Future-Proof Your Data: Overcoming Migration Challenges

Cloud Data Engineering

To remain competitive, businesses strive to stay ahead by embracing cloud technologies and optimizing their data infrastructure. A successful data migration to Google Cloud can lead to many benefits, such as increased efficiency, higher team satisfaction, and unlock new streams of revenue with advanced analytics and AI. 

In this blog, we’ll look at data migration, shedding light on the common challenges that organizations face. From handling skills gaps when adopting the new platform to addressing security concerns and mitigating downtime, each data migration stage presents unique hurdles that impact its overall success. We’ll look at how to overcome or avoid these challenges and how Datatonic’s Turbo Data Migration Framework ensures a seamless data migration. 



1. Skill Gaps

As data migration involves transferring data from one system to another, it often requires expertise in both the source and target systems, as well as a comprehensive understanding of the data structures and formats involved. 

In this complex process, the absence of teams skilled in these systems can lead to various complications. Without individuals proficient in data mapping, extraction, transformation, and loading (ETL) processes, businesses may encounter difficulties in preserving data integrity, ensuring accuracy, and mitigating potential data quality issues. 

Furthermore, a lack of proficiency in data security and compliance may expose companies to risks, such as data breaches or regulatory non-compliance. 

Bridging these skill gaps requires a multidisciplinary approach, focusing on upskilling, such as through co-development or ongoing training during the migration to ensure everyone has the skills required to leverage the new platform. 

“Datatonic embraces a collaborative approach, taking our clients’ teams on the migration journey with us. By the project’s conclusion, we aim to ensure we leave behind a high-performing team, well-equipped to handle the maintenance of a state-of-the-art cloud platform.” – Tom Pindar, Business Intelligence Lead 


2. Setting unrealistic expectations

Setting unrealistic expectations and timelines can pose a significant challenge during data migration. Eager to get onto a new cloud platform, businesses may underestimate the complexity involved in transferring large volumes of data between systems, leading to overly optimistic expectations regarding the duration and seamless execution of the migration. 

Unrealistic timelines can result in rushed decision-making, and overlooking crucial steps such as data validation, testing, and troubleshooting. Down the line, this may cause data corruption or data loss, or more downtime, creating frustration among end users.

“We have witnessed first-hand the repercussions of setting unrealistic expectations reverberate across projects, manifesting in budget overruns, compromised quality, and employee burnout. These three consequences are deeply intertwined: a hurried attempt to adhere to budget constraints often leads to compromises in quality, while the strain of unrealistic deadlines diminishes team morale. Embracing a realistic outlook on project complexity and timelines will not only ensure the delivery of a high-quality product but also motivate and engage a workforce, yielding long-term benefits for the company.”  – Tom Pindar, Business Intelligence Lead 

To overcome this challenge, it is crucial to establish transparent communication channels, educate stakeholders about the intricacies of data migration, and conduct thorough assessments to accurately estimate the time and resources required for a successful and smooth transition.

With Datatonic’s Turbo Data Migration Framework and years of experience, we have developed a repeatable approach. This includes a comprehensive planning phase and templates that make it easy to understand the timeframe and budget requirements for a successful migration. 


3. Not having a clear understanding of business value

Without a well-defined understanding of specific business objectives and the value that data migration is expected to bring, businesses risk investing resources into a process that may not align with strategic goals. 

This lack of clarity can result in misguided priorities, leading to the migration of irrelevant or obsolete data, and may overlook opportunities for strategic data projects or transformations that could enhance business insights, such as advanced analytics with Looker, or advanced AI + ML. 

Key stakeholders must be engaged early in the process to conduct a comprehensive analysis of business requirements and establish clear metrics for success, to ensure that the data migration aligns closely with the overall business objectives and delivers tangible value to the organization.


4. Minimizing security risks

Addressing security concerns is paramount in ensuring a seamless migration without compromising sensitive information. To mitigate these challenges, businesses must implement robust security measures, including encryption protocols, an access control plan, and comprehensive auditing mechanisms. 

Additionally, thorough planning and risk assessments should be conducted before starting any data migration project, identifying potential areas of vulnerability and devising strategies to address them. By prioritizing security in the data migration process, organizations can safeguard valuable information and maintain the trust of stakeholders and end users of the platform. 

“A single breach of security can have far-reaching consequences, impacting not only the organization directly but also its partners and customers. Datatonic has vast experience in implementing Google Cloud’s wide range of security tools, including but not limited to encryption, identity and access management, threat detection, and compliance controls. These features are designed to address the most pressing security concerns, while providing scalability and flexibility to adapt to changing needs.”  – Tom Pindar, Business Intelligence Lead 


5. Ensuring high data quality

Inaccurate, incomplete, or inconsistent data can lead to a multitude of problems, impacting decision-making, customer experience, and overall business efficiency. These issues are key drivers for migration in the first place. 

During migration, data may encounter compatibility issues between old and new systems, resulting in data quality degradation. As well as this, duplicate records, missing information, or formatting discrepancies can arise, requiring meticulous attention to detail and comprehensive data-cleansing strategies.

Organizations must invest time and resources in profiling, cleaning, and validating data to ensure that the migrated information meets the highest standards of accuracy and reliability. By addressing data quality issues head-on, businesses can optimize the benefits of migration, enhance operational performance, and make informed decisions based on new trustworthy data. This can then be enhanced with analytics and visualization, using tools such as Looker. One example of this is ASOS, which uses BigQuery and Looker as its marketing analytics platform.



Migrating to Google Cloud can unlock rapid value for your business, providing better access to crucial data to inform decision-making. It also helps to prepare businesses for advanced analytics and AI to further increase efficiency and enhance customer experiences, opening the door for Generative AI. By understanding the challenges of data migration, companies can plan ahead to prevent any issues.

Datatonic’s Turbo Data Migration ensures a seamless migration to Google Cloud, taking necessary planning measures to minimize risk, and quickly addressing challenges as they arise. Read about our Enterprise Data Warehouse experience, with Pets at Home

Datatonic, 5x Google Cloud Partner of the Year, is the leading cloud data + AI consultancy for the world’s largest businesses, delivering tangible innovation at the leading edge of Google Cloud. Learn about our experience with Google Cloud, and contact us to find out more.

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