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Data migration to a data lake: what to know before you begin
Migrating data to a data lake is much more than just moving files or purchasing more cloud space. 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 one of the main obstacles in modernization projects . This shows that the challenge of migration lies not only in technology, but also in the ability to clearly structure the movement: 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 just another repository, more expensive, more complex, and as limited as the legacy system it sought to overcome. Therefore, before starting, 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 create a transition plan without disrupting operations; how to structure governance from the beginning; and, most importantly, what changes in practice when data ceases to be a problem and becomes a strategic asset.
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Every system has a breaking point , and when it comes to data, it usually arrives silently.
First, there's difficulty integrating information from different sources. Then, reports take longer than they should. Gradually, data stops circulating and begins to accumulate . Until the infrastructure simply can no longer keep up with what the business needs. This is the limit of on-premises legacy systems. 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 infrastructure no longer keeps up with the speed and diversity that the business demands.
And here's the central point: it's not that the Data Lake doesn't work with legacy systems. It's that the legacy systems no longer support the logic of a modern
Data Lake This is because migrating to Data Lake isn't just about space. It's about elasticity, continuous integration, distributed security, and real scalability – attributes that on-premise infrastructure cannot offer without high costs, complexity, and risk of failure.
When data stops serving the business and starts hindering decisions, the symptom isn't technical: it's strategic . Response time shortens, the quality of analyses drops, and IT starts being seen as a department for correction, not innovation.
At that point, insisting on legacy systems is postponing the inevitable. The way forward is to build awareness of the limitations of the current structure and prepare the transition to an environment capable of freeing up data to work for the business.
But how do you plan this transition without paralyzing the operation that's already underway? That's what we'll see in the next topic.
Migrating data while the business continues operating requires more than just a good tool: it demands careful planning. 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 the reliability of information.
The data migration plan needs to be born from the company's context: its pace, its priorities, and its operational complexity. It's not about executing a major technical overhaul, but about conducting a realistic and smooth transition .
Here are the essential 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 operations. It's not just about where the data is located, but how it moves.
- Define the scope with a focus on impact : don't try to migrate everything at once. Prioritize migrating the most critical data, those that support strategic reports or that currently suffer the most in the legacy environment;
- Ensure coexistence between environments : legacy systems need to continue operating while the Data Lake comes into play. To achieve this, prepare integrations that avoid duplication and ensure consistency.
- Automate from the start : ingestion, cataloging, and validation pipelines
- Implement continuous testing : validate not only whether the data arrived, but whether it arrived intact, up-to-date, and with the context preserved;
- Monitor the value in real time : track the gains from the new structure from the first migrated data. This allows you to quickly adjust the course based on what works and what doesn't yet.
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 to deliver value even before it's 100% complete.
For this to work, 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 discuss next!
Migrating to a data lake often stems from a positive expectation: to provide greater agility and autonomy to business areas. However, if governance isn't structured from the very first data entry, this freedom quickly turns into risk : inconsistent reports, exposed sensitive data, and unreliable metrics.
Governance, in this context, is not synonymous with bureaucracy. It is the ability to provide context and reliability to data at the moment it enters the Data Lake . This includes three fronts that need to work together:
- Structured metadata : each piece of information carries with it "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, each step of the data is traceable and auditable.
This makes it possible to avoid scenarios where decisions are made based on different versions of the same report or on data that has become invalid. Therefore, instead of hindering operations, governance acts as an invisible safety net , allowing each area to have autonomy with responsibility .
In practice, modern solutions already incorporate this logic. Our Skyone Studio , 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 ceases to be a parallel 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 transforms into a reliable environment for decisions with a direct impact on the business. That's precisely where the value of migration begins to become apparent in daily operations. Keep reading to learn more!
Migrating to a data lake doesn't end when the data is transferred. It's in everyday use that the value is revealed: reports become faster, integrations become invisible, and routines that previously consumed energy now run automatically.
In practice, the changes are concrete:
- Faster responses : analyses that previously required hours in local environments can now be processed in minutes, enabling decisions to be made even during a negotiation or strategic meeting;
- Predictable growth : the increase in data volume no longer creates bottlenecks. The cloud environment grows on demand, without upfront investments in infrastructure.
- Consistent indicators : 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 cease to be silos and begin 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 up the IT team for more strategic activities.
And these effects are already materializing in market results. Panasonic , a global leader in the consumer 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 a cloud-based Data Lake 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 simply reflecting the past and start anticipating the next step . This shift, from outdated reports to real-time decisions, is what makes the Data Lake a strategic engine for innovation and competitiveness.
If your company is considering this move, talk to one of our Skyone consultants ! We are ready to help transform the complexity of migration into a clear, secure journey aligned with the pace of your business.
Migrating to a Data Lake marks the transition from a business that reacts to data to a business that acts with data. This difference is revealed daily: reports that arrive on time , decisions that don't depend on assumptions , and teams that stop wasting energy on repetitive tasks to focus on what really matters.
This leap doesn't happen by chance. It's the result of conscious choices : recognizing the limitations of legacy systems, planning the transition without compromising operations, and structuring governance from the start. This combination separates a Data Lake that functions as a simple repository from one that becomes the central engine of the strategy .
Migration is the beginning. The next step is to create a culture that makes data part of the decision-making process. To delve deeper into this topic, we recommend reading another article on our blog : How to analyze data for a data-driven approach ?
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