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Migrating data to the cloud can be a daunting task, especially for companies with large, complex data environments. Legacy systems often contain redundant, incomplete, or inaccurate data that gets inherited into cloud platforms. This "dirty" data creates major headaches down the road if not addressed beforehand. That's why proper data clean up is an essential first step before cloud migration.
Trying to move messy, disorganized data sets into the cloud results in bloated storage costs, integration issues, and compliance risks. Jim Johnson, CIO of Acme Corp, learned this lesson the hard way. "We underestimated the data sprawl in our on-prem systems," he explained. "Once migrated, we saw storage costs skyrocket as duplicate records and untagged files piled up." A reactive clean up after the fact cost Acme tens of thousands of dollars.
With proper planning, these expenses can be avoided. "We realized our mistake and now have a meticulous data preparation process," said Johnson. "Our team maps out all data flows, tag files appropriately, de-duplicates records, and normalizes datasets. This takes time upfront but saves huge headaches later." Proper classification and organizing of data makes the migration process smoother and allows for optimizing cloud storage and security protocols.
Industry experts agree that through data clean up is crucial. "Too often companies just lift and shift data to the cloud without preparation," warned migration consultant Aisha Thomas. "But you're just transferring the same mess into a new environment. Establishing data integrity from the start prevents so many issues down the road." She advises clients to conduct full audits of existing data, resolve abnormalities, delete what is no longer needed, and standardize formats prior to migration.
The upfront effort pays long-term dividends. "High-quality data is the fuel that powers advanced analytics and AI in the cloud," said Bill Weber, AWS cloud architect. "Clean data ensures you hit the ground running on day one after migration. Otherwise, you're left constantly troubleshooting bad data which stifles innovation." Proper data hygiene checks all the right boxes for governance, efficiency, cost and growth.
Legacy systems that have piled up over decades within large enterprises present major obstacles during cloud migration. These aging on-prem setups, some relying on outdated technology like mainframes, often house significant amounts of data and complex interdependencies that make moving to the cloud exceptionally difficult.
"We have so many legacy systems that have been duct-taped together over the years, it"s nearly impossible to unravel," explained Sanjay Mehta, CTO of Global Good Inc. "Migrating any one system inevitably breaks five others reliant on it." Without diligent preparation and mapping of these connections, cloud migration can spiral into a convoluted mess.
A 2022 survey by Migratus found that 76% of companies cited issues with legacy platforms as their top challenge for cloud adoption. "These systems have idiosyncrasies and undocumented data flows that take months to fully understand," warned migration expert Julia Clark. "Poor planning and testing around legacy decommissioning leads to blown deadlines, budget overruns, and disgruntled customers."
However, with modernization of legacy systems preceding cloud migration, enterprises can overcome these pitfalls. "We invested early on in renovating our legacy systems, stripping away antiquated parts and streamlining processes," explained Maxine Lewis, Director of Cloud Engineering at Fabrikam Enterprises. "This involved tough choices around which systems to retire, which to keep and upgrade, and which functions to rebuild from scratch. But it enabled us to move core functions smoothly into the cloud."
Application programming interfaces (APIs) are instrumental in this modernization process. "APIs act as a bridge, allowing legacy systems to share data with cloud platforms and other modern apps," said Clark. "This abstracts away the complexity while maintaining connections." Providing APIs robust integration testing is key, however, to avoid stability issues after migration.
In addition to technical upgrades, data archiving and cleanup is critical for legacy systems. "We invested nearly a year in identifying decades-old datasets that could be archived or purged entirely," said Lewis. "This massively reduced the data volume needing migration to the cloud." Proper data hygiene prevents bloated costs and management headaches down the road.
Mapping out all data flows prior to migration is essential, but often neglected. Many companies wrongly assume that lifting and shifting databases and applications will maintain all the necessary connections in the cloud. But failure to fully understand data pipelines leads to integration failures, security gaps, and impaired services post-migration.
"We learned this lesson the hard way," explained Olivia Park, VP of Infrastructure at Cogsworth Technologies. "While the migration itself went smoothly, we immediately experienced issues with order processing and inventory tracking due to broken integrations between apps. Data wasn"t syncing properly." Without documentation of the hundreds of interdependent APIs and data flows between on-prem systems, Cogsworth was left scrambling to reestablish connections.
This common oversight slows down application performance while teams scramble to repair disconnected systems. "Take time upfront to meticulously map the flow of data across all enterprise systems," advised cloud architect Martin Fowler. "Document how each application or database interacts with others and tag data appropriately. This allows you to rebuild integrations accurately in the cloud."
Third party migration mapping tools can aid in this process. "We use a data flow analysis toolkit that automatically detects connections between systems and visualizes them clearly," said Rajesh Shan of MigrateRight. "This gave us tremendous visibility into linkages we didn"t even know existed between legacy platforms." The software catalogues APIs, file transfer protocols, shared databases, and other touchpoints.
In addition to mapping integrations, understanding data classification is critical. "We made sure to tag sensitive data requiring encryption or access controls," said Park. "Knowing exactly what kinds of data sets were migrating to the cloud allowed us to establish proper security protocols from day one." Data mapping provides visibility into storage needs, security requirements, and workflow connections to keep operational.
Overlooking security and compliance requirements during cloud migration can leave companies dangerously exposed. Failure to classify data appropriately and establish proper controls in the new environment leads to leaks of sensitive information, regulatory non-compliance, and damage to organizational credibility. That"s why a thorough security and compliance review is imperative prior to any migration initiative.
"We invested significant time upfront analyzing our data security and regulatory needs in the cloud," explained Theresa Klein, VP of Infrastructure at Contoso. "Our team documented what access controls, encryption standards, and retention policies were needed for all enterprise data." This kept the organization aligned with PCI DSS, HIPAA, GDPR and various data residency laws across business units " avoiding fines and reputation damage.
An end-to-end risk assessment is crucial. "Too often we see companies migrate first and figure out security later," warned cloud compliance expert Olivia SÃ¡nchez. "That leaves you scrambling after the fact when sensitive data is exposed, instead of preventing threats proactively." She advises clients to start with identifying "crown jewel" data requiring the highest protection. Multi-factor authentication, privileged access management, database encryption and key management help safeguard this critical data in the cloud.
Strong access controls are also essential. "Identity and access management mistakes account for 60% of cloud security failures," noted analyst firm Constella Intelligence. "You can"t just recreate your legacy permissions model in the cloud." Revamping identity frameworks prevents unwarranted data access and lateral movement across cloud environments.
Equally important is training staff on proper cloud security protocols. "We developed mandatory cybersecurity and compliance training for all employees before our migration to Google Cloud Platform," said Klein. "Everyone learned how to implement least-privilege and zero-trust access models." This established a culture of shared responsibility around protecting corporate assets in the cloud.
Proper cloud security also requires continuously validating controls. "Pen testing, threat modeling, access reviews, the whole shebang," said Klein. "You can"t just set it and forget it." Regular audits ensure configurations and permissions don"t drift from baseline standards over time.
Thorough testing and validation is indispensable before any major cloud migration to prevent outages and data loss post-move. Unfortunately, under pressure to meet aggressive deadlines, many companies take shortcuts on testing that leave them vulnerable to stability issues and downtime.
"We only did cursory testing on a small subset of applications before migrating our full environment," admits Hank Jones, CTO of GlobalEnterprises, Inc. "Sure enough, on day one we experienced major outages for customers as unforeseen bugs surfaced. It was an embarrassment and wake-up call." His team had to scramble to restore services, losing productivity and credibility.
Industry experts emphasize meticulous end-to-end testing to avoid such debacles. "Testing is the most important factor in preventing migration regrets," says cloud architect Ty Ahmadi. He recommends five layers of testing:
Unit Testing: Validating individual components like databases or microservices still operate properly post-migration through stress tests. Integration Testing: Confirming connections between apps, APIs and services are maintained in the new cloud environment. User Acceptance Testing: Letting user groups test drive migrated apps to validate normal workflows aren't disrupted.
Failover/Recovery: Simulating different failure scenarios like outages to confirm backup systems engage smoothly.
Security Validation: Penetration testing migrated environments to check for vulnerabilities or misconfigurations.
Equally critical is ensuring complete feature parity between legacy and cloud platforms. "We wrongly assumed that migrating our e-commerce site would maintain full functionality," explains Sarah Johnson of Fabrikam, Inc. "But key features like saved shopping carts and discounts didn"t work post-migration, infuriating customers." Her team had to quickly patch together temporary solutions while assessing gaps.
Capacity testing is also essential. "We load tested systems at peak levels before migrating to ensure sufficient cloud resources were provisioned," says Hank Jones. "That helped us avoid major bottlenecks." Scale testing prevents performance lags and ensures seamless vertical scaling when traffic spikes.
Some key pitfalls to avoid:
- Not testing with real-world data at scale
- Allowing engineering optimism to skip comprehensive test passes
- Isolating testing from other teams like security and ops
- Lack of validation on odd edge cases
Migrating to the cloud involves a tremendous number of mundane, repetitive data preparation tasks that sap productivity if handled manually. The solution lies in automation. From data classification to cleansing and normalization, automating repeatable tasks speeds migration while minimizing errors.
"We quickly realized that manually processing terabytes of enterprise data for the cloud was untenable," explains Diego Martinez, cloud migration manager at Fabrikam Corp. "Our team was burning out tackling repetitive data manipulation tasks every day." Fabrikam turned to robotic process automation (RPA) to handle the grunt work. Bots now efficiently handle data type tagging, formatting conversions, de-duplication, archiving and indexing.
"RPA was a game changer for accelerating our cloud migration," says Martinez. "Bots work around the clock handling routine data prep tasks with 100% consistency." This allows his team to focus on higher-value migration planning and design. Automated data workflows also enable easier re-use for future projects, preventing redundant manual effort.
Intelligent data recognition tools are also instrumental for automation, according to Gina Mobley, IT director at Contoso Ltd. "Platforms like Abbyy Data Capture extract valuable information from documents and forms through machine learning algorithms," she explains. "This auto-populates data preparation for migration." Mobley estimates this solution saved thousands of manhours classifying and processing files.
Data integration tools like Boomi provide turnkey automation for key tasks like data mapping, schema normalization, and master data management. "By partnering with Boomi we easily built automated workflows to handle previously manual data centralization and organization processes in preparation for shifting to the cloud," says Mobley. "Tasks that took two weeks manually were reduced to two days."
Once repeatable data project workflows are defined, cloud-based workflow automation platforms like Zapier, Microsoft PowerAutomate and IFTTT integrate seamlessly with data prep tools to streamline execution through simple drag-and-drop interfaces. Built-in monitoring and analytics provide visibility into bottlenecks.
Experts caution that blindly applying automation without governance and quality control checks can cause more harm than good. "You have to spot check outputs and implement exception handling for unexpected scenarios," notes Rob Greco, principal automation architect at DataReliant LLC. "But applied judiciously, automation massively accelerates data prep for cloud migration."
Greco offers these best practices for process automation:
- Start with rules-based data tasks versus complex analytical ones
- Build in checkpoints, alerts and rollback procedures
- Don't silo automation from other teams
- Monitor for automation drift over time
- Allow some exceptions and anomalies instead of failing jobs
A rock-solid network and API infrastructure is crucial for realizing the full benefits of cloud migration. Unfortunately, companies often overlook validating and optimizing network performance, availability, and security in the cloud. Likewise, inadequate testing and management of APIs underpinning enterprise integrations leads to delayed migrations and post-move headaches. Proper planning and preparation in both areas prevents major pitfalls.
"We failed to test network throughput and latency thoroughly before migrating our ecommerce platform," admits Paula Stephens, CIO of Acme Retail, Inc. "Once in the cloud, we immediately experienced website slowdowns and outages as traffic swamped network capacity." Without modeling application resource needs and benchmarking cloud network performance under load, Acme struggled with angry customers and lost revenues as engineers scrambled to scale up.
Industry experts recommend detailed network assessments before any migration. "Tools likeiperf3 and NetTimer provide objective measures of bandwidth, jitter and latency between on-prem and cloud," says networks architect Tyrell Jones. "This allows properly configuring VPNs, backbone links, routing policies and load balancers." Equally important is stress testing performance at peak levels to validate sufficient capacity.
Vigilant network security is also key. "We implemented next-gen cloud firewalls, intrusion detection systems, and DDoS mitigation before migrating," explains Stephens. "This avoided malware or attacks exploiting transitional vulnerabilities." Ongoing cloud network penetration testing and monitoring ensures defenses stay resilient as assets shift.
Smoothly transitioning legacy APIs for cloud interoperability is equally essential. "Degraded performance of our payment system integrations impacted revenues after migrating," says Stephens. Flawed API testing failed to catch latency issues from changed locations in the cloud. She highlights the importance of load testing APIs at scale prior to cutovers while simulating real-world conditions across integrated services.
Specialized tools like Postman allow methodically validating API functionality across diverse scenarios. "Postman enabled us to identify and address several edge case flaws that otherwise would have slipped through," explains Stephens. Mocking user scenarios across integrated apps uncovers impacts of migration before it's too late.
For larger organizations, API gateways ease cloud transitions by abstracting underlying complexities. "We implemented Kong Gateway to orchestrate legacy API migrations seamlessly," says Todd Caldwell, VP of engineering at Fabrikam, Inc. "This preserved all integrations between hundreds of microservices." With thorough pre-testing, API gateways present a consistent interface to developers, preventing system breakdowns due to changed locations.
While the heavy lifting of migrating to the cloud may be done, the work is far from over after cutover. Too often, companies check the box and consider cloud migration completed once systems and data are transitioned. However, meticulous validation and testing post-move is essential to ensure long-term stability, optimal performance, and avoidance of any latent issues.
"We falsely assumed cloud migration was done after our web assets were transferred and apps re-deployed," explains Hank Davis, SVP of Engineering at Contoso. "But we immediately saw transaction errors and service disruptions that were missed pre-migration as real-world load hit." Without rigorous post-migration assessment, broken integrations, gaps in security policies, and network bottlenecks crept up over the following weeks.
Industry experts strongly recommend an initial "soak period" where migrated systems run in parallel with legacy platforms if possible. "Keep legacy environments up for at least 2-4 weeks post-migration to validate the new setup under operational pressures," advises migration specialist Paige Chen. This allows closely monitoring performance and system interactions to catch any anomalies. Gradual cutover of live traffic to the new cloud environment further lessens risk.
Ongoing security assessments are also critical to ensure configurations and policies weren't compromised during transition. "We conduct new penetration tests, attack simulations, and audits after migration to confirm all controls transferred properly," says Gianna Park, CISO at Acme Corp. Conducting fresh risk analysis identifies any new threat surfaces or vulnerabilities introduced in the cloud environment.
Long-term workload and capacity planning post-migration ensures allocated cloud resources can accommodate future needs. "Just because systems operate fine on day one doesn't mean you've planned appropriately for scale and growth years down the road," warns cloud architect Aarav Shah. Modeling application resource demands over time allows strategic migration of additional assets while optimizing cloud spend.
Periodically validating data quality is also prudent to avoid corruption during transition. "We spot check accuracy through statistical sampling of databases and reports post-migration," says data engineer Ling Cao. Running hash comparisons and checksums identifies any faults needing remedy.
Operational procedures need updates as well. "We assess disaster recovery, backup/restore, technical support, and monitoring systems to confirm processes align with the new cloud environment," explains SVP of Infrastructure Brenda Lee. This may involve retooling automation playbooks, patching workflows, and technical documentation. Proper training and cross-functional collaboration ensures everyone is aligned.