How artificial intelligence turns structured data into actionable insights.

Around the world, companies across industries have access to an unprecedented amount of organized information: customer records, sales data, inventories and much more. However, although this data is carefully stored and easy to access, the real challenge is transforming it into useful insights that truly guide strategic and operational decisions.

It is in this scenario that artificial intelligence (AI) emerges as an indispensable tool. AI not only processes large volumes of organized data, it also identifies patterns and trends that would go unnoticed in conventional analysis. According to Gartner , by 2025, more than 70% of companies should adopt AI for analyzing organized data . This data reveals an important change: AI is no longer seen just as an innovation and is now considered a concrete answer to extract real value from data, and transform this information into effective strategies.

But how can AI transform organized information into strategic

insights What are the practical benefits of this technology for different sectors? In this article, we will answer these questions and explore how AI can improve data management to generate meaningful results and support decision-making. Happy reading!

What is structured data and what is its relevance?

With the advancement of digitalization, companies increasingly accumulate large volumes of data, but not all of this data is equally usable. Thus, structured data is particularly valuable due to the ease with which it can be accessed and analyzed .

This type of data is essential for companies that seek to base their decisions on concrete and actionable information, especially with the support of artificial intelligence, which this organization needs to process and interpret information accurately. Next, we will explore these aspects in detail.

Structured data: definition and practical examples

Structured data is information that follows a rigid and standardized format , generally organized into rows and columns that facilitate access and manipulation. This information is stored in systems such as relational databases, where the rigid structure allows for quick queries and analysis. This type of organization is extremely important for AI systems that need organized data to detect patterns, generate insights and provide answers efficiently.

• e-commerce company that maintains a database with information on each transaction carried out, including columns for “purchase date”, “product sold”, “transaction value” and “customer location”. With AI analyzing this data, the company can identify sales patterns, such as the increase in purchases of certain products in certain regions or periods of the year. These insights can help the company adjust inventory, launch marketing and optimize its service based on concrete and easily accessible information.

Difference Between Structured and Unstructured Data

But not all information available in companies follows a rigid and standardized structure. This is what we call unstructured data (such as emails , customer feedback Since they do not have a fixed format , they require specific techniques for analysis, as they are in free language and do not fit into tables or spreadsheets. This makes processing and extracting information from this data more complex .

• Comparative example: In a retail company, structured data may include information that allows AI to generate quick analyzes about consumer profiles and purchasing preferences (such as customers' age, location, and purchasing history). In contrast, if the company collects textual reviews from customers on social networks, this unstructured data needs techniques such as natural language processing to interpret feelings and opinions. So, while structured data is ideal for quick analysis, unstructured data requires more detailed approaches to extract value .

The importance of structured data for companies

We can say that structured data is more than a practical way of organizing information: it represents a strategic basis for informed decisions . This data allows companies to monitor results, adapt operations based on evidence and anticipate market demands. Because structured data is easy to access and analyze, it allows AI to generate insights efficiently and at scale.

• Application example: consider a supermarket chain that tracks the sales history and purchasing behavior of its customers. With AI analyzing this data, the company can identify seasonal peaks, such as increased demand for certain products on festive dates. This way, the company can adjust orders for popular products, avoiding stockouts and improving the customer experience. With this organization, structured data, combined with AI, enables more precise planning and optimized operations , which directly impacts the company's results and competitiveness.

Now that we know how structured data works and why it is so valued, let's understand how artificial intelligence can extract even greater value from it?

How does artificial intelligence transform structured data into insights ?

Artificial intelligence enables companies to leverage structured data to extract deep and useful information, revealing patterns, anticipating behavior and guiding strategic decisions. The transformation of this data occurs in careful steps , where AI organizes, interprets and cross-references the information, using advanced algorithms to detect relationships that go beyond conventional analysis.

This process occurs in several phases and is based on advanced techniques, which allow AI to continuously learn and adapt to data . Below, we explore each step of this processing and the techniques that make it possible to extract value from structured data.

The role of AI in data transformation

Artificial intelligence plays a fundamental role in transforming structured data into practical information, as it goes beyond simply organizing data . With advanced algorithms, it identifies patterns, reveals trends and allows predictions with an accuracy that traditional methods cannot achieve. Furthermore, AI is not limited to understanding the present, but also anticipating future behaviors based on historical data.

This transformative potential occurs because AI can process large volumes of data at a much higher rate than humans. Thus, its role goes beyond assisting in operations: it redefines the way companies analyze and apply information , which provides a broader and more integrated view.

Steps of processing structured data with AI

For artificial intelligence to extract value from structured data, it follows a multi-step process. Each step is designed to refine the data and ensure that analyzes are performed with high accuracy :

1. Collection and integration: the first step is to centralize data from multiple sources into a common base, creating an integrated and complete view .
   
2. Cleaning and preparation: At this stage, AI comes in to eliminate duplications, correct inconsistencies and fill gaps in the data, ensuring that quality is maintained . This step is essential, as accurate data results in more reliable analyses.
   
3. Analysis and identification of patterns: with the data prepared, AI now begins to identify patterns and trends , such as seasonality, consumer preferences and changes in purchasing behaviors.
   
4. Generation of insights and predictions: and finally, established patterns are translated into insights and predictions, which serve to support strategic and operational decisions .

Essential AI techniques: machine learning and deep learning

To transform structured data into insights , artificial intelligence uses advanced techniques , the main ones being machine learning (in Portuguese, machine learning) and deep learning (in Portuguese, deep learning). Each of these techniques offers unique contributions, check them out:

1. Machine learning (ML): is a technique that allows AI to learn from historical data, identifying patterns and offering predictions . In structured data, ML is widely used for predictive analytics, forecasting product demands and identifying market trends.
   
2. Deep learning (DL): is a subarea of ​​ML, uses complex neural networks to perform deeper analyses . Although more common in unstructured data such as images and text, DL can also be useful in structured data, especially for detecting more complex relationships.
   

• Practical example: a bank can use machine learning to analyze structured credit data and predict a customer's default risk. Deep learning can be used to identify more complex patterns in financial behavior, offering a detailed view that allows for safer decisions.

Next, we will see how these transformations impact operational efficiency and improve the customer experience in various sectors, highlighting the concrete benefits that AI offers to the market.

Benefits of transforming data into insights with AI

By converting structured data into insights , artificial intelligence creates a powerful foundation for companies to make more informed decisions and respond quickly to market demands. These benefits go far beyond traditional analysis : they bring efficiency and a strategic vision that directly impact growth and innovation. Below, we explore the main gains that AI provides when translating data into practical and applicable information.

More agile and accurate decision making

With artificial intelligence, companies can process and interpret large volumes of data in a matter of seconds, identifying patterns and trends that support real-time decisions. This provides a unique ability to adapt , especially in changing market environments. AI eliminates manual work and reduces uncertainty, allowing companies to make faster, more informed decisions.

According to a McKinsey , companies that use AI to support strategic decision-making increase their productivity by up to 40% . This productivity gain results precisely from AI’s ability to generate insights .

• Application example: In a retail chain, AI can monitor the performance of products across multiple locations and identify changes in demand throughout the day. With these insights , the company adjusts inventories and customizes offers according to local preference, maximizing sales and minimizing waste .

Personalization and improved customer experience

By understanding customer preferences and behavior, artificial intelligence enables highly effective personalization of interactions and offers. Structured data allows AI to identify customer behavior patterns (such as purchasing habits or product preferences) helping companies to offer more meaningful and relevant experiences for each profile.

According to research from Salesforce , 84% of consumers value personalized experiences, and companies that use AI to personalize interactions can increase conversion rates by up to 20% . This impact reflects the importance of AI in shaping a customer experience that not only meets expectations but also strengthens relationships and loyalty.

• Application example: an e-commerce uses AI to analyze purchase history and the items most viewed by each user. Based on this data, AI suggests products aligned with the customer's interests, increasing the conversion rate and promoting a more engaging experience .

Below, we'll explore how its practical applications across different industries optimize operations, drive innovation, and shape the future of the market.

Practical applications of AI in structured data analysis

As we've seen, artificial intelligence is transforming entire industries by processing structured data and translating it into insights . This organized information becomes the basis for innovations and improvements that directly impact operations, strategies and the customer experience. Below, we highlight some examples of areas that AI is impacting, demonstrating the breadth and depth of its applications.

Marketing and consumer behavior

In marketing , understanding consumer behavior is essential to create strategies that meaningfully connect with the public. Artificial intelligence uses structured data, such as purchase history, digital interactions and demographic data, to identify patterns and predict future trends. This allows companies to create highly segmented and personalized campaigns , increasing the effectiveness of actions and strengthening relationships with customers.

Salesforce report revealed that 80% of consumers prefer to interact with brands that offer personalized experiences . This data demonstrates how AI, when analyzing structured data, directly contributes to companies meeting consumer expectations, personalizing products, services and communications.

• online fashion store can use AI to identify patterns in customers' structured data , such as preferences for types of clothing in certain seasons. Based on these insights , the store creates personalized campaigns, offering discounts on products that customers have already shown interest in. This results in greater engagement and increased sales.

Finance and predictive analytics

In the financial sector, artificial intelligence has stood out for its ability to process large volumes of structured data (such as credit histories, transactions and payment patterns), to predict economic trends, reduce risks and identify fraud effectively. The intelligence generated by this data enables faster and safer decisions, essential in a dynamic market.

According to PwC , applying AI in the financial sector could reduce operational costs by up to 22% by 2030 . This data reflects how AI, by transforming structured data into insights , not only promotes greater security and efficiency, but also reduces costs by automating critical tasks and optimizing financial processes.

• Practical example: A bank can use AI to analyze structured data from thousands of daily transactions and detect unusual activity that indicates possible fraud. Additionally, based on credit history and financial behavior, AI can predict a customer's likelihood of default , adjusting credit limits or suggesting renegotiations before the situation evolves.

Health and early diagnosis

In healthcare, artificial intelligence is redefining the use of structured data by identifying patterns in medical records, test results and clinical histories. This allows for faster and more accurate diagnoses, as well as more effective and personalized treatments .

Accenture estimates that by save the United States up to $150 billion per year . This data demonstrates the economic and operational impact of AI in healthcare, by transforming structured data into diagnoses and solutions that save resources and improve patient care.

• Practical example: a hospital can use AI to analyze structured patient data, such as glucose and cholesterol levels, medical history and routine exams. AI identifies early signs of conditions like diabetes or high blood pressure, allowing doctors to recommend lifestyle changes or preventative treatments before the problem worsens .

Despite clear advances and benefits, practical applications of AI in structured data analysis raise important questions about privacy, security, and ethical use of information . In the next section, we will discuss how companies can balance innovation and responsibility to ensure efficiency and confidence in the use of technology.

Challenges and ethical considerations in using AI

Although artificial intelligence has revolutionized the way structured data is processed and transformed into insights , its use is not without ethical and operational challenges. Privacy, security and transparency are critical concerns that companies need to address when integrating AI into their processes. Therefore, addressing these issues responsibly is essential to building trust and ensuring that the benefits outweigh the risks. Understand more below.

Privacy and data protection

The collection and use of structured data is surrounded by privacy concerns, especially in a global landscape where consumers are more aware of how their information is used . Regulations such as the GDPR (General Data Protection Regulation) in Europe, and the LGPD (General Data Protection Law) in Brazil reinforce the need for transparency and security in data handling.
IBM report , the average cost of a data breach (per incident) was $4.45 million in 2023 . This shows that a structured data breach not only affects a company's reputation, it can destroy consumer trust and generate financial costs that compromise the viability of the business . Therefore, companies using AI need to ensure that data is collected, stored and processed securely and in compliance with regulations.

Data quality and algorithmic bias

The success of artificial intelligence directly depends on the quality of the structured data that feeds its algorithms. Incomplete, biased or poorly organized data can lead to skewed results and discriminatory decisions , harming both businesses and consumers.

According to an MIT , algorithms trained on biased data can have a 35% higher error rate in critical analysis . The data reminds us that when structured data contains biases, AI can perpetuate or even amplify inequalities, leading to discrimination in areas such as credit granting, employment decisions or medical diagnoses . Therefore, ensuring data impartiality is a technical and ethical challenge that requires continuous attention.

AI transparency and explainability

One of the main challenges in using artificial intelligence is the “black box” of advanced algorithms, such as deep learning , which make it difficult to explain how decisions are made. Lack of transparency can generate distrust among consumers and regulatory questions.

According to Accenture , 77% of consumers expect companies to use AI ethically and transparently . Explaining how AI systems reach conclusions increases trust and facilitates acceptance of automated decisions in sensitive industries such as healthcare and finance.

As we face these ethical and operational challenges, it is important to look to the future of AI and its applications, as we will do in the next section.

The future of AI in structured data analysis

Artificial intelligence is constantly evolving, and its application to structured data analysis promises even more transformative changes in the future . The focus will not only be on efficiency, but also on how to integrate technology, ethics and innovation to solve increasingly complex problems. Let's explore how these advances will shape the coming years?

• Intelligent automation and fully integrated processes: in the future, AI will play a central role in automating processes, eliminating manual tasks and enabling faster decisions based on real-time data. Integration with corporate systems, such as ERPs and CRMs, will make information flows more fluid and connected . Companies with integrated processes will be able to react quickly to market changes, reducing operational costs and creating proactive strategies to meet customer needs.
  
• Explainable decisions and trustworthy AI: The future of AI will not just be about what it can do, but how and why it does it. Technologies like Explainable AI (XAI) will enable systems to present clear reasoning for their decisions, increasing trust with consumers and regulators . Sectors such as healthcare and finance, which require clear justifications for critical decisions, will benefit greatly.
  
• Ethics and sustainability as strategic priorities: With increasing dependence on AI, ethical and sustainability issues will be at the center of discussions. Stricter regulations will emerge to ensure the fair and responsible use of AI , preventing discrimination, protecting sensitive data and promoting positive social impacts. Ethics will no longer be a differentiator, but a basic expectation for companies that use AI. Organizations that incorporate these practices will be better positioned to meet the demands of conscious consumers and strict regulators.
  
• Quantum computing and advanced analytics: Quantum computing is one of the most promising innovations on the horizon, this technology will allow AI to process structured data at unprecedented speeds, paving the way for solutions that are currently unfeasible through traditional technology . Combined with AI, it will be able to solve complex problems in minutes , such as global supply chain simulations or predictive analytics in the healthcare sector.

We can say that the future of AI in structured data analysis is not just a question of technological advances, but also of how to balance innovation, transparency and responsibility . Companies that embrace these changes will have the opportunity to lead their markets, creating value not only for themselves, but also for society as a whole.

For companies that want to harness the transformative power of artificial intelligence in structured data analysis, support from specialized partners like Skyone is critical . We are at the forefront of this transformation, offering solutions that connect cutting-edge technology to the strategic needs of each business. Through advanced tools, cloud integration and automation expertise, we help companies transform data into insights , ensuring that innovation and efficiency go hand in hand. This partnership not only facilitates the adoption of AI, but also positions organizations to lead in the future of data management .

Want to know more? Contact one of our experts and find out how we can support your company!

Conclusion

Throughout this article, we've seen how artificial intelligence is transforming structured data into a driving force for strategic decisions and innovation . More than technological tools, AI represents a paradigm shift in the way companies in all sectors understand and use their information. In addition to accelerating decisions, it personalizes experiences and drives advances in sectors as varied as healthcare, finance and marketing .

However, the power of AI demands responsibility. Ensuring data quality, avoiding bias and operating transparently are critical challenges that need to be addressed tactfully and seriously. Companies that manage to combine ethics and innovation will not only increase their operational efficiency, but will also build trust with their consumers and partners.

The future of structured data analytics is promising and dynamic , with emerging technologies like quantum computing and explainable AI shaping new horizons. Investing in preparation, training and innovation will make your company at the forefront of this evolution, creating value not only for themselves, but for society as a whole .

Do you want to continue deepening your knowledge about systems integration and its importance for business efficiency? Check out our article on the role of integration in digital transformation.