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Introduction
You may have heard that data is the new oil . But for many companies, this wealth is more for a wasteland: chaotic, poorly distributed and difficult to explore. And it is not for lack of raw material: only in 2025, will the world generate more than 463 examatabytes of data a day , according to Raconteur .
The problem is that amount does not mean clarity . In everyday life, what is often seen are systems that do not talk, reports that generate more doubts than answers and increasing pressure for rapid decisions, even when data is not ready. The feeling is to always be a step back .
This is why Data Warehouse has been gaining ground in conversations about efficiency and data maturity . Because it is not enough to have information: it must be available, structured and reliable at the right time.
Throughout this article, we will simplify this concept , show how it works in practice, and explaining why a data warehouse can be the key to smarter decisions and more agile strategies.
Good reading!
What is a Data Warehouse and what is it for?
Every company wants to be more analytical. But in practice, the first obstacle is often very basic: data is not ready . Some are in local spreadsheets, others in different systems and do not always speak the same language. When this scenario repeats itself, any attempt at analysis becomes an exercise in noise interpretation.
It is exactly to solve this kind of challenge that Data Warehouse exists. It acts as a kind of “data center” of the company , gathering information from different sources in one place, with structure, logical and historical. But more than just storing, it organizes this data so that they can be really used , with consistency, clarity and purpose.
And what does it fit anyway? It serves to support decisions that cannot depend on achievements. With a data warehouse , you can have a more reliable view of the company's performance, understand behaviors over time and generate insights that support faster and more effective actions.
This centralization also reduces rework, avoids disagreements between areas and releases time of teams that once lost hours consolidating data manually. That is, it prepares the ground for more mature analysis , without promising miracles - just delivering what many companies do not yet have, which are organized and available when they really matter.
A common question at this stage is to confuse data warehouse with the so -called data lake . Although both deal with large data volumes, they have different purposes : while Data Warehouse organizes and structures information for business analysis, Data Lake stores data in gross, without treatment, being more used in exploratory projects as data science. In the end, each has its own role and can even coexist in the same strategy.
But how does all this work in practice? That's what we will see below.
How a Data Warehouse in practice
The proposal of a Data Warehouse seems simple: gather data in one place to facilitate analysis. But behind this idea, there is a robust architecture that needs to work quietly and efficiently so that the strategy really gains traction.
Instead of relying on multiple spreadsheets and systems that do not talk to each other, Data Warehouse organizes data journey : from origin (such as an ERP, CRM or financial system) to the moment this data is transformed into affordable and reliable insights.
This journey takes place in well -defined layers . And understanding how each one works helps to visualize what makes a data warehouse so needed in companies that want to make decisions with more security and speed.
Architecture layers
The operation of a Data Warehouse rests on three major steps : ingestion, storage and analysis.
- Ingestion : Data is collected from different sources. Here, the challenge is to standardize formats, correct inconsistencies and ensure that everything that comes in is enough to be analyzed later. It is not enough just to import data, it is necessary to treat it;
- Storage : This layer organizes the data in structures that preserve the history and facilitate the crossing of information. This is where chaos begins to take shape, creating a solid base for quick and secure queries;
- Analysis : Finally, the analytical layer paves the way for these data to be read by Business Intelligence (BI), dashboards and reports tools. This is where the value appears: when business areas can access reliable information without relying on spreadsheets or manual extractions.
This layered model is what allows data warehouse to fit operations of all sizes . Of course, without promising miracles, but delivering what many companies do not yet have: control .
OLAP X OLTP: What does this mean
If you've heard of OLAP or OLTP, you might have thought they are unique acronyms of those who live in the technology world. But the difference between the two is actually very practical and essential to understand the role of Data Warehouse .
OLTP ( Online Transaction Processing ) is the model used by operating systems such as ERPs. It is optimized to record everyday activities: sales, registrations, payments. Already OLAP ( Online Analytical Processing ) is aimed at analysis . It allows you to browse the data in depth, identify patterns, make historical comparisons and generate strategic responses.
While OLTP serves to make the company work, OLAP helps the company to think. And that's why Data Warehouse , based on the OLAP model, has such an important role: it creates space where the past becomes learning and information becomes a decision.
Understanding how a data warehouse works just part of the equation. The next step is to know that it does not have one way and that this choice can directly impact what you can extract from your data.
Main types of Data Warehouse : Which one fits your business best?
Choosing a Data Warehouse is not just a technical decision. It is also a strategic decision that needs to consider the company's reality , the moment of operation and the maturity of the team to deal with data.
Not every company needs a centralized and robust structure early on. In some cases, the wiser is starting with a more tactical model, facing a specific area. In others, the urgency for consistency and unified vision makes investment in an inevitable corporate architecture.
The important thing is to understand that there are possible ways. Next, we explain the most used types in the market , focusing on what they offer and for those who make the most sense.
Enterprise Data Warehouse (Edw)
Edw is the most comprehensive and structured model. It consolidates data from the entire company , from various areas and systems, in a single analytical repository . This allows strategic decisions to be made based on consistent information, always aligned between the teams.
This type of architecture is ideal for organizations that face challenges with data silos, contradictory views between areas or difficulty in creating integrated analysis. Edw solves this by creating a “unique truth” of corporate data.
On the other hand, it requires more technical preparation, investment and governance . Its adoption makes more sense when the company already recognizes data as a strategic asset, and is ready to structure this management in a centralized and lasting way.
Operational Data Store (ODS)
The ODS is more tactical, focused on supporting almost real time operations. It does not replace an EDW, but complements, creating an updated data layer that can be consulted quickly without the complexity of a complete analytical structure.
It is especially useful in scenarios where time is critical factor . Daily sales, service indicators, logistics flows or inventory monitoring are examples of uses where the SDGs can offer answers with agility, even with limited analytical depth.
Companies that are still maturing your data strategy can use OSD as an intermediate step . It resolves operational pains without requiring a technological revolution.
Data Mart
Data Mart delivers analytical autonomy to specific areas of the company. It organizes data from a single domain (such as marketing , finances or HR), with the structure and metrics most relevant to that context.
This allows each team to have quick access to their own information , without depending on great consolidations or the IT team. The result is more agility and focus on local decision making.
In addition, Data Mart is a great gateway to companies that are taking the first steps in analytical culture. It allows you to start small, validate value and climb more safely.
Types aside, what really matters to the business is the result. And when a Data Warehouse starts working well, the effects appear in places where there was only friction before. Next, let's talk about these gains clearly and concretely.
Real Data Warehouse for companies
Few things are as frustrating as having to make an urgent decision and realize that the data is "almost there"; One number hits, another not. The report of one area contradicts that of another. The time it should be used to act becomes spent time , trying to understand what is happening.
It is in this kind of scenario, common and silently expensive, that Data Warehouse begins to make a difference. Because more than a technical solution, it is a structure that reorganizes the way the company deals with the information itself .
By centralizing data in a unique environment, Data Warehouse eliminates noise between systems, reduces rework and increases confidence in the analysis. When everyone accesses the same source, with the same rules and consistent history, decisions gain agility and lose that constant feeling of "something is still missing."
Among the main benefits, it is worth mentioning:
- Unique and reliable view of the business , with consolidated and updated data in one place;
- Reduction of rework in manual consolidation of spreadsheets and reports;
- Greater agility in decision making , with accessible and aligned indicators between areas;
- Better use of team time , which focus on analysis instead of collection and validation;
- Reinforcement of data governance , with clear rules on metrics, access and treatment of information;
- Preparation for a more analytical culture , without depending on makeshift tools or processes.
In short, a well -structured Data Warehouse doesn't solve all problems, but changes the game . It prepares the terrain so that the data ceases to be an obstacle and become a real ally of the strategy.
At this point, the value of Data Warehouse is clear. Now, let's follow our immersion, understanding how to take the first step, with the right care at the right time.
How to get started: the first steps to adopt a data warehouse
Recognizing the value of Data Warehouse is important. But transforming this understanding into a practical action, and with clear beginning , is what really moves the company towards a more strategic data culture.
The good thing is that this journey does not need (and should not) start with large promises or complex structures. What it requires is clarity : where the data problem bothers the most? Which area suffers the most from rework, noise or lack of confidence in the information?
From these questions, it is possible to start with focus and realism . Know the main steps that help traction without complicating:
- Map the most critical friction points : Identify areas or processes in which data is fragmented, contradictory or difficult to access on a daily basis;
- Prioritize where the impact can be faster : the path does not always start with the whole company. Sometimes a specific team already feels the effects of a poorly resolved flow;
- Choose the most appropriate model for the current moment : This includes evaluating between Edw, SDG or Data Mart, according to maturity and necessity;
- Consider the systems that need to integrate : Understanding which sources feed the most important decisions helps to better plan the initial structure;
- Involve the right people from the beginning : Data Warehouse is not an IT project, but an initiative that needs the adhesion of those who will consume and generate value with the data.
More than a technical project, this is a change of perspective. Data Warehouse organizes the basis for the company to make decisions with more confidence and less improvised - and this starts with a well -oriented movement from the beginning.
The first steps define the course, but it is the care on the way that ensures that the project really advances. Next, we address the points that deserve extra attention . Follow!
Important care to avoid headaches
Implementing a Data Warehouse is a strategic decision that can turn the way your business uses the data. However, it is essential to be aware of some care to avoid common problems that can compromise project success :
- Involvement of Business Areas : Treating Data Warehouse as a project exclusively of IT is a common mistake. The lack of involvement of business areas can result in solutions that do not meet the real needs of the company;
- Focus on data quality data warehouse analyzes and decisions . It is mandatory to implement data validation and cleaning processes from the beginning;
- Scalability Planning : With data growth, Data Warehouse needs to be able to climb properly. Lack of planning can lead to performance problems and cost increasing;
- Security and Conformity : Ensure data security and compliance with regulations, such as the General Law on Personal Data Protection (LGPD), is decisive. Negligence in this regard can result in fines and damage to the company's reputation;
- Change Management : Implementing a Data Warehouse involves changes in company processes and culture. It is important to manage these changes effectively to ensure the adoption and success of the project;
- Choice of proper technology : Selecting the right technology for company needs is critical. An inappropriate choice can result in difficulties in integration, unsatisfactory performance and high costs;
- Continuous Monitoring and Maintenance : After implementation, it is necessary to monitor the performance of Data Warehouse and perform periodic maintenance to ensure its efficiency and relevance.
According to Forbes , about 80% of Data Warehouse projects fail to achieve their goals , often due to the lack of proper planning and involvement of stakeholders.
Anticipating the challenges is what separates a project that evolves from one that locks halfway. But it is not enough to avoid errors: you need to know where to bet. Therefore, in the next section, let's talk about how to make choices that support growth, and why the right technology needs to come with a business vision.
How to choose the right solution: what to evaluate and how skyone can help
Choosing a Data Warehouse is not a purely technical decision: it is a choice of vision. Because the right tool is not just about storing data, but to support decisions, create fluidity between areas and prepare the company for a more agile and oriented management model.
The problem is that, in practice, many solutions seem to promise the same. And that's where the criteria need to go beyond the “what they do” : you have to start considering what the delivery is like, how well the solution adapts to your business and how much it sustains evolution over time.
Thus, when evaluating a solution, it is worth noting:
- The ease of integration with the systems you already use;
- The scalability of the structure as its data volume grows;
- The governance and security offered, especially compared to LGPD and internal compliance;
- The support and monitoring that technology offers in post-implementation;
- How much the solution helps translate data into business value and not just reports.
At Skyone , we believe that organized data is just the beginning. What really matters is what your business can do with them , with speed, clarity and safety. Therefore, our platform goes beyond storage. It delivers performance, scalability and real visibility to those who need to decide without wasting time or margin of error.
If you have gotten here, it is because you know you can do better. And perhaps the next step is not even a decision for now, but a conversation . How about, together, to understand your scenario, your urgencies and think together what makes the most sense now? Talk today to one of our experts and discover solutions that connect with your reality!
Conclusion
In times of informational overload, there is no shortage of data: it lacks direction . And that is precisely when Data Warehouse shows its true value by turning a disorganized environment into a solid base for better, faster and factual decisions.
Throughout this article, we show that the concept need not be a technical mystery. It can and should be a practical part of the routine of companies that see data as strategic assets, not as a problem to solve.
Of course, each organization has its time, its structure and its priorities. But in common, all share a starting point: the desire to stop improvising and start deciding more safely . And when this will find structure, the potential changes the level.
In short, we can say that Data Warehouse is not the end of the journey, but the beginning of a new way of thinking, operating and growing with the data next to them - not against them.
If this content has helped you see more clearly, the next step is to keep learning. On blog , we gather ideas, trends and reflections that help companies like yours transform information into action. Access and explore other texts!
FAQ: Frequently Asked Questions Warehouse
Even with so many data circulating in companies, it is common for the Data Warehouse to still generate doubts , especially when mixing with other acronyms, solutions and promises of the data management universe.
If you are looking for clear answers to understand if this structure makes sense to your business, this is a good starting point. Whether you are someone plunging now on this topic or just wanting to validate your understanding, the following questions have been designed to make everything more accessible from the first contact.
What is a Data Warehouse and how does it differ from other databases?
While a traditional database records and organizes everyday transactions (such as sales, registrations or payments), Data Warehouse is designed to consolidate historical information, integrate distinct sources and offer an analytical view of the business. It is optimized to generate reports, cross data and support strategic decisions - something that operating systems alone cannot efficiently do.
Does every company need a data warehouse ? Or just large organizations?
It is not a matter of size, but of necessity. If your company deals with scattered data, inconsistent reports or difficulty accessing reliable information, a data warehouse may be a viable solution, even in smaller structures. There are scalable models such as Data Mart or Operational Data Store , which serve specific teams and grow along with the company's maturity in data.
Do you have to have a data team to start using a data warehouse ?
Having a dedicated team is useful, but not mandatory. With right partners and proper solutions, it is possible to implement a data warehouse even in companies without an internal data team. The important thing is to be clear about the problems to solve and rely on technical support that translate business goals into a viable and scalable analytical structure.
