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A decision-focused Atlassian Cloud migration strategy guide, covering architecture, apps, identity, and adoption beyond tools and timelines.

Learn how to move from Atlassian Data Center to Cloud with less risk, better ROI, and access to Atlassian’s latest innovations.

Discover four key reasons why 2026 is the ideal year to migrate from Atlassian Data Center to Cloud and how to prepare your organization for the transition.

Adopting cloud modernization meets current demands and builds a foundation for the future. Understand why AWS is the key to increasing your business value.

Energisa modernized 70 systems by migrating to AWS with e-Core’s support, gaining scalability, availability, and performance in the energy sector.

A successful cloud migration doesn’t start with tools, it starts with the right plan . Download this guide to discover a structured, strategic approach to migrating and modernizing workloads on AWS . Whether you’re just starting your cloud journey or looking to optimize an existing environment, this whitepaper gives you the clarity and framework to do it right. What you’ll learn:  A 3-phase cloud assessment and planning framework AWS tools that support discovery, migration, and modernization How to reduce cloud spend, improve security, and scale with confidence Real-world planning strategies, including containerization, serverless, and data modernization

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. Start your data journey today.

The Client

Virtualization has revolutionized how companies manage their IT environments, offering flexibility, scalability, and cost reduction. However, the recent change in VMware’s licensing policy has led many of their clients to reconsider their virtualization strategies. In this transformative scenario, running virtualized environments in the cloud, such as a virtualization on AWS, can emerge as an attractive and viable alternative. Amazon Web Services (AWS) , for instance, the leader in the cloud computing market, offers robust virtualization solutions, providing flexibility, scalability, and efficiency for organizations of all sizes. What is Machine Virtualization? Machine virtualization is a technology that allows for the creation of multiple instances of operating systems on a single physical hardware. This enables various applications and operating systems to run simultaneously, optimizing resource use, offering greater flexibility, and simplifying management while significantly reducing infrastructure costs. By abstracting the physical hardware, virtualization enables more efficient and dynamic IT resource management. What are the advantages of virtualization on AWS? In the context of AWS, virtualization is fundamental to its infrastructure-as-a-service (IaaS) offerings. AWS uses its own virtualization technology, which allows for the creation of virtual machine instances (EC2) in a highly efficient manner. These instances are based on a lightweight hypervisor developed by AWS, called Nitro , which provides low overhead and maximizes the performance of virtual machines. Additionally, AWS facilitates the migration of existing virtualized environments, including those on VMware, directly to its cloud with the AWS MGN (Application Migration Service) , which minimizes time-consuming and error-prone manual processes by automating the conversion of servers for native operation on AWS. Virtualization on AWS offers several significant advantages: Scalability : Users can quickly scale resources up or down as demand changes, paying only for what they use. Cost-efficiency : It eliminates the need for upfront investments in hardware and reduces ongoing operational costs. Flexibility : It offers a wide range of instance types optimized for different workloads and allows the use of various AWS pricing models, enabling the choice of what best suits the company at every stage without surprises from pricing changes. High availability : AWS’s global infrastructure allows for the distribution of applications across multiple availability zones and regions, enhancing resilience. Security : It provides advanced security and compliance tools, including network isolation and data encryption. Simplified management : It offers intuitive tools for monitoring, automation, and management of virtualized resources. Is it smart to migrate from VMware? Migrating a virtualized environment from VMware to AWS represents a strategic opportunity for organizations. By leveraging AWS’s robust infrastructure and innovative services, companies can overcome the challenges imposed by VMware’s licensing changes and reach greater levels of operational efficiency, flexibility, and innovation. Virtualization on AWS offers a clear path to IT infrastructure modernization, enabling organizations to focus on their core business while enjoying the benefits of a market-leading cloud platform.

Discover how our Business-Aligned Technical Modernization solutions replace outdated systems with agile, secure, and scalable cloud-native technology. From optimizing your infrastructure to tackling complex data challenges, our expert team supports your business at every stage of modernization. Download the full content to learn how we can help turn your tech stack into a sustainable growth and customer value driver. →

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