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1. Introduction
We live in a world where everything generates data. Each click, purchase, conversation or registration turns into digital fragments that grow at exponential speed.
According to an Exploding Topics survey , over 328 million terabytes of data are created every day. This is equivalent to about 328 million HDs of 1 Terabyte being filled daily - such a gigantic volume that it escapes our human ability to interpret.
But the volume itself does not mean competitive advantage. Gross data are like newly exhausted oil : they have no shape or direct value yet. They only become real fuel when they go through a transformation process. And that's where many companies wage, not knowing where to start or what exactly extract from this “gross ocean”.
In this article, we will take the first step: understand, simply and practically, how to turn data into real intelligence. You will find out what this means in practice, because it matters to the future of your business and how this transformation enables the use of artificial intelligence (AI) with more safety, speed and clarity .
Whatever the size of your business, this journey begins with a question: "What is your data trying to say?" .
Let's find out!
2. Data transformation: From digital oil to AI fuel
If gross data is the “new oil”, data transformation is the process that makes it usable - something comparable to turn gross oil into quality fuel, ready to move intelligent systems with efficiency and safety.
In the context of artificial intelligence, this transformation is what separates initiatives that only react, from those that anticipate, learn and evolve. Because it is not enough to collect data : you need to treat it, organize and make sense to them. Only then is it possible to generate real intelligence.
Turning data into practice means gathering scattered information on different systems (such as spreadsheets, CRMS, ERPs, e-commerce , public bases, etc.), and working this information to make sense when placed side by side. This involves standardizing, cleaning, connecting and structuring data so that it can be used with reliability, including AI applications.
This is a decisive step for any company that seeks agility in decisions and predictability in actions . And most importantly, this process need not be complex or inaccessible. With the right technologies, data transformation can be automated and continuous, no longer a bottleneck and becoming a real competitive advantage.
But after all, why did this become so urgent now? What has changed in the current scenario that made data transformation a strategic priority for companies of all sizes? This is what we will see below.
3. The importance of data transformation in the Age of AI
Artificial intelligence does not work with any data: it depends on a solid, reliable and well -structured base . If the data arrive incomplete, disconnected or duplicate, AI loses efficiency and, worse, can generate distorted answers. It's like trying to build logical reasoning with mismatched information; The result will hardly be coherent.
This is why data transformation is no longer a technical differential and has become a basic condition for those who want to use it strategically. More than a technology issue, is it a choice on how decisions will be made from now on: based on clear data or vague assumptions?
Thus, companies that dominate their data can predict trends, automate routines, reduce risks and respond to market changes. And contrary to popular belief, this capacity is not limited to large corporations. What makes the difference is the process - and it is precisely that we will detail next.
3.1. The refinement process: from collection to analysis
Transforming data is not a single step, but a continuous journey that goes through five main phases :
- Collection : Identify and gather data from different sources, such as spreadsheets, systems, banks, APIs, CRMS, ERPs, among others;
- Standardization : Align formats, fields, nomenclatures. This is where many problems of duplicity or incompatibility are solved;
- Quality and Cleaning : Eliminate inconsistencies, duplicate data, incomplete or obsolete inputs;
- Structuring and Integration : Organize data in models that allow intelligent crossings, relationships and analyzes;
- Analysis and activation : With the prepared base, the data come to life, either to feed AI systems, dashboards or to support human decisions more clearly.
Each of these steps is essential to ensure that what enters the system is, in fact, a valuable asset, not just "volume" without context .
And now that you have understood why and how the transformation, the next question is inevitable: what does your company really gain from it? Let's find out.
4. Benefits of efficient data transformation for companies
Transforming data is not just a technical step, but a strategic turn. When done efficiently , this transformation allows data to cease to be a static repository and boost decisions, automate processes and reveal opportunities.
It's like getting out of a panel car to a high performance model, with all the data in real time on the display : speed, route, fuel, temperature. The difference is that in business, these indicators point to financial performance, customer behavior, operational bottlenecks and more .
Companies that dominate this journey can:
- Act with predictability : using historical and contextual data to anticipate demands and reduce risks;
- Make more security decisions : evidence -based, not achievement;
- Gain time and efficiency : eliminating repetitive tasks with intelligent automations;
- Increase competitiveness : with a clear reading of the market and the operation;
- Customize offers and experiences : crossing behavioral data with history and preferences;
- Release AI potential : feeding algorithms with reliable and structured data.
All of this generates a more agile, more analytical and less vulnerable culture to uncertainty, exactly what differentiates companies that only react from those who lead.
And if the gains are clear, how to put it all into practice? In the next topic, we show what your business needs to implement this process with assertiveness. Keep following!
5. Implementing the data transformation in your company
As we mentioned, transforming data efficiently is not an exclusive mission of large corporations , and with robust technology teams. Increasingly, this process has become accessible, especially when there is clarity about the goals and questions that need to be answered.
The first step is not in the tools, but in understanding your own way. Just as a pilot knows every circuit curve before the race, your business needs to identify which data is most relevant, where it is and what needs to be answered on them. With this in mind, the next step is to structure a flow that allows:
- Collect the right data from the right sources;
- Integrate this information without creating noise;
- Transform and organize data for continuous use;
- Distribute this data to people and systems that will use it.
This flow need not be manual, slow or complex. This is where the right tools come in; Check it out.
5.1. Essential tools and technologies for the process
An efficient data transformation is based on technologies that automate the way of data , from its origin to value generation. Among the most important resources are:
- Integration Platforms (IPAAs) : connect data from different sources (spreadsheets, CRMS, ERPs, seats, APIs) with speed and flexibility;
- Transformation Environments (ETL/ELT) : Responsible for organizing, cleaning, standardizing and preparing data for real use;
- Modern Storage Structures ( Data Lake or Lake House ) : Store data at different preparation levels, enabling faster and more reliable analysis;
- Flexible manipulation languages : such as SQL and JavaScript, which allow thin adjustments on the data path, with agility and control;
- Panels and Viewers : Tools such as Power BI, Metabase and Dashboards turn data into clear and affordable visual reading.
These technologies allow data transformation to happen in an integrated, secure and scalable manner without demanding a full fleet of experts to ride the process.
But, like every innovation journey, implementing data transformation also brings challenges. In the next topic, we will address the main points of attention and how to overcome them strategically.
6. Challenges and considerations when working with data transformation
Turning data into strategic assets does not happen automatically. Like any complex system, it is necessary to tune gears, test boundaries and be aware of critical points that can compromise the entire course.
Next, we show the most common challenges on this journey and what to consider from the beginning to ensure traction and consistency in the results!
6.1. Privacy and data security
In a scenario where companies deal with increasing volumes of sensitive information, security is the first component that needs to be under control . It is not enough to accelerate: it is necessary to ensure that the brake works, that the data is protected by safety layers and legal compliance, as required by LGPD (General Data Protection Law).
This includes practices such as encryption, access control, anonymization and safe storage . That is, AI can only operate with confidence when data is protected by a robust and armored environment.
6.2. How to manage large volumes of information
Data come from all sides: ERPs, CRMS, spreadsheets, APIs, public banks and more. Managing this volume requires a structure designed for high speed and stability. This is where solutions such as Data Lakes and Lake Houses , which act as well -organized supply centers - separating raw data from the data ready for use without locking the system.
With this, it is possible to keep the operation fluid, without bottlenecks or processing overload , even when the volume of data increases.
6.3. The importance of qualified professionals in data analysis
As much as technology evolves, no system runs alone without a good pilot . Qualified professionals make a difference in interpreting contexts, validating information quality and directing data to smarter decisions.
They are responsible for turning numbers into strategic narratives , and ensuring that the data, once refined, actually impact the business.
6.4. Change of Mentality and Organizational Culture
Adopting a data -oriented culture is how to change the pilot style: it requires training, consistency and clarity of purpose . These are not just tools, but people who trust the data to decide, learn, and adjust the course based on evidence, not assumptions.
When this culture consolidates itself, the data is no longer just one report at the end of the month, and becomes an asset that guides the company's daily life .
Overcoming these challenges is what ensures stability and scale. And with the right structure in place, now, it's time to look forward : what comes next in the role of data within artificial intelligence? Check it out.
7. The future of data transformation and its impact on AI
In the coming years, the advancement of artificial intelligence will no longer be measured only by the ability to respond fast, but by the quality of learning it is able to absorb in real time - and this is directly linked to the way data is transformed in the daily life of the operation.
Today, more mature companies are already starting to incorporate AI layers within the data pipeline This means that processes such as standardization, correction, enrichment and categorization are made automatically, without depending on coding or manual adjustments . AI acts even before the analysis: it organizes, warns, anticipates.
According to McKinsey , 72% of companies already use some level of AI , which shows that adoption has grown, but still lack preparation at the base. This scenario makes room for a decisive movement: the adoption of private general models , trained with internal data and protected by controlled environments.
Instead of using a generic AI trained with external content, these companies develop intelligent agents capable of responding based on contracts, technical manuals, historical service or any other strategic source of the business.
It is not just about efficiency, but to build an intelligence that respects the context and confidentiality of the operation . The result? Less dependence on public data, more accurate answers and greater control over models that really generate value.
This future is already under construction. And those who now start to structure the data with strategic vision put your business ahead in the intelligence game.
In the next block, we show how Skyone already delivers this scenario in practice!
8. How Skyone enhances data transformation in your company
At Skyone, we don't believe in generic solutions. We know that each company has a different starting point, and that is exactly why our platform has been designed to adapt to the most diverse scenarios , without complicating, without requiring an internal revolution.
Over the years, we have realized that the real challenge is not only in integrating systems, but in giving fluidity to the journey of data , from origin to practical application. Therefore, we create a structure that eliminates noise, automates steps, and delivers visibility in real time over everything being transformed.
In practice, this means that we can:
- Read data directly from spreadsheets, legacy systems, SQL banks and external sources, without depending on manual encoding with each new input;
- Apply transformation logic to JavaScript and Jenonata with flexibility, as if each data underwent a thin adjustment bespoke before reaching the AI;
- Operate in both cloud and local environments, respecting what each client needs in terms of compliance, privacy and performance;
- And keep it all connected in one place with governance, traceability and full control.
Our role is to ensure that your business data circulate as it should: without friction, clearly, and ready to generate real intelligence . Our platform does the hard work behind the scenes, while you and your team focus on using data as strategic assets.
Want to take it off the paper and see how it applies to your operation? Talk to a Skyone expert . We are ready to help you turn data into decisions with much more autonomy, speed and scale!
9. Conclusion
Transforming data is not just a technical movement: it is a strategic maturation . Throughout this article, we have seen that gross data has no value by themselves. They need to be extracted, organized, refined, and activated so that they can generate more agile decisions, more accurate answers, and real intelligence in AI applications.
It is clear that the challenge is not only in the amount of information available, but in the ability to structure this information with consistency, safety and context. And that this process does not depend on giant projects or complex structures: it depends on vision, clear intention and tools that make this transformation fluid.
As artificial intelligence advances, the way we treat the data becomes even more decisive. Anyone who wants to accelerate, with stability and control , needs to ensure that the "engine" of the data is clean, well calibrated and ready to respond efficiently. It was with this view that we organize this content: to help you see data as a living asset, not as a static file .
Want to keep exploring as data and AI can translate into real advantage in business? Also read our article “Going to Business: How Artificial Intelligence can transform your business”!
FAQ: Frequently asked how to turn gross data to
Whether for those who are starting to explore the universe of Artificial Intelligence (IA), or for those who already understand the importance of data, always the same initial doubts arise: “ Is my company ready?”, “Do I need a robust structure?”, “Is this for smaller business?” .
Here we put together the most common questions, and answer them with objectivity, clarity and real applicability.
1) Can small companies benefit from data transformation?
Yes. Business of all sizes can benefit from data transformation, especially minors, which gain agility and intelligence without the need for heavy structures. With accessible tools and simple automations, you can integrate information from spreadsheets, CRMs or ERPs and start making more assertive, evidence -based decisions. The secret is to start clearly about which data is most relevant and what is the goal of transforming it.
2) How to know if my company is ready to adopt data transformation?
You don't have to have everything organized to start, but you need to know what you want to find out with the data. If your company already has scanned processes (in CRMS, spreadsheets, sales platforms, etc.), and faces questions such as: “Why do results vary?”, “Where are we missing opportunities?”, “What can we predict better?”, So there is a starting point. The data transformation serves precisely to give clarity to what is dispersed today. The most important thing is to have a clear problem or goal. The rest can (and should) be built along the way.
Theron Morato
Data expert and chef in his spare time, Theron Morato brings a unique look at the universe of data, combining technology and gastronomy in irresistible metaphors. Author of Skyone's “Data Bites” column, it turns complex concepts into tasty insights, helping companies to extract the best from their data.
