple from a high-level point of view. After all, you’re simply moving data between two or more locations. In practice, however, migrating data can be one of your IT department’s trickiest data management initiatives.

According to LogicWorks, 90 percent of CIOs in charge of data migrations moving from on-premises to the cloud have encountered problems during this process, with 75 percent missing planned deadlines. 

What’s causing such a high failure rate for data migration projects? It often comes down to a lack of best practices. In this article, we cover seven best practices and data migration tools that can help your organization avoid costly overruns, frustrating complications, and other common issues. 

  1. Setting the Data Migration Project Scope
  2. Establishing a Realistic Time Frame for Database Migration
  3. Analyzing Your Current Data
  4. Cleansing Your Data
  5. Aligning IT and Business Teams
  6. Selecting the Right Data Migration Tools and Vendors
  7. Backing Up Data
  8. Creating a Long-Term Data Management Strategy with Integrate.io's ETL Tool

1. Setting the Data Migration Project Scope

Define the parameters of your data migration project long before you get started. Like many other large IT undertakings, data migration is vulnerable to scope creep. You could end up with goals that far exceed the original plan, which causes delays and adds complexity.

The Journal of Information Security recently published a paper called “Data Migration Need, Strategy, Challenges, Methodology, Categories, Risks, Uses with Cloud Computing, and Improvements in Its Using with Cloud Using Suggested Proposed Model.” It found 90 percent of data migration project specifications change, and more than 25 percent of these projects go through multiple specification changes. 

Start small with migrating data. For example, you can choose a single database to move as a proof of concept. After you’re successful with this part of the project, you can expand your scope. 

2. Establishing a Realistic Time Frame for Database Migration

Overly optimistic time frames can lead to staff burnout, cut corners, and costly do-overs. Choosing a realistic schedule based on your defined project scope sets your team up for success. Large-scale data migrations can take several months to a year. 

When you plan your project, make sure that it doesn’t slow down your existing business operations. Spread it out over time to minimize disruptions and lower the chances you’re overburdening your IT staff with the workload. Add in time for unexpected delays and additional work requirements. 

3. Analyzing Your Current Data

Data migration projects range from simple to very complex based on factors such as: 

Before you start the data transfer, investigate how much data you need to move. Are there fields or records you don’t use that you can leave behind? Do you need to enrich the existing data with another data source? Are there compatibility issues to consider? 

After you take a close look at the data and how it’s used in your organization, you’ll have a better understanding of the right types of data migration strategy. The two most popular options are “big bang” and “trickle” migrations, differentiated by the speed of the move and how many phases it involves. Big bang migrations work best when you can designate a downtime window, while trickle migrations are best for systems that you shouldn’t interrupt. 

4. Cleansing Your Data

Data migration is a GIGO (garbage in, garbage out) process. If you don’t address data quality as part of your migration, then you’ll only move low-quality data from one place to another. The quality doesn’t magically improve once it arrives at its destination. 

When you’re planning for large-scale data migration, it’s an opportune time to deep clean your enterprise data. Remove inaccurate, out-of-date, and duplicate data before the project begins drastically improving your data quality. Use testing and validation checks throughout your migration to identify and correct issues in a timely fashion. 

Data migration tools can optimize the data cleansing process, but you also need to pair up this one-off cleaning with strong data governance. 

5. Aligning IT and Business Teams

Data migration falls under the IT department’s responsibilities, but it affects your entire organization. Getting approval from key stakeholders to align IT and business teams before starting the project is critical to its success. 

The IT department needs to understand the data migration’s overarching business goals, and your business teams should have basic knowledge of the technical challenges involved in the data migration process. 

Both teams need to work together to stop the harmful impact of data gravity and data silos during the migration. Some data migration tools can even empower non-technical users to perform part or all of the data migration themselves, and they offer timesaving automation features.

6. Selecting the Right Data Migration Tools and Vendors

If you don’t have the in-house experience and technical know-how to follow data migration best practices, third-party data migration software and vendors can fill this gap. While cobbling together a data migration solution on your own is possible, given enough time and energy, it can lead to many roadblocks for your project. The transition is much faster and smoother if you have the right technical resources on hand. 

Research data migration tools and vendors to see which works best for your use case. If you plan on partnering with a vendor, evaluate them based on the technology they use and their previous experience with similarly sized data migration projects. 

7. Backing Up Data

Data loss is a significant concern during migrations. Even losing a few critical records during this process could be a catastrophe for your organization. Establish a robust data backup, data replication, and business continuity plan long before you start the data migration. Testing your migration before running it in production is another critical step of data protection. 

Creating a Long-Term Data Management Strategy with Integrate.io's ETL Tool

From strategic planning to choosing the right tools, these best practices will give your project a strong chance of success. However, data migration is only the first step in an ongoing data integration and management process to keep your databases and data warehouses up-to-date with accurate information. 

Integrate.io’s ETL solution makes it easy to set up data pipelines with an intuitive, user-friendly interface and no- and low-code functionality. Drag and drop your way to better data management and access more than 100 built-in integrations and plenty of features to automate your workflows. Try out this powerful data migration tool for yourself with a 14-day pilot.