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1. Introduction: Why Migrating to a Data Lake Reshapes the Business
Migrating data to a data lake is much more than moving files or purchasing more cloud storage. It's a strategic decision because it redefines how data flows, connects, and transforms into value. But every strategic decision requires preparation—and that's where many companies stumble.
According to TDWI , 85% of organizations report that the time and effort required to integrate new data sources is a major obstacle in modernization projects . This shows that the challenge of migration lies not only in technology, but also in the ability to clearly structure the process: what to migrate, how to prepare the data, how to ensure security, and how to avoid rework.
Without this planning, the Data Lake risks becoming nothing more than a new repository, more expensive, more complex, and as limited as the legacy one intended to overcome. Therefore, before beginning, it is essential to understand the real impacts of migration .
In this article, we will explore the signs that the current environment is no longer sufficient; how to put together a transition plan without hindering operations; how to structure governance from the outset; and, most importantly, what changes in practice when data stops being a problem and becomes a strategic asset.
Shall we?
2. When your on-premises legacy no longer supports your Data Lake
Every system has a breaking point , and when it comes to data, it usually arrives silently.
First, it's a difficulty integrating information from different sources. Then, reports take longer than they should. Gradually, data stops circulating and starts to accumulate . Until the structure simply no longer keeps up with the business's needs. This is the limit of the on-premises legacy. Designed to store smaller volumes and more predictable data, the on-premises worked well in a more static scenario. But today, when we talk about migrating corporate data to the cloud, this structure no longer keeps up with the speed and diversity that the business demands.
And here's the key point: it's not the Data Lake that doesn't work on legacy. It's the legacy that no longer supports the logic of a Data Lake .
This is because migrating to a Data Lake isn't just about space. It's about elasticity, continuous integration, distributed security, and true scalability —attributes that on-premises infrastructure can't offer without high costs, complexity, and risk of failure.
When data no longer serves the business and begins to hinder decisions, the symptom isn't technical: it's strategic . Response time shortens, the quality of analysis declines, and IT becomes seen as a correction department, not an innovation department.
At this point, insisting on legacy is postponing the inevitable. The path forward is to build awareness of the limitations of the current structure and prepare for the transition to an environment capable of freeing data to work for the business.
But how can we plan this transition without paralyzing the operation already underway? That's what we'll explore in the next topic.
Data Lake migration without disrupting operations
Migrating data while the business continues to operate requires more than a good tool: it requires careful consideration. The rush to "modernize everything at once" is often the biggest saboteur of migration projects . After all, moving data without planning can crash essential systems, duplicate efforts, and compromise information reliability.
The data migration plan needs to be informed by the company's context: its pace, priorities, and operational complexity. It's not about executing a major technical shift, but rather about conducting a realistic and smooth transition .
Check out the fundamental steps to structure this journey safely:
- Understand the starting point : map the systems that generate the most data and the flows that most impact daily life. It's not just where the data is, but how it moves;
- Define scope with an eye on impact : Don't try to migrate everything at once. Prioritize the migration of the most critical data, data that supports strategic reporting or that currently suffers most in the legacy environment.
- Ensure coherence between environments : the legacy environment needs to continue operating while the Data Lake takes over. To achieve this, develop integrations that avoid duplication and ensure consistency.
- Automate from the start : pipelines aren't a luxury, they're infrastructure. Automating these routines reduces errors and frees up IT for more analytical tasks.
- Implement continuous testing : validate not only whether the data arrived, but whether it arrived intact, up-to-date, and with the context preserved;
- Monitor value in real time : Track the benefits of the new structure from the first data migration. This allows you to quickly adjust your path based on what's working and what's not yet working.
With this plan, migration ceases to be a risk and becomes a lever for efficiency . Operations continue, data gains momentum, and the new environment begins delivering value even before it's 100% complete.
To be successful, all this fluidity needs to be anchored in a non-negotiable point : security and governance from the very first data point. That's what we'll talk about next!
4. Governance from the very first data: how to ensure real security
Migrating to a data lake usually stems from a positive expectation: to provide greater agility and autonomy to business areas. But if governance isn't structured from the first data ingestion, this freedom quickly becomes a risk : inconsistent reports, exposed sensitive data, and metrics that are no longer reliable.
Governance, in this context, is not synonymous with bureaucracy. It's the ability to provide context and reliability to data as it enters the Data Lake . This includes three areas that must work together:
- Structured metadata : each piece of information has “labels” that indicate its origin, format, update time and usage rules;
- Clear access profiles : users only access what they are authorized to see, with a record of who accessed it and when;
- Recorded lifecycle : From ingestion to deletion, every step of the data is traceable and auditable.
This prevents decisions from being made based on different versions of the same report or data that has expired. Instead of slowing down operations, governance acts as an invisible safety net , allowing each area to exercise autonomy and accountability .
Skyone Studio platform , for example, integrates governance directly into the ingestion layer: automatic cataloging, access control, and compliance with regulations such as LGPD and ISO 27001. In other words, governance is no longer a side task and becomes a native part of the migration process.
When this structure is well-defined, the Data Lake ceases to be just another technical repository and becomes a reliable environment for decisions that directly impact the business. This is precisely where the value of migration begins to appear in everyday life. Stay tuned to learn more!
5. Post-migration value to the Data Lake : what changes in practice?
Migrating to a Data Lake doesn't end with the data transfer. It's in everyday use that the value is revealed: reports gain speed, integrations become invisible, and routines that previously consumed energy now run automatically.
In practice, the changes are concrete:
- Faster responses : analyses that used to require hours in local environments can now be processed in minutes, enabling decisions to be made even during a negotiation or strategic meeting;
- Predictable growth : increasing data volume no longer creates bottlenecks. The cloud environment grows on demand, without upfront infrastructure investments.
- Consistent metrics : a single database eliminates discrepancies between reports and duplicate versions, bringing clarity and confidence to areas that depend on accurate numbers;
- Continuous integration : data from ERP, CRM and external applications ceases to be silos and begins to form a single view of the business;
- Intelligent automation : ingestion, validation, and enrichment routines no longer depend on manual effort, gaining reliability and freeing the IT team for more strategic activities.
And these effects are already materializing in market results. Panasonic , a global leader in the electronics sector, reduced information processing time by 75% and achieved 65% savings in operational costs by modernizing its data ecosystem with Skyone Studio.
The Wish Group , one of the biggest names in hospitality in Brazil, achieved 5x greater efficiency in data management with Data Lake and reduced its operational costs by 90% by automating processes with AI agents, also using Skyone Studio.
Ultimately, the value of migration becomes apparent when the numbers stop counting only the past and start anticipating the next step . This shift from delayed reports to real-time decisions is what makes the Data Lake a strategic driver for innovation and competitiveness.
If your company is considering this move, talk to one of our Skyone consultants ! We're ready to help transform the complexity of migration into a clear, secure journey that aligns with your business's pace.
6. Conclusion: Well-done migration is the bridge between chaos and intelligence
Migrating to a Data Lake marks the transition from a business that reacts to data to one that acts on it. This difference is evident in everyday life: reports that arrive on time , decisions that don't rely on assumptions , and teams that stop wasting energy on repetitive tasks and focus on what really matters.
This leap doesn't happen by chance. It's the result of conscious choices : recognizing the limits of legacy systems, planning the transition without compromising operations, and structuring governance from the outset. This is what separates a Data Lake that functions as a simple repository from one that becomes the central engine of strategy .
Migration is the beginning. The next step is to create a culture that makes data part of the decision-making process. To learn more about this topic, we recommend reading another article on our blog : How to analyze data for a data-driven approach ?
