Tackling the top 5 business risks of data landscape modernization
The cloud empowers enterprises with on-demand scalability, flexibility, and cost benefits, enabling them to respond to fast-changing business requirements and fuel growth.
According to Gartner1, more than 70% of companies have now migrated at least some workloads into the public cloud. While most IT and data teams are under immense pressure to migrate their data warehouse, ETL, and analytics quickly to the cloud, the path to modernization remains complex and risky. This blog outlines the top 5 associated business risks and how to address them.
#1 Business disruption
For most enterprises planning to move to the cloud, the fear of business disruption is overwhelming.
- Will the end user experience be impacted?
- Will our day-to-day operations continue to run smoothly?
- Will there be any downtime or data loss?
These questions play heavily on the minds of infrastructure and operations leaders. A deeper dive reveals the underlying reasons behind these apprehensions – inadequate documentation, lack of in-house expertise, highly complex and technical systems, incomplete understanding of existing code logic, etc.
An automated assessment of legacy workloads can provide complete visibility into the existing codebase, including inventory profiling, identification of workload interdependencies and data/process lineage, key resource utilization metrics, and query complexity assessment.
These insights help enterprises choose the right transformation candidates, optimize their workloads for the target, plan the migration in phases, and ensure a seamless transition. Our assessment and transformation accelerator, LeapLogic, recently helped an American retail company convert Netezza and Informatica to an Azure-Databricks stack and operationalize the migrated workloads without any hitches in just 16 weeks.
#2 Spiralling costs
While strategizing the move to the cloud, most enterprises are concerned about their existing investments, not to mention the years of effort they have put into writing business logic, rules etc. To make the most of existing investments, it is important to reuse legacy workloads like data, analytics, and DML, ETL, orchestrator and reporting scripts, wherever possible.
Intelligent automated solutions can help reuse your existing investments by smartly converting diverse workloads and migrating the schema and data to the target platform.
Another common concern is technical debt. Design and code defects stacked in the legacy system over years can snowball into a death-spiral of technical debt, which causes multiple operational issues. Modularizing the architecture is one of the common techniques for avoiding technical debt.
For this, it is important to identify dependencies between the workloads at the process and data level, which is highlighted in lineage. Automated workload assessment and transformation with LeapLogic helped a telecom giant automate Teradata modernization and save millions of dollars.
#3 Inability to meet SLAs
Workloads transformed to the target-equivalent may not always perform well enough to meet business or technical SLAs. Often, even if they can be executed in the new environment, they fail to perform optimally on the target tech stack and architecture. This in turn impacts production SLAs, business decisions, costs, and the overall time-to-market.
To meet performance SLAs and control costs, focus on optimizing the price-performance ratio during migration. Start by identifying technical debt and anti-patterns in the source systems and code. These need to be resolved and optimized at the orchestration level to enable parallel execution as compared to sequential execution (which can lead to delays).
Advanced tools for automated transformation provide optimization recommendations for the target at the schema, code, and orchestration level. These recommendations are based on the enterprise’s workload types (ETL-heavy/consumption-heavy, etc.) and business goals.
A Fortune 500 global enterprise technology provider was able to improve SLAs by 20% through automated migration of legacy Teradata workloads to a modern data platform.
#4 Security loopholes
Transferring data to the cloud potentially carries many security risks, such as insider threats, accidental errors, external attacks, malware, misconfigured servers, insecure APIs, compliance breaches, etc.
A transformation partner can help implement best practices, define regulatory compliance standards, and set up effective policies and protocols for encryption/masking, authentication, and authorization. The migrated data and transformed queries also need to be tested and validated in the new environment.
However, for compliance and regulatory reasons, many enterprises choose not to share their sensitive data for testing. An automated migration accelerator can address this challenge by generating a sample dataset with tens of unique records based on the exact query conditions and validating these with 100% accuracy. In addition, the solution can feed the customer-provided dataset for testing on real datasets, which is suitable for integration testing of transformed queries.
#5 Project delays
For any modernization initiative, it is critical to clearly define the scope of migration and prioritize use cases with high business value. Segregating the workloads into units for as-is migration, optimization, and complete refactoring can help transform workloads in sprints and avoid schedule overruns. It is equally important to choose the right migration partner and toolset to ensure on-time completion.
Many organizations try to save on costs by giving the migration project to an internal team, even if they are not ready for it. This often leads to errors and rework, increasing costs, and delays in implementation.
To ensure successful cloud modernization, people at every level across the organization need to align on goals based on business value. All stakeholders must have a holistic view of the project scope and understand the responsibilities of the internal teams as well as the migration partner. Relevant in-house personnel should be aware of the industry’s latest tools, best practices, and reference implementations. Re-skilling and upskilling also need to be continuous processes.
Additionally, enterprises must ensure effective program and portfolio management by integrating IT governance with organizational governance.
Large-scale data estate modernization projects can be extremely challenging because of the number of unpredictable variables involved. A manual approach can get the job done, but it requires substantial time commitments and comes with a high risk of human error.
Automated transformation accelerators like LeapLogic can help businesses transform both legacy logic and code, and journey to the cloud with the highest level of accuracy and minimal disruption.
To know more, book a demo today.