Solving the Cloud Migration Pitfalls in 2025

The rise of AI has transformed cloud migration from a slow, strategic roadmap into a pressing necessity. Yet, there’s still no unified playbook for data teams tasked with moving data to the cloud.

In the rush to modernize, nearly two-thirds of organisations name security and compliance as the biggest obstacles to cloud adoption. This is often due to a fragmented understanding of responsibilities: data teams know the business needs but not necessarily the security requirements; IT teams know the systems and infrastructure but not the data governance policies; and legal and compliance teams understand the regulations but not the technical realities of implementation. By incorporating automated engineering for data governance and security early into your cloud migration strategy, you can avoid many of the pitfalls that stall or derail modernization efforts.

Here are four common pitfalls that occur during the cloud transition and how addressing them early with automated governance, security, and engineering can set you up for success.

4 Pitfalls Derailing Cloud Migration (And the Data Automation You Need to Fix them)

Pitfall #1: Metadata and Business Context are lost in migration

You wouldn’t relocate an archive and leave the index behind. But that’s exactly what happens in many cloud migration projects across large enterprises.

Most organisations today have a formal cloud strategy in place, with a plan to gradually move their legacy systems to one or more hyperscale environments or public cloud. These transitions typically happen project-by-project, often led independently by individual system owners focused on moving raw data, not necessarily the metadata or business context that gives that data meaning.

In many cases, the legacy systems being migrated were never integrated into a joined-up data catalogue to begin with. Metadata, such as ownership, classification, lineage, or business definitions, was never captured – and never migrated.

As a result, a growing number of cloud datasets are technically present but functionally disconnected. No one knows what they contain, where they came from, or how they should be used. Over time, organisations end up with an incomplete or fragmented data catalogue, undermining their ability to govern data effectively, enforce policies consistently and trust the data they rely on for decision-making.

When implement correctly, the migration process can help solve all of these governance pitfalls.

Relevant Bluemetrix Solutions

If you are migrating data from legacy systems (DB, DW, Mainframe, or File) into cloud environments (Azure, AWS, GCP), Bluemetrix and Control-M provide automation functionality through Pipeline Templates, Pipeline Variables, and API functionality, which simplifies and expedites the creation of data pipelines.

All these pipelines automatically capture and record metadata of the data that is being migrated, storing it in a Data Catalogue. Bluemetrix Data Manager can integrate with any Data Catalogue solution, such as Collibra, Alation, Atlas, or Watson Knowledge Catalogue and much more. Business Data can also be created at the pipeline level and automatically added to the Data Catalogue.

Leveraging Bluemetrix Data Manager & Control-M for data migration, metadata and business data from legacy system are consolidated into a single Data Catalogue, ensuring enterprises maintain unified data governance in one Data Catalogue.

Pitfall #2: Data access delays kill business agility

At the start of most cloud adoption journeys, one of the objectives is to improve data access for business users on analytics, reporting, and AI initiatives. But in practice, complete realisation of this goal is difficult to achieve.

This is generally because data is routed through an overstretched data engineering team to build or modify pipelines for every new request. Most data engineering departments are often under-resourced and buried in backlogs, causing delays in accessing the data that was intended to be available in near real-time.

What business users often overlook is that the real issue is not the cloud technology itself, but the lack of a scalable access model. When there is no defined process for requesting, approving and delivering the data, the system breaks down. The engineering team becomes a bottleneck, business users become disengaged, and governance teams lose line of sight into how data is being accessed or used.

Waiting until after migration to design these workflows only compounds the problem. If secure and scalable access paths aren't built in from the start, cloud adoption won’t accelerate innovation.

Relevant Bluemetrix Solutions

As noted in cloud modernization efforts, eliminating data access delays can be resolved with Bluemetrix Data Manager, with varying effectiveness:

• When Data Engineering teams create reusable pipeline templates in Bluemetrix, business users can choose the best option and input their data sources.

  
  

• Business users select the pipeline, which is most appropriate to their needs, input the data sources for validation, and deploy it to Control-M (Bluemetrix default, in-built workload orchestration tool) or Data Engineering team for scheduling.

  
  

• In this way, business users have access to data as soon as it's available, while the Data Engineering team maintains governance and secures data lakes with its access processes.

Pitfall 3. Mainframe data is a migration dead end

Of all the data types moving to the cloud, mainframe data is typically last in line — or at best, the least favoured — and with good reason: unlike modern systems, mainframes rely on EBCDIC-encoded binary formats that weren’t built for modern analytics or cloud-native storage.

Decoding and converting mainframe data into a usable format is a highly manual, expertise-driven process. It typically requires developers to write custom ETL scripts or data transformation logic that can parse binary-encoded files, decode them, and then serialise the data into modern formats, such as Avro, Parquet, or CSV. From there, the data must be ingested into target platforms such as S3, Hive, BigQuery, or Oracle, each with its own schema requirements and integration patterns.

As the source data evolves, these pipelines must be constantly re-engineered to handle changes in encoding, structure, or schema. Without an automated, governed approach to these transformations, making mainframe data available for analytics is nearly impossible.

Relevant Bluemetrix Solutions

When migrating mainframe data to the cloud, Bluemetrix has developed a connector that automatically decodes EBCDIC binary data text files into Data Frames in memory. Once the data is decoded and in memory, the data is validated and transformed into an analytics-friendly format for further processing.

The data can then be written out to any destination, such as S3, HIVE, BigQuery, Oracle, or HDFS, depending on the analytics processing engine being used. By automating this process, Bluemetrix eliminates the need for a dedicated software team to manually write scripts, therefore, simplifying the migration of data from mainframe environments into cloud or data lake environments.

Pitfall 4. Exposure of PII data threatens compliance from day one

The rise of data breaches has made one thing clear: leaving customer PII unencrypted in cloud data lakes is no longer a tolerable risk. High-profile incidents involving tens of millions of records have shown how quickly unsecured data can become a business liability.

But the threat goes beyond breach headlines. Data privacy laws like GDPR and DORA require data-centric protections at the point of ingestion, or before data is written to disk. Leaving PII in plain text, even temporary exposure, creates compliance exposure and audit complexity from day one.

To address this, enterprises typically opt for one of two methods: data masking or tokenization. Data masking is irreversible and often alters the underlying data format, i.e., the size of the string, character types, etc., which makes the masked data incompatible with many downstream applications, such as ML or GenAI. Data tokenization, by contrast, preserves the original data structure and supports controlled reversibility for decryption when necessary, making it a more flexible option for both protection and performance.

For most large enterprises, storing data in a pseudo-anonymized format is a baseline requirement for compliance and risk management. Integrating this protection directly into the migration pipeline—not layering it on retroactively—ensures sensitive data is secured from the start and protected the moment it lands in the cloud.

Relevant Bluemetrix Solutions

Bluemetrix includes two methods for pseudo-anonymization and tokenization of customer data, depending on your data infrastructure:  

Option 1: Deploy Bluemetrix Tokenization

Bluemetrix provides in-memory tokenization/ pseudo-anonymization functionality as standard use for the creation of all data pipelines.

This solution supports all types of common data formats, including Credit Card numbers, email addresses, Social Security Numbers, and more. Additional templating is also available to simplify the application of transformation.

Since the tokenization is carried out in-memory, no raw data is ever kept on disk.

Bluemetrix tokenization solution is FIPS 140-3 compatible and adheres to NIST standards. By utilizing Spark-based processing, the system distributes tokenization tasks across all nodes in the cluster for enhanced performance. This approach is no-code and includes an extensible library of templates for various data types.

Option 2: Cloudera Native Tokenization

For enterprises deploying in Cloudera environments, Cloudera Native Tokenization is a built-in tokenization platform. Using Java UDFs native to Spark/Impala/Hive, you can seamlessly integrate existing ETL/ELT tools to tokenize and detokenize data at any stage of your pipeline creation. Additional features include:

• A scalable architecture to handle datasets of any size

• Centralized key management via Cloudera Ranger/KMS for better security control

• Pre-built routines for faster implementation of use cases

• FIPS 140-3 and NIST compatible

• Ability to tokenize structured and semi-structured data

Data security and governance are a core component of the cloud migration process, especially with the separation of compute and storage in modern cloud architecture. Including them, along with a strong data engineering foundation, in your cloud migration plan from the outset can avoid insufficient, inconsistent, and unscalable implementations.

It goes beyond that, though; incorporating automated data engineering that builds governance and security directly into every stage of migration pipeline can improve your ability to collaborate cross-functionally with accountability, adequately assess data solution options, and produce data usage audits and reporting for compliance purposes.

If you are a Cloudera user, Bluemetrix allows for businesses to facilitate quick, scalable, and secure cloud migrations with our fully managed, easy-to-use instances. Start with a free trial or speak to our experts to request a customized demo of Bluemetrix Data Manager today.