Data has become a significant commodity for many businesses and organizations. As a result, the storage and management of data have never been more critical, and the usage of data centers, servers, drives, and other internal storage to store data are naturally considered delicate forms of equipment.

Maintaining data efficiently helps organizations to utilize it for their operations better. One of the key ways to efficiently manage information is through a migration strategy. Data migration is the process of copying records from one source and transferring it to another. In theory, this sounds relatively simple. However, there are always significant risks attached to data migration, and it’s a core task that you must handle with care. If you do not execute a data migration strategy correctly, it is likely to fail.

A study by Bloor Research put the failure rate of data migration projects at 38%. The main reason for failure is where there is not enough analysis before beginning the project. 

Through careful planning and execution, your data migration strategy is likely to reduce the level of risk. A pivotal phase within your data migration strategy is data preparation. Data preparation means to prepare, extract, and transform data sets for secure transfer (migration) to the target source. Although it is one of the most time-consuming and laborious stages of data migration, it is a pre-condition that lays the foundation for success.

To prepare your data efficiently for smooth migration, below are five key steps to make this a success.

Know Your Data

The first step is to identify exactly what data you’re looking to migrate. Data can be stored anywhere, such as from CRM tools, spreadsheets, and other internal sources. This may require extensive manual intervention to bring data from various sources, but unstructured data makes the migration process more complicated. If your data is unstructured, you’ll need to perform data cleansing to standardize the data sets and eliminate duplication. Take note of any data retention policies to tidy up your storage requirements. For example, data governed by HIPAA is subject to data retention requirements set by states.

Understand Data Volume

An accurate estimate of the data volume you’re migrating is essential. It’s not enough to account for the tools and systems you’re migrating from, but understanding the amount of data you’re migrating can help determine effort estimation and the amount of time required for the migration process.

Data Cleansing and Validation

Data cleansing is the process of identifying and correcting inaccurate records from a data set, or for a better phrase, standardizing the data. Keeping redundant data sets will increase the timestamp and memory when migrating data across, and your data systems that you’re migrating to may not have enough storage capacity to complete the migration, causing failures. Performing data cleansing is essential to avoid this problem.

Data Profiling

Data profiling comprises analyzing and reviewing data from its source and ensure the data is of the highest quality. Consider it as a plan to retire outdated data and archive it away. As part of this, data profiling involves identifying anomalies, duplications, and what data needs to be fixed at the source. Typically, data profiling would combine the process of Extract, Transform, and Load (ETL) to enrich and migrate quality data to another location.

Assigning Data Ownership

Managing the data itself is important, but it is recommended that you assign data owners. The collection of data from various sources should be governed by responsible persons within the business or organization.  This can reduce overheads in rectifying data issues and eliminate the need for extensive data cleansing for future migrations.


A fundamental pre-requisite before data migration is to ensure that the target sources are adequately prepared to avoid severe issues to the migration process. Once accurate and standardized data is fed into the target system, you’re highly likely to have a reliable data migration strategy that produces little to zero errors to the data or the migration process.