Did you know that 38% of data migration projects fail? This is an astonishing statistic, especially to the many data-driven organizations we have today.

To grow and thrive, today’s businesses must leverage data as a vital instrument for making strategic business decisions. They depend on data to create brand awareness, develop products, etc.

Put simply; data makes the world go round! But transferring data online can be tricky. This is especially true if you’re not sure what data migration mistakes to avoid.

6 Common Data Migration Mistakes

We’ll walk through six data migration mistakes that data professionals commit and how you can fix them.

  1. Undertaking Data Transfers With Unclear Data Specifications

Because data transfer projects have high stakes, experts must understand the data specifications. This helps them avoid data migration mistakes that arise from misinterpreting data documentation.

Data not properly documented can lead to data loss and data integration problems (e.g., duplicate records).

Make data specifications detailed and easy to understand. This way, data professionals don’t misunderstand data designations or data fields. For instance, “Full Name” can be a great data field (e.g., for an employee), but it can also be a data table column header (e.g., in an SQL database).

Complex data transfer projects require getting clarification from knowledgeable data stakeholders. And this should happen at both the source and target systems.

  1. Failing to Segment Data in a Data Warehouse

Business data is rarely homogeneous. Instead, data often comes from various systems and sources. It includes data elements such as names and addresses with different data formats.

Data professionals should segment data at the source before transferring it online. This way, they can understand the data’s structure to ensure data migration success. Segmentation helps you avoid data migration mistakes such as:

  • Mismatches in Data type
  • Data formatting problems
  • Data completeness issues

Segment data as you receive it from the source. This way, data migration processes run smoothly and without glitches.

It also reduces data integration time because data professionals only need to import mapped data once. There’s no need to repeat the process multiple times for different data elements.

  1. Using Poor Tools When Migrating Business Data

A poor data transfer tool can be a deal-breaker when transferring data online.

Data migration tools not equipped to handle certain data types or volumes can result in data loss.

For instance, the lack of data filters for applying data scrubbing rules may cause data corruption. The lack of data cleansing capabilities could lead to incomplete and inconsistent data records.

Data professionals must choose a robust data transfer tool to avoid these mistakes. These tools should come with flexible features such as:

Powerful mapping engine. Capable of mapping any source application structure into the target system. And this is without manual coding. This helps detect and fix errors during the mapping phase.

Flexible configuration options. Allows users to control all the critical components that influence successful mappings.

Comprehensive data validation capabilities. Data validation rules should match data from the source system. Data scrubbing can help detect data quality issues before they become data integration problems.

  1. Missing Data Standards or Best Practices

Data standards ensure that data is treated consistently throughout processing and storage steps. This reduces the risk of inconsistent treatment among different applications and systems.

It also helps reduce costs by simplifying integration processes. Business users don’t need to spend as much time converting different data formats into a consistent format.

Data transformations, imports, and exports are activities that require standards to ensure integrity. Data professionals must know what data quality issues their organization needs to address. Then they can choose data transformation methods that help solve particular data problems.

Data types and structures should match your organizational and industry standards for consistency. This reduces the risk of experiencing data integration problems due to bad practices. For example, all application systems in a health care facility use the same patient ID for each patient information record.

  1. Transferring Sensitive Data Without Encryption

You should encrypt sensitive data during transmission to keep it safe from unauthorized access.

Encrypted sensitive data can only be decrypted by the right people with the right permissions. Encrypting data ensures that only authorized users have access to that information. It also removes any risks of sharing sensitive data online.

Data professionals should encrypt all types of sensitive data before sharing it over the internet. Sharing data online requires a secure connection to prevent unauthorized access from hackers.

A hosted solution is preferred because it protects encrypted data at all times. Whether in transit or temporarily stored on the hosting servers. A regular in-house system may not meet encryption standards for sharing data online. The hosting company’s security standards can be more flexible than your organization’s standards.

  1. Failure To Compress Data

Data compression reduces the size of managing digital documents and files for easier storage and transmission.

Many organizations are still not using data compression due to challenges managing it. It requires investment in time and energy because you have to compress each file type manually. You also have to ensure that the process will not affect their quality.

However, this is a simple task that you can complete with a Mac. Check out this guide on how to compress a video on mac.

Avoid Data Migration Mistakes

These are the six most common data migration mistakes that business users should avoid. They can lead to high costs, errors, delays, and other issues when managing your data. Data professionals are uniquely positioned to help organizations effectively manage their digital data.

We hope this article has given you ideas on creating a data migration strategy. Learn more by reading our other blog posts.


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