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A top insurer aligned data and AI to cut waste, speed up delivery, and turn preauthorization into a fast, accurate, real-time process.

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Artificial Intelligence solutions are increasingly integrated into business operations, transforming decision-making and process optimization. However, the adoption of AI data preparation still faces significant challenges. According to a global Precisely survey from September 2024, 66% of organizations say that poor data quality and lack of data governance are the main obstacles to the success of AI . This data highlights a critical point: the success of AI projects depends essentially on the quality of the data. For Artificial Intelligence to be effective, it is essential to rigorously collect, organize and prepare the data, guaranteeing the accuracy and reliability of the models . Without a reliable foundation, even the most advanced models fail to deliver real value. This raises a fundamental question: Is your company really prepared to turn data into a competitive advantage? Or are you still accumulating information without a clear purpose? Find out how to structure your AI data preparation process and unleash the full potential of AI to drive your business forward. The 3 essential pillars for a successful data journey An organization’s data journey is a dynamic and continuous process of evolution, where data centralization, quality, and governance become intertwined as the company matures. These three pillars are not isolated stages, but rather mutually reinforcing dimensions . Let’s see how they relate to each other and influence the evolution of data maturity in an organization: 1. Unifying your data: centralization and integration One of the first challenges in the data journey is overcoming the dispersion of information. Many companies find themselves with valuable data spread across several different systems , departments and formats, a scenario known as “data silos”. For example, imagine having customer data in a CRM, sales information in spreadsheets and stock data in legacy software. This fragmentation prevents a unified view of the business. The solution? Unify this data in a centralized repository, such as a data lake, data warehouse or cloud solution. This centralization ensures that everyone in the organization has access to a consistent and complete view of the data, allowing for more accurate analysis and informed decisions. Centralized access is one of the first steps toward a scalable and sustainable AI data preparation process . 2. Ensuring accuracy: data quality and reliability After unifying your data, the next step is to ensure its quality and reliability. In large volumes, data can easily become disorganized , inconsistent or inaccurate . This process of refinement is vital, as poor quality data inevitably leads to inconsistent AI models and, consequently, wrong business decisions. High-quality, reliable data is the foundation of any successful AI initiative. Machine Learning models, for example, are trained based on this data, so if the data is compromised, so is the model’s performance. By investing in data quality, your company guarantees that analysis and predictions will be accurate, and that the decisions made based on these insights will be more effective and strategic, reducing operating costs by avoiding errors and rework. When building your AI data preparation foundation, quality is not optional, it’s essential to enabling trust and value. 3. Protecting and managing: data governance and security With your data centralized and its quality assured, the next step is to establish a solid governance and security structure. Data governance ensures that information is used ethically, responsibly and in compliance with laws and regulations. It defines clear policies on who can access what data, how it can be used and how long it should be stored. At the same time, data security protects the company’s most valuable asset from unauthorized access, loss or damage. This involves implementing technical measures such as encryption, firewalls and intrusion detection systems, as well as organizational measures such as security policies and employee training. Effective data governance and security protect the company from legal and financial risks, and promote trust among customers and partners. By demonstrating a commitment to data protection, the company builds a solid reputation and opens doors to new business opportunities. Good governance is what turns a well-intentioned AI data preparation initiative into a secure, scalable, and sustainable strategy. Business benefits A well-structured data journey is not just a technical requirement, but a strategic differentiator that generates real impacts, such as: Faster and more accurate decisions: reliable data makes analysis more agile and strategic. Reduced operating costs: automation based on data optimizes processes and eliminates inefficiencies. Personalized experiences: data analysis makes it possible to create interactions that increase customer loyalty. These benefits reinforce the strategic role of data in the competitive positioning of companies , highlighting the importance of investing in its organization and preparation through a clear AI data preparation approach. Conclusion The data journey is more than a technical process; it is a strategic differentiator for companies that want to thrive in an AI-driven market. By centralizing, qualifying and governing their data efficiently, organizations are positioned to turn challenges into opportunities, driving innovation and generating tangible results. AI data preparation is the first step toward unlocking the full potential of Artificial Intelligence in the business world.  At e-Core, we help companies turn data into powerful insights, preparing them for the future of AI. 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Accurate and reliable data collection is crucial for businesses in retail and e-commerce. It enables organizations to make informed decisions and gain a competitive edge by leveraging insights to optimize operations and understand customer behavior. In today’s digital landscape, data-driven strategies have become indispensable for adjusting inventory levels, tailoring marketing efforts, and forecasting trends. Key areas for effective data collection Customer behavior lies at the heart of any successful retail or e-commerce strategy. Predictive models, built on data collection, allow companies to anticipate trends with accuracy. By understanding purchase patterns and preferences, businesses can not only optimize inventory but also refine marketing campaigns. This ensures they stay ahead of demand and cater to customer expectations in real time. Website interaction is another vital area. Tracking how customers navigate and interact with an e-commerce platform helps companies improve the user experience. It’s not just about gathering data, but about using that data to create a seamless shopping experience that keeps customers engaged and encourages conversions. Preparing to leverage data Before diving into advanced analytics, it’s essential to lay a strong foundation by understanding the unique aspects of the business. Each company is different, and the approach to data collection must be tailored. For instance, selling Christmas trees and selling cars involve entirely different retail practices. To effectively integrate data collection points and predict customer behavior, the nuances of each business must be recognized. At e-Core, we’ve seen this firsthand with clients where in-depth collaboration with business teams was key to success. This deeper understanding allows for fine-tuning caching solutions and enhancing performance, a crucial step in optimizing any data-driven product . Identifying key areas for data collection, such as customer interactions, purchase patterns, and website navigation, sets the stage for more sophisticated analytics. Case study: Improving performance through data At e-Core, we worked with a retail client to implement strategic data collection points aimed at improving overall performance. By gathering insights from customer behavior and refining caching solutions, we boosted the client’s website performance while enhancing data accuracy. You can learn more about this study here. The power of data analytics cannot be overstated. It transforms raw data into actionable insights, improving everything from supply chain management to marketing success. With the right tools in place, businesses can drive efficiency and profitability, positioning themselves for long-term success in an increasingly competitive landscape.

Data, when viewed as assets, doesn’t automatically generate value. A favorite analogy of mine, taken from the book “ Data is Everybody’s Business: The Fundamentals of Data Monetization ,” is to see data as seeds. Simply planting the seed and relying on the natural effects of sun and rain without any effort might result in a plant: these are the insights we naturally obtain just by organizing data. However, left to natural processes alone, it’s clear that the plant won’t grow as much as we’d like it to. To achieve this, we need to water, fertilize, and care for it: this action is equivalent to treating data to extract value. With this effort, the plant can bear fruit, which equates to the value of the data. But if these fruits aren’t harvested and utilized, they may fall and rot. So, in addition to creating value, someone needs to oversee and manage it. Just like in our analogy, data products generate different levels of value. They can be categorized into three types: Improvements, Wrapping, and Information Solutions. 

Artificial Intelligence (AI) is a transformative force in today’s digital landscape, yet terms like Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) are often used interchangeably, leading to confusion. This article aims to demystify these concepts, highlighting their differences and significance in the current industry. Investments in AI are becoming increasingly substantial ( “Stanford AI Index report” published at World Economic Forum , KPMG , Unite.AI , All About AI ). However, many people still lack a clear understanding of the differences between AI, ML, DL, and GenAI. This gap often leads to unrealistic expectations and misdirected investments. So, how do these technologies differ, and how can we apply them effectively? Our goal is to provide a clear understanding of these concepts, presenting the perspective of authors Ian Goodfellow, Yoshua Bengio, Geoffrey Hinton, and Andrew Ng. They have extensively addressed these concepts and their applicability in solving specific problems, which will be discussed throughout this article. What is Artificial Intelligence (AI)? AI is a broad field within computer science that aims to create systems capable of performing tasks that typically require human intelligence. It encompasses various technologies, from rule-based systems to advanced algorithms that can learn and adapt. Implementing AI can be complex and expensive, requiring high-quality data and sophisticated algorithms. AI is classified into two categories: Narrow AI, designed to perform specific tasks, and General AI, which can perform any intellectual task a human can. Within AI, Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) are specialized subsets, each designed for different objectives and varying in complexity. These technologies, while interconnected, serve distinct roles in advancing AI’s capabilities. 

A Digital Transformation Journey through the D2E Program Executive Summary A leading company in the port sector partnered with AWS and e-Core to participate in the AWS D2E Mobilize workshop. The goal was to drive their digital transformation by creating an Integrated Logistics Intelligence platform meant to integrate and automate the logistics processes of their clients, providing an end-to-end view of transportation. Workshop Objectives The workshop had two main objectives: Develop a roadmap for the creation of a commercial control panel, providing a 360° view of customers by consolidating data from various business areas and supporting commercial negotiations. Establish a delivery plan to make the MVP (Minimum Viable Product) feasible, enabling subsequent deliveries of business value. Additionally, the goal was to bring together all responsible leaders and decision-makers to ensure that the MVP delivers real gains for the company. This collaborative effort aimed to understand each area’s relationship with its customers and partners, addressing both technical and operational needs. >> Read also: How to be data-driven? Start by answering these 5 questions The Vision The vision for the Integrated Logistics Intelligence platform was to use internal and external data to provide a comprehensive solution for logistics integration and automation. The platform aims to: Provide complete visibility and control of transportation. Generate relevant insights for more efficient commercial negotiations. Offer real-time notifications about relevant events for the clients’ cargo. Recommend actions to mitigate predicted impacts. Demonstration A practical example demonstrated how the platform could predict delays in vessel arrivals and suggest logistical adjustments to avoid additional costs, significantly improving operational efficiency and customer satisfaction. Expected Business Results The benefits expected to be achieved by the company include: Commercial efficiency: increased revenue and margin per customer, and reduced customer acquisition costs. Customer experience: increased customer retention and loyalty, and enhanced Customer Lifetime Value (CLTV). The benefits anticipated for customers include: Operational efficiency: reduced average delay time per transported cargo, reduced cash provisioning for delays and unexpected costs, and decreased downtime of production lines. The MVP: Commercial Control Panel Focus areas: 360-Degree Customer View and Insights Generation: The MVP will create a comprehensive 360-degree view of customers by consolidating data from various business areas (terminals, tugboats, agency) to generate commercial insights (leads) for specific business areas. Data Platform Consolidation: The MVP will consolidate data from operational systems and market data. Commercial Representatives’ Insights and Lead Ranking: Views for Terminal and Tugboat representatives will rank high-value opportunities (leads) and provide feedback to improve the ML model. Tugboats’ commercial team insights will be integrated into the systems. Group-Level Commercial Efficiency View: A group-level view will offer end-to-end customer visibility, including metrics like total billing and normalized revenue (R$/hour), to understand good and bad customers globally. Implementation approach: Solution Foundation: Establish a flexible and scalable architecture as the foundation for the organization’s data environment, enabling future solution development. Data Source Integration: Initiate the collection of data from various sources to support data processing efforts and create a comprehensive knowledge base accessible to different teams. Data Enrichment: Enhance the value of existing data by cross-referencing and integrating information across the organization to generate new insights. Analytics Capabilities: Set up the architecture for analytics, including data loading processes, and develop dashboards to provide actionable insights. Knowledge Sharing: Facilitate knowledge transfer between specialist teams and other departments to ensure broad access to expertise and promote informed decision-making. Scaling Quickly The evolution of the platform will include: Training Machine Learning models for predicting and mitigating delays, leading the company to become more data-driven , knowing more about their data and generating new ones to make smart decisions. Processing unstructured data to generate additional insights. This will make the data access easier from different teams through data standardization. Developing APIs for data integration and sharing with clients and partners helping the data consumption faster for less technical teams and simpler for partners. Future Roadmap The next steps for the project include mapping customer journeys with new features, data architecture evolution and understanding. Develop recommendation engines for future actions and modernizing the customer experience with new applications and technologies as the organization understands how the new data products are helping them. Conclusion Participation in the D2E Mobilize program and the partnership with AWS and e-Core provided a unique opportunity for a leading company in the port sector to advance its digital transformation, improve operational efficiency and customer experience, and reach new heights in logistics innovation.

The Challenge Founded in 2017, a55 is a fintech that provides significant financial support to new economy companies. It offers solutions for companies in the service industry, such as ERP’s, CRM’s, and Marketplaces, which have clients with predictable revenues.  However, they faced challenges with their data architecture that hindered efficient credit analysis, customer understanding, and portfolio recovery. The need for a robust and scalable infrastructure to support their data-driven credit offerings was critical for maintaining their competitive edge. The Solution: Data Architecture Modernization a55 partnered with e-Core to modernize their data architecture. The project involved a comprehensive review and enhancement of their cloud infrastructure according to the Well-Architected Framework and Data Lake Best Practices. Additionally, e-Core implemented Infrastructure as Code (IaC) to streamline deployment and management processes for all data platforms. These improvements enabled a55 to leverage data intelligence for more accurate credit offerings, enhancing their unique value proposition and fostering a data-driven culture . The Resu lts The collaboration with e-Core led to significant improvements for a55: 40% Increase in Capital Generation: Enhanced data architecture allowed for more efficient credit analysis and better capital allocation. 15% Improvement in Portfolio Recovery: Improved understanding of customer data contributed to more effective recovery strategies. 70% Reduction in AWS Costs: Optimized cloud infrastructure led to substantial cost savings. Launch of a New DeFi Product: The modernized infrastructure supported the development and introduction of a new decentralized finance (DeFi) product. The successful modernization of a55’s data architecture empowered the fintech to offer more precise credit lines based on data intelligence, solidifying its position as a key player in the financial services industry.