In the current scenario of digital transformation, where information is considered the new oil, efficient data management has become essential for the success of any organization. In this context, semi-structured data emerges as a key category, offering flexibility and adaptability that structured and unstructured data often fail to provide. Therefore, this article explores the importance of semi-structured data and then discusses the precautions required when dealing with large volumes of this data.
What is semi-structured data?
Before we delve into the importance and care, it is crucial to understand what semi-structured data is. In contrast to structured data, which follows a rigid format like tables in relational databases, or unstructured data, which lacks any defined form of organization, semi-structured data has a flexible organization.
More specifically, they contain structural elements, such as tags or markers, that make them easier to interpret and process, but without the rigidity of traditional schemas.
To illustrate, common examples of semi-structured data include JavaScript Object Notation (JSON), eXtensible Markup Language (XML), YAML (YAML Ain't Markup Language), and even some types of logs and configuration files.
Thanks to this flexibility, these formats allow data to be easily read and manipulated by machines, while also offering a representation that can be understood by humans.
The Importance of Semi-Structured Data
1. Flexibility in Data Management
One of the biggest advantages of semi-structured data is its flexibility. In an environment where data needs can change rapidly, the ability to adapt the structure of the data without the need to reconfigure the entire system is invaluable.
For example, in software development, new fields can be added to a JSON object without breaking existing functionality.
2. Easier Integration
In a world where integration between different systems is the norm, semi-structured data facilitates communication between heterogeneous platforms. Modern APIs often use JSON to transmit data between servers and clients due to its compatibility with various programming languages and ease of use.
3. Support for Big Data and Advanced Analytics
With the exponential increase in the amount of data generated, especially from sources such as social networks, IoT sensors, and online transactions, semi-structured data offers an efficient solution for storing and processing this information.
Big data tools, such as Apache Hadoop and Apache Spark, are optimized to handle semi-structured data, enabling advanced analytics that can reveal valuable insights for businesses.
4. Improved User Experience
Applications that use semi-structured data can offer richer and more dynamic experiences to users. For example, NoSQL databases, which often store semi-structured data, allow for more flexible queries and faster responses, resulting in more responsive and interactive interfaces.
5. Reduction of Operating Costs
The flexibility of semi-structured data also contributes to the reduction of operational costs. By eliminating the need for rigid schemas, businesses can avoid the costs associated with database restructuring and data migration, especially in environments that are constantly evolving.
Cautions When Dealing with High Volume of Semi-Structured Data
While semi-structured data offers numerous benefits, managing large volumes of this data presents specific challenges that require careful attention. Below, we discuss the key precautions that organizations should consider when dealing with high volumes of semi-structured data.
1. Infrastructure Scalability
As the volume of semi-structured data grows, the IT infrastructure must be able to scale appropriately to accommodate the increase in storage and processing.
Cloud-based solutions are often preferred due to their ability to scale elastically, allowing businesses to adjust resources as per demand.
2. Query Performance and Indexing
Efficiency in querying and indexing semi-structured data is crucial to ensure fast response times and effective analysis. Using appropriate indexing mechanisms, such as secondary indexes in NoSQL databases, can significantly improve query performance. In addition, optimizing queries to avoid costly operations is essential to maintain efficiency.
3. Data Quality and Consistency
Maintaining the quality and consistency of semi-structured data can be challenging due to its flexible nature. Implementing data validation and cleansing processes is critical to ensure that the information stored is accurate and usable. ETL (Extract, Transform, Load) tools can help standardize and clean data before it is stored or analyzed.
4. Security and Privacy
With large volumes of semi-structured data, security, and privacy become critical concerns. It is essential to implement robust security measures, such as encryption of data at rest and in transit, role-based access controls, and continuous monitoring to detect and respond to threats.
In addition, compliance with regulations such as the LGPD (General Data Protection Law) must be strictly maintained to protect sensitive information.
5. Metadata Management
Managing metadata is vital to making semi-structured data easier to discover and understand. Well-defined metadata allows users to locate and interpret data efficiently.
Data cataloging tools can help organize and document metadata, making it easier for different departments within the organization to access and use the data.
6. Choosing the Right Tools
Selecting the appropriate tools and technologies to store, process, and analyze semi-structured data is crucial to success. NoSQL databases, such as MongoDB, Cassandra, and Elasticsearch, are often chosen for their ability to handle large volumes of semi-structured data efficiently.
In addition, big data platforms such as Apache Hadoop and Apache Spark offer advanced processing capabilities that are essential for complex analytics.
7. Continuous Monitoring and Maintenance
Constant monitoring of the performance and health of systems that store and process semi-structured data is essential to quickly identify and resolve issues.
Infrastructure monitoring tools and logs should be implemented to track key metrics such as CPU usage, memory, query latency, and error rates. Proactive maintenance helps prevent outages and ensure systems operate optimally.
8. Backup and Recovery Strategies
In environments with large volumes of semi-structured data, implementing robust backup and recovery strategies is critical to preventing data loss. Automated backup solutions that support the dynamic nature of semi-structured data ensure that information can be restored quickly in the event of failures or disasters.
9. Team Training and Qualification
To effectively manage large volumes of semi-structured data, it is essential that IT and data analytics teams are well-trained and empowered. Investing in continuous training and appropriate development tools ensures that the team is prepared to face the specific challenges associated with semi-structured data.
10. Data Governance
Implementing effective data governance is crucial to ensure that semi-structured data is managed consistently and aligned with the organization's strategic objectives.
Data governance encompasses policies, processes, and standards that regulate the creation, use, storage, and sharing of data, promoting accountability and transparency.
Benefits of Proper Semi-Structured Data Management
Investing in the care necessary to manage large volumes of semi-structured data brings numerous benefits to organizations:
1. Informed Decision Making
With well-managed and accessible data, businesses can make more informed decisions based on real insights. Semi-structured data analysis allows you to identify trends, patterns, and opportunities that can be exploited to gain competitive advantage.
2. Improved Operational Efficiency
Efficient management of semi-structured data reduces redundancy and improves operational efficiency. Optimized data storage and retrieval processes allow teams to focus on more value-added tasks, increasing overall productivity.
3. Innovation and Agility
The flexibility of semi-structured data supports continuous innovation and organizational agility. Companies can quickly adapt their systems and processes to respond to market changes, developing new products and services in a more agile way.
4. Customer Satisfaction
By using semi-structured data to personalize experiences and respond quickly to customer needs, businesses can significantly increase customer satisfaction and loyalty. Well-analyzed data allows the creation of solutions that are more aligned with the expectations of the target audience.
Managing Big Data for Informed Decisions and Innovation
Semi-structured data bridges the gap between the rigidity of structured data and the flexibility of unstructured data, offering a powerful solution for information management in the age of big data. Its importance lies in its ability to provide flexibility, facilitate integration between systems, and support advanced analytics.
Dealing with large volumes of this data requires a strategic approach. Infrastructure scalability, query optimization, and data quality assurance are essential precautions.
Investing in its proper management mitigates risks and maximizes benefits, empowering companies to make more informed decisions and foster innovation.
As the demand for data increases, managing it effectively becomes crucial. Organizations that balance flexibility and control will be better positioned to turn information into value.

Discover the power of real-time data analytics

Using the information available in any company is essential to make more appropriate decisions and increase profitability. In this sense, relying on real-time data analysis is essential to analyze data effectively and be agile when adjusting the gears of your enterprise.
But you need out-of-the-box data to simplify the process, allowing your business to focus on gaining meaningful insights to drive results.
Want to know more about the advantages of using real-time data analysis in your company? Keep reading!
What is real-time analytics?
Real-time data analysis is related to the processes of collecting, processing, and analyzing information as it is generated. This feature allows you to make assessments and take action instantly.
In this sense, real-time data analysis helps companies make decisions and make adjustments quickly based on the latest data available. This possibility can lead to improved performance.
This approach is crucial in areas where speed is essential, such as finance, computer network monitoring, fraud detection, social media analytics, and others.
It is also worth remembering that real-time data analysis involves the use of specialized tools and technologies such as Big Data, Artificial Intelligence, NoSQL databases, and real-time visualization tools.
Why is it important to use it in business?
Through real-time data analysis, it is possible to obtain valuable insights in an immediate and up-to-date way. Thus, it is easier to understand how the company's operation is going, what can be done to improve customer acquisition and loyalty, and even differentiate yourself in the niche of operation.
This possibility arises because it allows for agile and accurate decision-making, identifying opportunities or problems instantly, which is essential to keep the company competitive in the face of changes in customer preferences or operational problems.
What are the benefits of real-time data analysis?
The use of real-time data analysis in the company can bring several benefits and help in market differentiation.
Reduction of failures
From real-time data analysis, the company has adequate resources to identify and correct problems and failures that arise in processes quickly, avoiding losses.
For example, imagine that you have a real-time monitoring system in your production process. It is able to identify abnormal operations, such as a sudden increase in defect rates, and send an immediate alert, allowing intervention before everything escalates, reducing waste of materials and time.
Prediction
This type of feature is also very useful when doing predictive analysis, helping to identify trends and patterns before they become obvious. That is, from it you can anticipate competitors and implement profitable solutions.
Suppose an e-commerce that can analyze customer preferences, the performance of certain products, and price fluctuations in real time. Based on this data, it is easier to predict future demands, adjust inventories, and even anticipate marketing strategies to maximize sales.
Agility in processes
From the observability of data and the ability to have constantly updated information, it is easier to make efficient and agile decisions to keep the company competitive in the market.
In this sense, businesses from different sectors can visualize changes in the pattern, such as a sudden increase in demand for a certain product, and adjust strategies to meet the increase in demand, maximizing profits.
Efficiency
Finally, it is worth mentioning that real-time data analysis helps to optimize processes and resources. From it, it is possible to optimize various company activities, reducing costs and increasing efficiency when performing tasks.
For example, if you have a real-time monitoring system for the vehicle fleet, you can identify more efficient routes, reduce waiting time, and minimize fuel costs, optimizing the logistics sector.
What challenges need to be overcome?
Although there are many advantages, it is necessary to pay attention to the difficulties that need to be overcome to achieve efficient use of real-time data analysis.
Accuracy
The first challenge that may arise is to have quality data and process it properly. This care is essential to ensure that decision-making is accurate.
Overcoming this difficulty requires the implementation of robust data collection and validation systems, as well as the use of reliable and accurate analysis algorithms. In this way, you can avoid lack of accuracy in the data.
Data volume
It can also be challenging to deal with a large volume of data in the company's daily life. If you have multiple sources of information available, such a scenario can be even more difficult.
In this regard, it is essential to have scalable storage and processing solutions that can handle the increased volume of data without compromising the speed or efficiency of analysis.
Data Speed
Finally, you need to be able to process and analyze data at a high speed as soon as it is captured, processed, and made available for analysis. This phase may require the use of high-performance IT infrastructure, with optimized solutions to handle real-time data analysis.
Additionally, it is important to minimize latency throughout the data stream, from capture to final analysis, to ensure that information is available when it is needed most for decision-making.

Data Analytics: Why is it so important for decision-making?

It is a fact that companies have found a very competitive market and increasingly demanding customers. Therefore, your actions and processes need to keep up with these movements. To adopt the best strategies, it is essential to invest in data analytics.
Nowadays, data analysis has become a great pillar of decision-making because companies cannot waste time or investments. Each step must be very well calculated, and data are the great allies to achieve goals.
Investing in data analysis is no longer an alternative. This transformation is essential to ensure the survival of a company. Read on to find out how companies are utilizing data analytics and why this strategy is so important for their decision-making.
Why is data analysis so important?
With data analytics, we analyze a large volume of data so that it can be interpreted. The goal is to have this material as a basis for making more strategic and effective decisions.
As we explained, it is not news that companies are finding an increasingly competitive market. Many organizations want to attract the attention of the same consumer profile. The one that develops differentials is the one that stands out.
But to have these differentials, it is necessary to understand the demands of the market and also the demands and objections of the public. All of this significantly influences the solutions developed and the internal processes of each company.
Therefore, it is essential that each action, decision, and strategy is adopted as accurately as possible. There is no margin for error because a wrong decision can compromise the success of the business.
To be sure of what is being done, data analysis is used. Through it, it is possible to identify trends, consumption habits, perceive patterns, and gather insights to assist in decision-making.
We can say that with data analysis a company does not "take shots in the dark". She has a clear vision of what she needs to achieve, can outline her goals, and define the best ways to achieve them.
How are companies using data analytics?
According to a survey of 333 companies in Latin America, including Brazil, 86% of them already use data, analytics, and artificial intelligence in their processes. Out of 10 organizations, 4 aim to identify consumption patterns and trends.
They also invest in data analysis to optimize processes, ensuring more operability at less cost. The focus is on increasing trust, security, and agility within the organization itself.
Despite this growing demand for data analytics and also the view that companies have about its importance, there are still challenges to be faced. One of them is to find well-trained professionals to implement this type of strategy. The other is the lack of data-driven culture. The companies themselves are not prepared for this transformation.
What are the applications of data analytics?
Data analytics is a versatile strategy. It can be implemented with different objectives, which is why it is so important for your decision-making. The data analytics process encompasses different stages, namely:
Problem identification: identify the problem or business question that you want to answer with the data;
Data collection: it is carried out through registries, external sources, databases or research;
Exploit data: they are processed to eliminate irrelevant information, duplicate data, or any other detail that is not useful now;
Data analysis: it is the moment when they begin to be gathered to make sense. Statistical modeling, regression analysis, descriptive statistics, time series analysis, among other techniques, can be applied;
Interpretation of the results: these reports will be interpreted so that they bring information that will help to understand the question from the beginning;
Communication of results: stage in which the results will be shared with interested parties. This can be done through a presentation or a formal report.
Realize the importance of investing in good tools to carry out all these steps of data analytics. With a good platform, you can capture, integrate, and store data, as well as
Generate reports and have technologies such as machine learning at your disposal, to reach the desired insight.
With a well-structured analytics platform, it is possible to just follow the market and its fluctuations and trends or plan your actions and make decisions in a strategic way.
How does data analysis influence decision-making?
Data alone doesn't make much difference to the company. In order to positively influence decision-making, it is essential that there is a goal. Thus, the data collection will be done in a strategic way, thinking about what you want to transform in the business.
A very simple example helps illustrate the importance of data analysis for your decision-making. Imagine that you have a gift to give to a person. You could buy any gift, just so you don't "go unnoticed," but that wouldn't make that gift something special.
The situation would be very different if you did a survey about this person's preferences, their characteristics, what they may be needing, something they would really like to earn, what they already have, among other details.
You would be collecting data to analyze the information and, based on it, define what could please the recipient the most. The chances of getting it right would be much higher, and your gift could be the best ever.
The solutions that your company delivers to the public are that gift. Therefore, it is essential to adopt data analysis to develop new solutions, thinking about what would be best for your audience.
But don't forget that data analytics has much broader applications than developing solutions. Any decision-making within your company can be based on data, even the choice of a communication channel.
It is possible to align your business completely by making much more accurate decisions if you have a clear vision of what needs to be done. Always based on the objectives you want to achieve to have efficiency and competitive differentials.
Data analytics is capable of expanding the view that a company has about itself, its audience, competitors, and the market as a whole. That is why it is also fundamental for your organization, being indispensable for decision-making to be more accurate and effective.
Don't forget that you need a good platform to implement data analytics in your company.

What is ALICE. AI and how does it turn data into strategic decisions?

ALICE.AI is a product that redefines the concept of artificial intelligence applied to business. While many tools are limited to descriptive reports or generic projections, this system operates in three dimensions: diagnosis, forecasting, and prescription.
Its premise is simple yet powerful: connect disparate data—from CRMs, ERPs, external sources, and even call center interactions—to organization-specific strategic questions.
For example:
• How to reduce customer churn by identifying cancellation patterns before they occur?
• What is the financial impact of adjusting prices in a certain segment?
• What prescriptive actions maximize consumers' lifetime value (CLTV)?
The answer lies in ALICE.AI's capacity to map critical challenges and translate them into dynamic analytical models. The tool not only analyzes history, but also simulates future scenarios, considering variables such as seasonality, customer behavior, and operating costs.
The result is actionable knowledge, ready to guide everything from tactical adjustments to strategic turnarounds.
How does ALICE.AI work in practice to optimize your results?
The magic of this technology lies in the combination of three pillars: technical flexibility, robust methodology, and focus on results. Unlike plastered systems, ALICE.AI adapts to the company's existing infrastructure, integrating with databases, clouds (such as AWS, Azure, or Google Cloud), and even open-source tools.
Features that set it apart:
1. Multidimensional Impact Analysis: Goes beyond basic cross-sections to assess how variables interact—for example, how changes in pricing affect customer satisfaction and profit margin simultaneously.
2. Customizable Predictive and Prescriptive Models: From predicting the likelihood of default to suggesting the "next best offer" for each customer, the models are adjustable and evolve with new data.
3. Intelligent Automation of Actions: Recommends real-time interventions, such as sending personalized promotions to prevent abandonment of shopping carts or adjusting inventories based on forecasted demand.
4. Continuous Learning: With each data cycle, the tool refines its algorithms, incorporating market changes and emerging behaviors.
Why choose ALICE.AI for truly data-driven management?
Companies that adopt a data-driven culture face a dilemma: how to prevent complex analytics from becoming static reports, forgotten in dashboards?
ALICE.AI solves this by operationalizing artificial intelligence, making it part of the daily flow of decisions.
Simplified Integration, Expanded Impact
Product implementation is hassle-free. Thanks to the decoupled architecture, it connects to any technological environment, avoiding the need for costly migrations. Additionally, its intuitive interface allows non-technical teams — such as marketing or sales — to access relevant insights without relying on analysts.
A retail use case: By integrating data from CRM, transactions, and social networks, ALICE.AI identified that customers with a high rate of customer service
complaints were 3x more likely to cancel subscriptions. The product not only warned about the risk but suggested personalized actions (such as targeted discounts and proactive contacts), reducing churn by 40% in six months.
Ready to Turn Data into Competitive Advantage?
ALICE.AI is a strategic ally for those who want to go beyond the obvious.
With success stories in sectors such as finance, telecommunications, and healthcare, she proves that data, when well applied, is capable of:
• Anticipate crises before they impact results;
• Optimize investments based on predictive ROI;
• Personalize experiences at scale, increasing loyalty.
Whether to solve specific problems or revolutionize the entire operation, this technology offers a clear path from accurate diagnosis to agile execution.
And the best? All this with a smooth adoption curve, supported by experts who guide your team through each step.
Data is not just numbers, it’s opportunities!
With many companies still adrift in a sea of information, ALICE.AI emerges as a smart compass. More than a platform, it is an accelerator of results, transforming raw data into profitable strategies.
With its adaptability, continuous integration, and focus on measurable actions, it not only responds to current market demands, but redefines what is possible in the age of artificial intelligence.
Discover the potential of ALICE.AI with Stefanini Data & Analytics
We are experts in turning complex challenges into tangible opportunities. With years of experience in advanced analytics and applied artificial intelligence, we provide the support you need to get the most value out of every insight.
Are you ready to turn your data into exponential results?
Learn more about ALICE.AI and how we can help your business reach new heights of efficiency and competitiveness. Contact our experts and schedule a demo today!

With IMPROVE, Stefanini Data & Analytics achieves up to 90% efficiency in carrier commissioning

Adoption of the IMPROVE platform provides greater transparency and precision to the process, increasing employee satisfaction and motivation
Developed to automatically process and organize complex commissioning rules, IMPROVE – Stefanini Data & Analytics' innovative solution that automates commissioning and variable compensation – offers real-time monitoring through interactive dashboards, ensuring clarity and immediate access to essential information for the company's sales team and management.
Its implementation is designed to ensure adherence to specific customer demands: in the first phase, commissioning rules are configured and tested in a production environment; In the second phase, employee training is carried out, allowing the team to operate the platform independently and without the need for advanced technical knowledge.
After the initial configuration, the data is entered via Excel, and the Stefanini Data & Analytics platform processes and exports the final information through spreadsheets and interactive dashboards, providing a clear and accessible view in real time. Finally, it is integrated with CRM, HR, and BI systems, offering even greater efficiency and automation.
This optimization ends up allowing the sales team to have access to real-time information through an application, reducing frequently asked questions and increasing transparency in the monitoring of goals and commissions. Increasing employee satisfaction and motivation, in turn, helps retain talent, reducing turnover and improving team performance.
This is what happened, for example, with Alares, an independent telecommunications operator. To optimize its commissioning system and increase clarity and efficiency for both the company and employees, the company identified the need to streamline processes for a large sales team and detailed compensation rules. In this context, Alares entered into a strategic partnership with Stefanini Data & Analytics to implement the Improve platform.
The platform has allowed Alares to reduce commission processing time by more than 90%, as it is no longer a manual activity. Now, this process can take only hours, whereas before it would have taken days. In addition to this efficiency gain, there is also greater transparency and accuracy in the commissioning system, positively impacting employee satisfaction and motivation.
Headquartered in São Paulo (SP), Alares provides high-speed Internet access services, via fiber optics, as well as pay TV, fixed telephony, data, as well as various digital solutions for people and companies. It has approximately 2,700 employees and operates in 228 cities in seven Brazilian states (São Paulo, Minas Gerais, Paraná, Rio Grande do Norte, Ceará, Paraíba and Bahia), currently serving around 777,000 customers.
For Everaldo Biselli, commercial director of Stefanini Data & Analytics, the details of the commissioning rules require careful adaptation to ensure that all particularities were taken into account. "The implementation of IMPROVE in this scenario requires a deep immersion in the operator's operations, as in the case of Alares, as each commissioning rule was unique and needed to be precisely linked to the corresponding data. It is always a joint work that involves collaboration between teams, that is, a truly 'four-handed' process, where every detail is mapped and personalized to ensure the accuracy of the solution", explains the executive.
"Our priority has always been to develop solutions that truly transform our customers' processes. With our solution, we were able to automate Alares' commissioning, offering a platform that not only centralizes the tracking of goals, but also increases transparency and efficiency in payments. We remain close to offer continuous support and integrate new features, ensuring that the solution evolves along with the customer's demands," says Filipe Cotait, CEO of Stefanini Data & Analytics.
"Our operation has always required a high level of precision and reliability and, with the support of Stefanini Data & Analytics, we started to use a solution that ensures greater efficiency to the process and strengthens the team's confidence. Now, our next step with Stefanini Data & Analytics is to integrate artificial intelligence into the system, with predictive analytics and real-time monitoring, so that our operation continues to meet new market demands," concludes Luiz Fernando Ciriaco, Channel and Commercial Planning Manager.

New professions in the spotlight with the advancement of Artificial Intelligence

Artificial Intelligence (AI) is undoubtedly one of the biggest technological transformations of our era, revolutionizing industries, processes, and even professions. Consequently, with the rapid advancement of AI technologies, new professions have emerged to meet the demands of a rapidly evolving market and a society that needs to learn to live with automated systems, machine learning, and data analysis.
In this article, we will explore some of the professions that will be essential in the coming years, and that will be increasingly required to integrate, optimize, and control artificial intelligence systems. Check it out!
1. Machine Learning Engineer
Machine learning engineers are already valuable professionals in the market, and their importance is only likely to grow. They are responsible for designing, creating, and
improving algorithms that allow machines and systems to learn from data and adapt to new information.
As a result, the growing demand for increasingly autonomous and intelligent AI systems drives the need for machine learning engineers, which will continue to be on the rise.
To meet this demand, these professionals need to have knowledge in programming, statistics, and the ability to work with large volumes of data.
Additionally, their expertise in mathematical modeling and algorithm optimization is essential for developing systems that are accurate and efficient.
2. Data Scientist and Data Analyst
Data scientists are one of the most talked about professions in recent years and continue to grow in importance, especially in the context of artificial intelligence. These professionals collect, organize, and analyze large volumes of data to extract insights and identify patterns that can be used to improve products, services, and processes.
In this same context, the data analyst, although more focused on performing analysis and reporting, will also play a key role in transforming raw data into useful information for decision-making.
For both data scientists and analysts, skills in statistics, data mining, and knowledge of machine learning are essential, as well as experience with tools such as Python, R, and SQL.
Therefore, in the coming years, as the volume of data continues to grow, the demand for data scientists will be even more expressive.
3. AI Ethics Specialist
The use of AI raises complex ethical issues, ranging from data privacy to the impact on the labor market and the control of automated decision-making systems.
As a result of this complexity, the AI ethicist emerges, a professional responsible for ensuring that AI implementations are fair, transparent, and respectful of human rights.
To ensure this responsibility, this expert needs to have a solid understanding of laws and regulations, as well as an ethical and philosophical insight to assess whether the technology is being applied responsibly.
In this way, this professional will have the role of creating policies and guidelines so that companies and organizations can use AI without harming or discriminating against users.
4. Robot Engineer and Roboticist
As robots become more common in manufacturing, logistics, and even the service industries, the profession of robot engineering is growing. These professionals are responsible for designing, developing, and testing robots that use AI to perform tasks that were previously performed by humans.
To do this, this area combines knowledge of mechanical, electrical, and software engineering, especially in programming AI algorithms for control and learning.
Thus, robot engineers play a key role in the development of robots that are able to interact with the environment, adapt to changes, and perform complex tasks autonomously.
Looking ahead, robotics is expected to advance towards sectors such as healthcare and education, further increasing the relevance of these professionals.
5. AI Project Manager
Implementing AI projects is complex and requires a high level of coordination between various teams, including developers, data scientists, AI experts, and ethicists.
For this reason, the AI manager comes in, a professional who combines project management skills with a solid knowledge of AI to plan, coordinate, and supervise the implementation of solutions based on artificial intelligence.
In this role, this manager is responsible for defining the project's objectives, managing the budget, and ensuring that implementation occurs within the established deadlines. Additionally, he needs to align the expectations of the team and stakeholders, communicate progress, and identify and mitigate risks throughout the project.
6. AI-Focused Cybersecurity Specialist
With the increased use of AI, cybersecurity has become one of the most sensitive and fundamental topics. The creation of complex algorithms and the analysis of large volumes of data also increase vulnerabilities and the risks of intrusions.
As a result, the AI-focused cybersecurity specialist emerges, responsible for protecting AI systems against attacks and intrusions that aim to exploit weaknesses in their programming or databases.
To play this role, this professional needs to have deep knowledge of digital security and AI algorithms, in addition to being able to identify suspicious patterns, perform vulnerability tests, and implement protection practices to prevent cyberattacks.
7. AI Trainer and Facilitator for Corporate Training
With the expansion of AI, companies have sought to qualify their professionals to understand and deal with new tools and technologies. As a result, AI instructors and facilitators become key to training employees and managers in the use of AI solutions within companies.
In this sense, they offer courses, workshops, and training to help teams understand the basics of AI, from practical applications to the use of specific software.
To be effective, these professionals must have, in addition to technical knowledge in AI, pedagogical and communication skills to transform complex content into clear and applicable teachings.
8. Conversational UX Designer
Chatbots and virtual assistants are increasingly common, and for these AI systems to be well received by users, it is essential that their interaction is intuitive and pleasant.
To achieve this positive interaction, the interaction designer for AI is responsible for creating conversational interfaces and user experiences that are natural and fluid. For this, this professional needs to have knowledge in UX/UI design, in addition to understanding human behavior, linguistics, and psychology.
In collaboration with AI developers, it works to ensure that interaction with technology is efficient and positive, optimizing user engagement.
9. AI Architect
AI architecture involves creating and maintaining the infrastructure necessary to support
artificial intelligence systems in an effective and scalable manner. The AI architect is the professional responsible for structuring and implementing this database and systems, in addition to ensuring the integration between different technologies and optimizing their performance.
In other words, this professional must have a deep understanding of the technical components of AI, such as network systems, databases, and security, and needs to be able to design architectures that support large volumes of data and real-time processing.
As such, the demand for AI architects will grow as companies seek to structure their digital operations more efficiently.
10. AI Law Specialist
The legal impact of AI is a field that is increasingly being explored, especially with the introduction of new privacy regulations such as GDPR in Europe.
Due to this growing legal complexity, the AI law specialist works on analyzing and interpreting these laws, applying them to technology to ensure that companies are compliant with data protection and privacy laws.
Therefore, this professional must have a robust knowledge of digital law and data protection regulations, as well as understand enough about the technology to interpret how laws can be applied to AI algorithms and systems.
Future-proof your business with intelligent AI solutions with Stefanini Data Analytics.
Artificial Intelligence is reshaping the job market, creating new professions, and requiring unprecedented specializations. Therefore, for companies that want to stand out in this dynamic scenario, having experienced partners is essential.
In this sense, we offer complete solutions in artificial intelligence, automation, and data management, helping to integrate new technologies effectively and safely.
To find out how our experts can turn your business challenges into opportunities with AI, get in touch and learn more about our custom solutions.

Unified Observability and Security: Simplifying Cloud Complexity to Innovate Faster and More Securely

Migrating to the cloud has become critical for businesses looking for agility and scalability. However, as cloud infrastructures become more complex and distributed, monitoring and securing these environments brings new challenges. In fact, traditional security and siloed monitoring can no longer keep up with the dynamics of the modern cloud.
Therefore, unifying observability and security is a powerful solution that allows businesses to simplify monitoring, identify and respond to threats in real-time, while ensuring that innovation can occur without compromising data protection and service efficiency.
In this article, we'll cover how this unification optimizes operations and reduces risk, enabling businesses to innovate quickly and confidently in the cloud.
The Importance of Cloud Observability and Security
In the context of complex systems, observability allows you to monitor and understand the behavior of different infrastructure components in real time. With good observability, companies can quickly detect problems, diagnose the root cause, and, consequently, optimize the performance of their services.
On the other hand, cloud security aims to protect data and applications by ensuring that threats, both internal and external, are promptly identified and mitigated. For many, the cloud poses an additional security challenge, as traditional physical control barriers do not exist.
In view of this, and considering increasingly sophisticated threats, an approach that combines observability and security is essential to anticipate risks and protect an organization's assets.
Motivations for Unifying Observability and Security
In a cloud environment, where assets are highly distributed and dynamic, a solution that centralizes observability and security is able to offer a more complete view.
To illustrate, the complexity of modern infrastructure, which often uses microservices, containers, and distributed architectures, increases the difficulty of monitoring and securing the environment in isolation.
In addition, the speed of evolution of cyber threats requires a proactive and integrative approach. Rather than reacting only after an incident, unified observability and security promote an agile response, which facilitates more effective problem resolution.
This unification also addresses the growing need for agility, as development and operations teams demand more fluid processes to keep pace with innovation.
Fragmented environments and self-contained systems not only increase the workload of teams; they can actually slow down the launch of new products, features, and services.
Key Elements for a Unified Security and Observability Solution
To achieve an efficient unification of observability and security, it is necessary to invest in components that facilitate data collection and analysis, in addition to protecting the environment as a whole.
Among these components, real-time monitoring is essential. This constant monitoring makes it possible to quickly identify anomalies and respond immediately to incidents, thus minimizing the negative impact on the operation.
Another relevant aspect is the centralized management of logs and events. These logs provide a broad view of events occurring in the system and, when centralized, accelerate incident analysis and fault remediation.
In addition, the collection and analysis of this data are driven by artificial intelligence (AI) and machine learning (ML), which can identify anomalous patterns and behaviors, allowing the automation of responses and facilitating the detection of threats that, in conventional approaches, could go unnoticed.
The automation of incident responses, in turn, constitutes an important differentiator in complex environments. With it, corrective actions can be taken automatically as soon as a threat is detected, reducing response time and allowing teams to focus on strategic tasks.
Finally, companies that need to meet compliance regulations, compliance, and audit integration provide assurance that all activities and records are compliant with regulations, facilitating ongoing audits and monitoring.
Advantages of Adopting Unified Observability and Security
A unified observability and security solution transforms the way businesses operate by streamlining processes and increasing security. One of the key benefits of this unification is holistic visibility.
A complete view of the entire infrastructure helps identify performance issues and vulnerabilities in real time, which in turn enables proactive response to incidents and threats.
Another crucial benefit is the reduction in operating costs. Unification eliminates the need for multiple independent tools and systems, which lowers licensing, maintenance, and training costs.
Consequently, the time required for teams to learn and manage various tools is significantly reduced, making the operating environment leaner and less prone to failure.
In addition, unification increases operational efficiency since it centralizes data and optimizes workflows. Automating alerts and responses allows teams to focus on critical issues instead of wasting time on repetitive, smaller tasks.
Finally, this approach accelerates the innovation cycle, freeing up IT teams to focus on development projects and continuous improvement. Thus, by reducing security and monitoring concerns, teams can launch new products and functionality faster.
Implementation Strategies for Unified Observability and Security
To implement a unified observability and security approach effectively, it is necessary to adopt an organizational culture that values both security and transparency of processes.
To do this, creating a culture of security and observability involves training teams, establishing clear policies, and encouraging collaboration between operations and security areas.
In this sense, communication between these teams is crucial, as the separation between these functions can often result in redundant and less efficient processes.
In addition to organizational culture, choosing the right tools is also essential. Solutions that offer scalability and automation support ensure that the unified infrastructure can keep up with the company's growth and future demands.
With the right tools, organizations can rely on AI and ML to make it easier to analyze large volumes of data and to detect evolving threats.
Finally, incident response automation should also be considered a priority. Implementing an automated response policy allows corrective actions to be taken immediately upon detection of an incident, which reduces reaction time and minimizes potential damage.
In cloud environments where everything happens in real time, this speed of response is essential to prevent major impacts and ensure the continuity of the operation.
Accelerate Your Digital Journey with Stefanini Data & Analytics
The complexity of the cloud poses a constant challenge for organizations looking to innovate without compromising the security and stability of their operations. Unified observability and security emerge as a powerful solution to simplify the management of modern IT environments, accelerating response time, and reducing operational overhead.
Companies that adopt this approach can ensure the integrity of their digital environments, facilitating innovation, and reducing costs.
The unification of observability and security is a necessity for companies that want to remain competitive and well-prepared for the challenges of the digital future.
Take control of your cloud security and unleash your team's full potential. With Stefanini Data & Analytics, you gain visibility, agility, and efficiency. Speak with one of our experts today.

The use of Artificial Intelligence for process automation

In recent years, the concept of hyperautomation has gained prominence in the business scenario. This trend, which combines various technologies, notably artificial intelligence (AI), to automate processes in a deep and efficient way, is revolutionizing the way companies operate.
However, hyperautomation goes beyond simple automation: it is a comprehensive approach to identifying, automating, and optimizing all processes that can be replicated by machines. Check it out!
After all, what is Hyperautomation?
Hyperautomation is a concept that goes beyond traditional business process automation. Unlike conventional automation, which often focuses on repetitive and isolated tasks, hyperautomation combines AI, machine learning, robotic process automation (RPA), and other advanced technologies to create a more fluid workflow in which the machine is able to learn, adapt, and make decisions.
To be more precise, hyperautomation unites digital tools that work together to discover, analyze, design, and even monitor processes, aiming for more sophisticated and complete automation.
Consequently, by enabling automation that does not rely solely on pre-defined schedules, hyperautomation provides a new layer of intelligence and adaptability.
How Artificial Intelligence Powers Hyperautomation
AI is a key pillar of hyperautomation. Its ability to understand and process large volumes of data allows businesses to optimize complex processes.
While RPA takes care of mechanical and repetitive tasks, AI extends the automation capacity, bringing intelligence to workflows. Specifically, AI contributes in areas such as:
Data Analysis and Decision Making: AI can process a massive amount of data in seconds, something that would be unfeasible for a human team. With machine learning techniques, it is possible to identify patterns that allow you to improve processes, predict failures, and even recommend actions that increase efficiency.
Automation with Pattern Recognition: Instead of relying on rigid rules, AI is able to learn from data and adapt to different scenarios. This is essential in processes that involve interaction with customers, document analysis, or any task that requires contextual interpretation.
Natural Language Processing (NLP): NLP tools allow AI to understand and interact in natural language, making it easier to automate processes that involve understanding text or dialogue. An example of this is automated customer service, where bots can solve basic questions and interpret complex questions.
Benefits of AI Hyperautomation for Businesses
Hyperautomation, when implemented strategically, offers benefits that go far beyond reducing operating costs. Below are some of the main ones:
• Increased Productivity: AI allows tasks to be performed at a much faster speed than humans, maintaining consistency and minimizing errors. Processes that used to take hours can now be completed in seconds, allowing teams to focus on more value-added tasks.
• Scalability: AI-powered process automation allows companies to scale their operations without this meaning a proportional increase in costs. For example, a company can increase its customer service by utilizing virtual assistants to handle frequently asked questions.
• Improved Customer Experience: With the help of AI, companies can offer faster and more accurate service, as well as personalized service. Data analytics allows businesses to understand customer preferences, anticipate their needs, and foster a stronger relationship.
• Data-Driven Decisions: AI provides a detailed analysis of KPIs (key performance indicators), facilitating more assertive and strategic decision-making. This helps companies quickly correct any deviation and adapt to new market demands.
Technologies that combine Hyperautomation
To achieve hyperautomation, companies use a combination of technological tools. Here are some of the main ones:
RPA (Robotic Process Automation): A tool for automating repetitive tasks that don't require much intelligence. RPA is an excellent foundation to start the journey towards hyperautomation, as it reduces human errors and frees employees for more strategic tasks.
AI and Machine Learning: AI brings the capability of machine learning to hyperautomation, enabling machines to understand data patterns, detect anomalies, and make predictions to optimize processes in real-time. In this way, it complements RPA, adding intelligence to automation.
Process Mining: A tool that identifies and analyzes processes in detail, providing a clear view of the workflow and highlighting areas that need automation. Therefore, it is an essential initial step to understand where automation will be most impactful.
Integration Platforms: With multiple tools being used simultaneously, platforms that integrate systems and data are indispensable. These platforms facilitate the connection between AI, RPA, and other technologies, making the workflow more cohesive and fluid, which is crucial for the effectiveness of hyperautomation.
Challenges of Implementing Hyperautomation
Implementing hyperautomation is not a simple process, as it involves the integration of various technologies and the transformation of business processes on a large scale.
One of the most common challenges is the complexity of integration between the tools, which requires detailed technical planning and the continuous support of specialists.
In addition, cultural change within the organization is critical to the success of hyperautomation. Many employees may feel threatened or skeptical of advanced automation, making change management an essential factor.
For hyperautomation to be well accepted, it is important to educate the team about the benefits of technology, demonstrating how it can facilitate and enhance their activities.
Another relevant challenge concerns security and compliance issues. Hyperautomation handles large volumes of sensitive data, increasing the company's responsibility in terms of data protection and regulatory compliance.
Therefore, security must be prioritized throughout the implementation process, ensuring that automation is carried out ethically and safely.
Examples of Hyperautomation Use in the Market
In the financial sector, hyperautomation is used to validate documents and perform credit analysis efficiently and safely, reducing approval time and reducing the chances of errors.
The healthcare industry also benefits, with hospitals automating chart management and using AI to analyze tests and support diagnoses.
In retail, hyperautomation contributes to more accurate inventory management and a more personalized service, by allowing the analysis of customer consumption behaviors. This helps to create targeted marketing campaigns and customized shopping experiences.
In industry, hyperautomation facilitates the monitoring of machinery and the control of production processes, reducing the waste of resources and improving the efficiency of operations.
Maximize Your Productivity with Hyperautomation Solutions from Stefanini Data & Analytics
Hyperautomation, with the support of artificial intelligence, is a strategic differential for companies looking to transform their processes with efficiency, scalability, and focus on the future.
At Stefanini Data & Analytics, our experts develop customized hyperautomation solutions designed to optimize operations, improve the customer experience, and drive results with security and innovation. Process automation Hyperautomation Artificial Intelligence Stefanini Data & Analytics

Reframing Data with DORA: Learn How to Be Data Efficient

Many companies deal with a large volume of data on a daily basis, but at the end of the operation, they end up not extracting the full potential they could from this asset. This is because a series of factors related to management, storage, and the method used to analyze everything that was collected.
However, with the modern market demanding more and more precision in relationships with consumers and partners, not having data efficiency is wasting business opportunities.
We interviewed Marcelo Gallota, an expert at Stefanini Data & Analytics, to talk more about data management and how DORA, the company's proprietary system, can help with this task.
What is data management?
Data management is the process of collecting, storing, organizing, analyzing, protecting, and effectively utilizing information in an organization. This can be done from a set of resources and techniques of the technology team while allied to sectors such as legal, marketing, sales, service, logistics and finance, and others that make sense for the business offer.
It also represents legal compliance with the rules of the LGPD (General Data Protection Law), prioritizing the secure collection, handling, and access of data in a secure and private way. As it is an obligation, companies must have management focused on data efficiency so as not to have problems.
What are the challenges for companies with data management?
The first and most common business challenge when dealing with data management is to keep data secure and available using the maximum possible optimization of infrastructure resources, especially in a large volume of information that enters its banks daily. After all, when the quantity is massive, the observability and quality of the extractions is not always adequate for the objectives.
After this issue has been resolved, managers will have an easier time visualizing and gaining insights into their operations. In this way, management will be based on business intelligence, allowing for better decisions that are aligned with goals, both in the short and long term.
Additionally, adapting to the LGPD is another common challenge for modern companies. However, with data management aligned with the requirements of standards and laws, it is easier to ensure compliance and avoid problems with legislation.
How to achieve data efficiency?
The first step to achieving data efficiency is to align all sectors of the company to this strategy. We have separated some of the points that should be part of the company's routine. See:
Create a plan to manage your data: Have a clear plan that unites the company's goals with the use of data. This involves setting specific goals and understanding what data is important, establishing rules for collecting, keeping, analyzing, and using it;
Follow patterns and automate processes: When possible, use patterns and automate data collection, storage, and analysis. In addition to avoiding errors, it keeps everything consistent and saves time and resources;
Maintain data quality: It is important to establish rules to maintain data quality. Regularly checking and cleaning data, maintaining a standard format, and having quality controls in place are all parts of this process. · Prioritize data security: Data security is indispensable. To do this, put strong measures in place to protect data from unauthorized access, loss, or damage;
Encourage collaboration and data sharing: Have different teams and departments work together and share data securely. This prevents duplicate efforts and helps data be used more effectively across the enterprise;
Measure and improve performance: Establish ways to measure performance and regularly monitor how things are going. Identify areas for improvement and adjust to become more efficient over time.
Use the right technologies: Investing in tools that fit the company's needs can make a big difference, including database systems, analytics programs, cloud storage solutions, and so on.
What is DORA's role in data efficiency? As you have noticed, to achieve efficient data management, the entire company needs to be aligned with the collaboration paradigm that this implies. However, the aspect that most facilitates the collection and administration routine as a whole is the use of efficient software, capable of handling data more quickly and securely.
According to Marcelo Gallota, "DORA, "a system offered by Stefanini Data & Analytics, was originally designed to handle the storage, organization of data and efficient access at low cost." However, in solving this problem, they identified an additional opportunity and extra value in historical data, which is often held for many years due to regulatory compliance concerns.
This historical data has significant analytical value. However, before DORA came in, it was not even possible to think about monetizing this data due to storage and access limitations.
With the introduction of DORA, the first problem was solved, and they were able to identify the opportunity to "reframe" the data. That is, transforming data that previously had low monetization potential, because it is purely historical, into data with high monetization potential, now that the system offers storage capacity and efficient access.
But how does DORA work?
DORA is a database specifically designed to handle large volumes of data in companies. It is intended to solve challenges related to the efficient storage, access, cost, and integration of this data into enterprise architectures.
To achieve this goal, the system offers compression capabilities that allow you to reduce data size by up to 90%, which helps optimize storage usage and improve overall efficiency. As such, DORA acts like a traditional database, but with additional features that make it uniquely suited to address the specific challenges faced by companies that handle large volumes of data—such as telecommunications, finance, HR, and other industries.
In practice, this resignification is the differentiated treatment with each piece of data, considering in which situations DORA can be used that are not part of a traditional scope. This allows the same data to be used in multiple areas, without taking up more space and serving plural purposes instead of a singular analysis, according to Marcelo Gallota.
And what benefits does DORA bring?
The main benefit is the enormous efficiency brought by the compression and consequent reduction of infrastructure resources (mainly storage). In addition, DORA is easy to integrate with any element of the company's data architecture, enables the monetization of historical data and helps companies reduce their carbon footprint due to the large reduction in infrastructure usage.
Now you understand why data management and efficiency cannot be neglected. With DORA organizing, processing, and displaying data with even more agility, it will be much easier to obtain valuable business insights, which will change the financial direction of your company in the medium and long term.
Get the best possible results when it comes to business management and data efficiency. Contact us and learn about even more benefits that DORA has to offer!

LORE: Turn Data into Business Insights

Creating intelligence in business decision-making is no longer a differential, but a necessity in building a strong and profitable brand. To this end, understanding what LORE is and how this platform can provide accurate, current, and reliable data is an important step in this direction.
To explain how this tool works in practice and what are the best ways to use it in your daily life, we invited Filipe Cotait, CEO of Stefanini Data & Analytics.
During this content, you will understand how valuable data is to maintain your company's competitiveness, as well as discover how companies are already benefiting from this technology.
What is LORE? "LORE is a DaaS (Data as a Service) platform, that is, you only pay for the data consumed, without having to pay for all the resources necessary for capturing, cleaning, processing and organizing the data", explains Filipe.
It was created to offer users a complete analytical journey, regardless of the nature of the data used, whether external or internal. Therefore, LORE can deliver any type of public data from various sources — such as APPs, websites, and social networks — in an automated way and at scale.
What are the benefits of LORE?
This tool directly contributes to your company being able to extract relevant information from data, even if it is spread across different sources, creating a broad and unified view of current market conditions. See below what other advantages of the platform for your brand:
Payment only on data: as the contracting model is "as a service", your company only pays for data — from apps, websites, social networks, and other sources — that are delivered according to your demand, frequency, and need;
Zero additional costs: the platform guarantees flexibility for creating, expanding, relocating or updating data sources at no additional cost, so that new data sources do not cause an increase in expenses with capture structuring;
Easy storage: if there is a need to retain the data collected for the creation of historical records, it is possible to hire this service to consult whenever necessary.
How does LORE help with business process improvement?
By including LORE in the business routine, your team has access to all this infrastructure, as well as dashboards and customized reports to simplify the interpretation of the recorded information. This is only possible because the platform's tools include AI, machine learning, and predictive analytics.
Facilitates decision-making
More and more business decisions are supported by insights produced by analytical processes that employ the best practices of each industry. They, however, depend on diverse, correct, continuous, and updated information so that the strategies adopted are aligned with the reality that the company faces.
Eliminates the need for a development squad
In addition to all the support in business decision-making, LORE offers an important means to overcome the challenges with the lack of qualified labor and knowledge in Big Data.
In practice, this technology offers great savings compared to the creation and maintenance of a development squad and infrastructure resources dedicated to data collection.
Increases operational efficiency
With access to data from various sources, your team is able to increase operational efficiency, reduce costs, launch new products, and meet new market demands. Thus, you focus only on the use of data, leaving the technological part to LORE.
Why use LORE in your business?
Using LORE in your business is an option to feed your business intelligence areas with updated data and constantly creating a differential for your business. According to Filipe, "the analyses applied to data help in the definition of strategies for action in various industries: retail, food techs/delivery, e-commerce, service companies and industries".
In addition, he cites several uses of LORE that motivate companies to use this solution, among which are:
Mapping of promotions carried out by large retailers;
Monitoring of product prices to the final consumer;
Mapping of payment methods accepted in commercial establishments;
Study of the products manufactured by an industry in its various sales channels, including the sale price to the final consumer;
Mapping of fuel prices with geolocation;
Customer prospecting, verifying that a certain product of theirs is not sold by a channel, however this channel sells from a competitor.
In addition, it is possible to integrate with various tools, according to the needs of your enterprise. Learn more about this topic below.
How to integrate LORE?
Integrations with other systems are essential to take advantage of the available tools even more and this is another of LORE's differentials. Through it, your company can use data even more efficiently.
In this sense, Filipe points out that "The data delivered by LORE can be integrated in different ways and formats. At the beginning of the project, the client informs us how he wants to receive and integrate the data and, based on this definition, it is made available.”
It is worth remembering that having the support of Scala's specialized professionals in the implementation of this tool is another differential that your company can take advantage of. Thus, the chances of achieving sustainable growth are even greater.
What type of company is LORE recommended for? "LORE can be used by companies of any size, as long as there is a need to consume large volumes of data continuously. For example, a startup may have the need for high-volume data to generate its new product or business, as well as large companies," explains Filipe.
He also points out that "our role is to assist the client in structuring and choosing the origin of the data, thus bringing the most appropriate data and at the best costs to the business objectives". Once this study is done, the entire process of capturing and delivering the data is delivered by LORE.
Now that you know what LORE is and how it can help your business, how about starting to take advantage of all the advantages of this tool in your business? By relying on Scala and our tools, the chances of standing out in the market and increasing profitability are very high. Want to use LORE in your business? So, contact usright now and talk to one of our experts!

LORE, ALICE.AI and DORA: The role AI analytics plays in your analytics journey

One of the biggest mistakes that modern managers make is to think that collecting and accumulating data is enough to achieve digital transformation. But what is the point of having data simply for the sake of accumulating?
To succeed today, they must be used to answer vital business questions that are adherent to customer strategies. AI analytics is a new approach that uses automation and artificial intelligence to empower a company through data.
In this special article, we invited Antonio Pádua, Director of Data & AI Sales at Stefanini Data & Analytics, to talk about the trio of solutions capable of bringing the power of data to your routine. Follow along.
How do Stefanini Data & Analytics solutions help companies achieve efficiency and productivity?
For some time now, companies from all sectors of the market have already realized the value of information for modern competitiveness. It's such a prevalent fact that 91.7% of businesses say they're increasing their investments in data technology.
However, many of these institutions fall into the trap of becoming digital hoarders: they have stuffed banks, but there is no connection between the stored records and their analysis for practical decisions on a daily basis.
"We have to understand that data is one thing and analytics is another," says Antonio. The true power of information comes from a complete structure that treats it from the moment it enters the system, through the organization to its use.
It is with this in mind that Stefanini Data & Analytics has developed three solutions as pillars of AI analytics: LORE, DORA, and ALICE.AI. With a focus on different stages of the data journey and vision of the Analytics discipline, they create a complete and integrated environment that is constantly monitoring and presenting insights for quick decision-making.
If you want to learn more about each of these tools, see below.
How does LORE consolidate its database?
LORE is a platform for automating and structuring the entry of data into the system, both from internal sources and the insertion of internal information generated in the business routine.
"LORE solves a problem for the company, which is how to collect external data." The expert also suggests the following scenario:
"Imagine a business in the retail market that needs to know how its competitors are doing for a next best offer strategy. In this case, it is possible to go to public data such as websites and applications and extract them in a structured way to enrich your base."
One of the great advantages of the LORE platform is to deliver data as a service, treated and ready to use in company insights.
This is possible with its modular construction, which connects collection, quality handling, storage and report management.
How does DORA structure and deliver data with high efficiency?
DORA is a Data Management solution, a very high compression database with significant space gain for efficient information management.
Its focus is on optimizing the relationship between people and processes and stored data, simplifying access, visibility, and providing operational agility. Again, Antonio proposes an example of use:
"Imagine the amount of data I have stored that I never worked with because it was expensive to resignify it to my current base. DORA has this ability to bring previously inaccessible data into its production environment and integrate it with current processes such as CRM, legal, financial and others."
With its high compression ratios, Dora further optimizes the need for storage space, reducing costs with servers or cloud services.
Like ALICE. Does AI deepen your business insights?
Once the data is retrieved, structured, and integrated by the previous solutions, it's time to seek the insights for modern competitiveness. This is where the ALICE solution comes in. AI.
The platform is focused on AI analytics, that is, the use of artificial intelligence to segment, compare, and contextualize data in a business bank.
Its goal is to provide deep insights that have real applicability in market strategies, recognizing patterns and trends before competitors.
To this end, it has a simple and intuitive interface that seeks to answer questions based on internal and external indicators, returning with accessible and assertive reports: identification of opportunities, isolation of bottlenecks, scenario projections, etc.
As the expert explains, analytics insights need to be connected to your business strategies. You need to know what you need to receive the most appropriate answers.
The questions may involve practical business issues, such as production forecasts, customer relationships, etc.
Raising the right questions, ALICE. AI analyzes your database in all aspects: production capacity, costs, pricing, trends, among others to suggest the most relevant strategies in relation to your goal.
It's a way to accelerate your business in every aspect. You reduce decision-making time, anticipate operational plans, and reduce unnecessary process costs. It is transforming data into business intelligence.
What are the differentials of Stefanini Data & Analytics solutions?
What sets Scala solutions apart from other options on the market is their understanding of what it means to use data in the digital age.
Having business intelligence is not just about having a lot of information. It is to have an automated and customized channel for treatment, structuring and practical use of it.
That's why we have all three platforms in a complementation approach. Each one acts on a crucial pillar of digital transformation.
LORE optimizes and simplifies data collection by creating reliable flows of information from both internal and external sources, including:
· Solution focused on business objectives
· Payment only for the use of the data
· Abstracting from complexity, delivering out-of-the-box data
· Autonomy to use public data without internal projects
· In-depth knowledge of data disciplines
DORA structures and organizes this information in high-throughput and compact databases, streamlining and making it cheaper to treat, monitor and use processes, providing:
· High data compression ratios
· Fast query speed even at high volumes
· Broad integration compatibility (API, Web services, native SQL)
· Excellent performance and cost-effectiveness in cloud environments
· Validated solution and operating in large telecom companies
ALICE.AI uses already structured data to provide impactful insights, which speed up decision-making, generate business innovation, and reduce risks, bringing to the business:
· Knowledge trees
· Accelerated predictive and prescriptive models
· Alignment of model results with business strategies
· Prescriptive outcomes focused on identifying business actions
· Proposes actions according to the intensity of the client's pain
· Ease of maintenance of the models
· Collaborative work with multiple models simultaneously
· Reusing hypotheses for other models
Going beyond the quality of our solutions, it is important to remember that we do not only deliver the tool, but also the service of implementing and using the solutions.
"In the case of ALICE. AI, for example, the client does not need to have a data team, we have our consultancy. Even if the client doesn't even know the questions to ask to generate insights, we can apply our methodology to create the ideal environment to receive the best answers."
In other words, our mission is to make business intelligence accessible to any business manager.
Which Stefanini Data & Analytics solution to start with?
The three Stefanini Data & Analytics solutions are designed to work as a single flow for data processing and AI analytics. But the way you invest and use the platforms can vary.
The keyword raised by Antonio is digital maturity. There is no point in trying to implement ALICE. AI, for example, if you don't have a consolidated database in the company.
Therefore, it seeks a logical progression for implementation that goes through the three pillars: LORE for collection, DORA for architecture, and ALICE. AI for insights.
It is also worth remembering that platforms, although automated, need a personal organization and adequate processes to bring the expected returns.
That is why we offer consulting services with specialized methodologies in Business Intelligence and AI analytics.
In an ideal progression, you can reach the implementation of the trio of solutions in an integrated way and completely inserted in your routine, without disturbing current processes.
Investing in data technology is investing in the future. As Antônio concludes, "it is necessary to understand that an analytics strategy behind its success is fundamental".
Those who have power over information today have a closer relationship with the public and can identify market trends faster.
Not only that, early access to change patterns allows you to make decisions with greater growth potential, taking into account all possible future scenarios.
For these reasons, investing in AI analytics is investing in consolidation. Learn more about LORE, DORA and ALICE.AI solutions now. Contact our consultants to schedule a demo.