Digital transformation is no longer an option, but a necessity for companies that want to remain competitive in an increasingly dynamic and data-driven market. However, many organizations still rely on fragile and outdated cloud infrastructures, which not only limit innovation capacity but also compromise security, operational efficiency, and the ability to extract value from data. 

Reliance on obsolete IT infrastructures is a common challenge in many companies, especially those that have grown rapidly or adopted technologies without long-term planning.

According to research by Kyndryl, 44% of critical systems are close to or have already reached the end of their useful life. This represents a significant risk, especially in a scenario where the demand for data-driven insights is only increasing.

Modernizing these infrastructures is a strategic necessity to drive business results and ensure operational resilience.

However, modernizing a data infrastructure is not a simple task. It requires careful planning, significant investments, and, above all, a clear vision of the results to be achieved. 

The data skills shortage

One of the biggest obstacles to modernization is the lack of specialized data skills. The same Kyndryl research revealed that 40% of leaders face skills gaps that hinder modernization. Without qualified professionals in areas such as data architecture, data science, and systems integration, even the most robust investments can fail.

The talent shortage is a global problem, compounded by the rapid pace of technological innovation. Many companies are finding that technology is advancing faster than their teams' training and development capabilities. This creates a vicious cycle: companies need to modernize their infrastructure to stay competitive, but lack the skills necessary to implement and manage these changes.

The solution? Investing in training and enablement, besides considering automation and artificial intelligence (AI) to meet part of this demand. Generative AI, for example, can help decipher legacy code and create technical documentation, reducing dependency on mainframe-specific skills. Partnerships with specialized service providers can help bridge temporary skill gaps.

The data needed to achieve good results

Before starting any modernization project, it is essential to clearly define the desired business outcomes. What does the company expect to achieve? Improve customer experience, reduce operational costs, or increase efficiency? With these goals in mind, it is possible to identify what data is critical and how to collect, manage, and analyze it efficiently.

A common mistake is trying to modernize all systems at once, which can lead to exorbitant costs and unsatisfactory results. Instead, the approach should be focused and incremental, prioritizing the areas that will bring the greatest business impact. For example, a company might start by modernizing its CRM (Customer Relationship Management) systems to get a clearer, unified view of the customer, before moving on to other areas like finance or operations.

Another important point is ensuring that data is consistent and reliable. Many companies face the challenge of fragmented data, where different departments have different definitions and metrics for seemingly simple concepts, like "customer" or "revenue". This can lead to inaccurate analysis and misguided decisions. Modernization must include data standardization and integration, ensuring that all areas of the company are aligned.

Data trust and security to modernize

Data security is a central concern in infrastructure modernization. With the increased use of AI, the ability to access and query large volumes of data has also grown, along with the risks of breaches and cyberattacks. Kyndryl points out that 65% of executives are concerned about cyberattacks, but only 30% feel prepared to manage these risks.

Modernization must include the implementation of robust security controls, such as encryption, continuous monitoring, and restricted access policies. In addition, it is crucial to ensure that data is reliable and consistent, avoiding information silos that compromise decision-making.

Another important aspect is compliance with data privacy regulations, such as LGPD (General Data Protection Law) in Brazil or GDPR (General Data Protection Regulation) in Europe. Modernization must ensure that the company complies with these laws, avoiding fines and reputational damage.

Concerns about costs and innovation

Modernizing infrastructure can be expensive, but not investing in it can be even more costly in the long run. However, it is important to balance operational maintenance expenditures with investments in innovation. Seth Ravin, CEO of Rimini Street, suggests that companies allocate 30% to 40% of their annual budget to innovation, ensuring they do not become technologically stagnant.

Migrating to the cloud is not always the most cost-effective solution. In some cases, keeping on-premise systems or adopting a hybrid approach may be more advantageous, especially for companies with stable computing demands. The key is to evaluate each case individually, considering factors like migration costs, performance needs, and security requirements.

How to leverage AI for modernization gains?

Generative AI and other automation tools are revolutionizing how companies approach infrastructure modernization. These technologies can help decipher legacy code, identify system dependencies, and even suggest improvements to optimize performance.

A practical example is using AI to manage mainframe operations, reducing human errors and accelerating processes. According to Kyndryl, automation can resolve up to 30% of IT issues, yielding significant savings in maintenance and downtime.

AI can further be used to analyze large volumes of data and identify patterns that would be impossible to detect manually. This can lead to valuable insights, such as market trends, customer behavior, or process optimization opportunities.

Modernizing with CodeNew

For companies dealing with legacy applications, a complete system rewrite is not always the best option. That is where CodeNew comes in, a solution that modernizes applications without the need to rewrite them from scratch. Utilizing cloud services and modular refactoring techniques, CodeNew optimizes performance, updates features, and reduces operational costs.

With CodeNew, companies can extend the lifespan of their applications, ensuring they continue to run efficiently and securely for many years. In addition, the customized approach allows for specific adjustments to meet the unique needs of each business.

With CodeBit you stay on top of the IT universe

Infrastructure modernization is a complex but essential process for companies that want to remain competitive in the digital age. With the right solutions and a strategic approach, it is possible to transform legacy systems into valuable assets, capable of driving innovation and generating significant business results.

At CodeBit, we are committed to helping your company navigate this challenge. We offer expertise in infrastructure modernization, data security, and the integration of emerging technologies like AI and Cloud Computing.

Get in touch with us to find out how we can help your business reach its digital transformation goals.