Modernization of data: path to smart decision-making

You have probably heard predictions that without data modernization, companies will not thrive in an increasingly competitive and digital world. While this may not be stated as explicitly, this idea is widely disseminated among technology companies.

However, you have certainly heard the famous phrase “data is the new oil,” popularized by Clive Humby, a pioneer in data science in 2006. The metaphor highlighted that, much like oil, data is a valuable resource but needs to be refined (analyzed) to have value. Clive was right, and this idea has been widely adopted by technology and business leaders,

But now, simply collecting data is not enough, as technologies like big data, artificial intelligence, and the Internet of Things have begun to emerge and transform entire sectors. Increasing investment in data modernization is necessary to gain advantages from these technologies; a survey released by LumenData shows that 77% of companies experienced revenue growth after investing in data modernization.

Continue reading to understand how data modernization is transforming the way companies make smart decisions and optimize their processes, as well as the implementation costs and best strategies.

What is data modernization and what does it mean?

In simple terms, data modernization refers to the process of transforming traditional data infrastructures and architectures into more modern, scalable, and flexible platforms and solutions. This process involves updating legacy systems, which are often limited in capacity and speed, to newer technologies, such as cloud-based solutions, real-time data platforms, and advanced analytics tools.

Data modernization is not just about migrating from a physical server to the cloud; it encompasses a complete review of how data is generated, stored, processed, and consumed by the company. It is a concept that is gaining increasing relevance; data released by LumenData shows that companies adopting data modernization solutions have seen productivity increases of up to 50% in their data teams, thanks to the automation of data management processes and the use of advanced analytics tools.

What are the main data modernization strategies?

Data modernization strategies are simply plans and practices adopted by companies to update and transform their data management infrastructures and processes, allowing them to keep up with modern technological demands and advancements. Resources like these aim to optimize how data is collected, stored, processed, and analyzed, resulting in greater efficiency, flexibility, scalability, and security.

Before adopting a data modernization strategy, it is essential to understand the business objective. Companies wanting to become more data-driven may prioritize strategies that enhance real-time data analysis, as seen with Netflix, which uses large volumes of data to monitor user behavior and offer personalized recommendations instantly. Others, like General Electric (GE), may focus more on reducing operational costs through automation and migration to the cloud. In GE’s case, migrating to the cloud allowed it to reduce maintenance costs for its industrial machines and equipment.

Here are the main data modernization strategies that companies are adopting:

Migration to the cloud

Cloud migration is one of the central strategies in data modernization. Cloud platforms offer scalability, flexibility, and cost reduction. Companies that migrate to cloud-based solutions can quickly expand their data storage and processing capabilities as needed. According to Flexera, 94% of companies are already using the cloud at some level, many adopting a hybrid approach (public and private cloud) to combine security and scalability.

Implementation of modern data lakes and data warehouses

Another important strategy is the creation of data lakes and data warehouses; such solutions allow for the efficient storage of large volumes of structured and unstructured data, facilitating access and real-time data analysis.

Data integration & automation

Automating the data integration process is essential to ensure data consistency and accuracy across the organization. Automated integration tools enable the combination of data from various sources (internal and external) in real time, which is the foundation for accurate analysis.

Advanced analytics with AI and machine learning

The use of advanced analytics, including artificial intelligence (AI) and machine learning (ML), transforms data into valuable insights. These technologies allow companies to predict trends, automate processes, and provide personalized experiences to customers.

Self-Service Analytics

The concept of self-service analytics allows end users within the organization, without advanced technical knowledge, to access and utilize data intuitively. This promotes the democratization of data and facilitates decentralized decision-making.

How can data modernization impact operational efficiency and decision-making in companies?

Data modernization can drastically impact operational efficiency and decision-making within companies, and even the survival of a business. One positive example comes from the healthcare sector, the Mount Sinai Health System, one of the largest hospital networks in the United States, which invested in data modernization and managed to improve patient data analysis leading to more accurate diagnoses and treatments; it was even cited as a success case by the New York Times. In contrast, Hahnemann Hospital in Philadelphia closed in 2019 with a history of financial difficulties due to high operational costs and an inability to modernize its systems and compete efficiently.

See in more detail how your business can benefit from data modernization:

Increase scalability

Cloud-based solutions offer scalability that allows companies to manage increasing volumes of data without compromising performance. This ensures that operations can grow with the business without the need for constant hardware upgrades.

Insight-based decisions

With clean, accurate, and accessible data, companies can generate more reliable reports and extract detailed insights that directly impact business strategy. Bank of America, for example, by adopting data modernization strategies was able to identify behavior patterns and started personalizing credit offers and services, increasing customer satisfaction and retention.

Data integration and holistic view

Modernization also facilitates the integration of different data sources, providing a holistic view of the entire organization; this factor is especially useful for large corporations operating in multiple departments and markets, allowing them to have a unified view of their operations and customers.

What are the best practices for implementing data modernization?

Data modernization is not just about technology; it is a cultural transformation that involves the entire team. The first step is to create a data-driven culture, as without this, in times of crisis or tight deadlines, data may be overlooked in favor of quick and unsubstantiated decisions, which can result in high costs in the future.

In addition, there are three other good practices that help in the assertive implementation of data modernization; check them out:

Define a progressive strategy

Create a clear strategy and start small; do not try to modernize all the data at once, focus on smaller, scalable projects to test solutions. Remember to integrate data modernization with business objectives, ensuring that the technology genuinely adds value and optimizes operations.

Automate data processes

By doing so, you improve efficiency, reduce manual errors, and speed up the processing of large volumes of information; furthermore, automation allows employees to focus on higher value activities, such as strategic analysis and innovation, rather than repetitive and time-consuming tasks.

Focus on governance and security

Implementing robust security policies and data governance is essential to ensure regulatory compliance, protect sensitive information, and increase stakeholder trust.

What are the costs associated with data modernization and how to justify the investment?

It is difficult to estimate the exact cost of adopting data modernization, as it varies depending on the complexity of the system, the size of the company, and the solutions implemented. However, investments have grown substantially, particularly in cloud infrastructure. In 2023, Gartner projected that global spending on public cloud services would reach $597 billion, a 21.7% increase from the previous year. Within this figure, approximately $41 billion would be allocated to management and security services.

Main costs in data modernization

• Cloud migration: transferring data and systems to cloud platforms involves costs with cloud services (such as AWS, Google Cloud, or Azure) and adjustments to the existing infrastructure. It is common for companies migrating to the cloud to see a significant reduction in hardware costs, but the initial implementation cost can be high.
• Hardware and software upgrades: replacing legacy systems with modern technologies may require substantial investments in more efficient hardware and software.
• Automation tools: using tools to automate data integration and cleaning, as well as analysis, may involve licensing costs and software development.

• Regulatory compliance: companies operating in regulated sectors, such as healthcare and finance, must ensure compliance with laws like GDPR and LGPD, which may incur additional costs for policy implementation and audits.
• Interruptions: during the migration process or implementation of new technologies, there may be interruptions in operations, causing a temporary loss of productivity or revenue until the new system is fully operational.

Although initial costs can be high, the investment in data modernization can be justified with tangible data demonstrating how these initial costs will be offset by long-term benefits. We have outlined a step-by-step process with the data you will need to gather to build a solid justification:

Initial cost vs. expected return

Detail the anticipated costs for implementation, such as software licensing, cloud migration, infrastructure, and outsourced services. For example: if your company is migrating to the cloud, what is the monthly/annual cost of infrastructure (AWS, Azure, Google Cloud) compared to the current cost of maintaining on-premises servers?

To measure the expected return, use data from companies that have gone through this process and show how eliminating redundancies and inefficient systems can generate savings and competitiveness.

Impact on productivity

Gather data on the time currently spent on manual tasks that could be automated, such as data integration, data cleaning, or repetitive processes. For example: how many hours per week do teams spend on manual processes that could be automated? Also, gather the number of errors caused by manual processes and show how automation can reduce these errors, resulting in less rework and higher data quality.

Improvement in decision-making

Analyze the current time it takes to generate reports or access relevant data and show how modernization can streamline these processes. For example, with real-time data, decisions that previously took days can now be made in minutes, resulting in faster responses to market changes.

Risk of Not Investing

Present the risk of not investing in data modernization; companies that do not modernize run the risk of becoming obsolete, losing ground to more agile and efficient competitors, and provide examples of similar segment companies that have ceased operations to illustrate this risk. Finally, demonstrate how modernization prepares the company to seize new market opportunities, with the potential to innovate using AI, big data, and other technologies.

How does data modernization relate to artificial intelligence and the Internet of Things (IoT)?

Data modernization enhances the use of Artificial Intelligence (AI) and Internet of Things (IoT), as it provides the necessary infrastructure to store, process, and analyze large volumes of real-time data generated by IoT devices, which are essential for generating actionable insights via AI.

AI, in turn, uses this modernized data to learn and make more accurate predictions, optimizing processes such as predictive maintenance, industrial automation, and service personalization in sectors such as healthcare, manufacturing, and logistics.

This integration allows for:

• Real-time analysis: By modernizing their data systems, companies can process and analyze large volumes of data from IoT sensors in real time, which is essential for rapid responses.
• Process optimization: AI applied to this data provides more precise insights, improving automation and operational efficiency.

Companies have moved beyond questioning the value of data; data modernization is now a crucial step to leverage advanced technologies, such as AI and machine learning, gain competitive advantage, and ensure resilience in times of uncertainty.

According to a study by the Digital Banking Report in 2022, many financial institutions are choosing to work with partners, as data modernization is a complex initiative that requires specific skills that may not always be available internally. By partnering with specialists, companies can accelerate implementation and ensure faster, more efficient results, as well as reduce long-term maintenance costs.

Not to mention that companies that work with partners are better equipped to handle challenges related to data migration and integration, while ensuring regulatory compliance and information security, which are critical issues, especially in certain businesses. Talk to one of our specialists and find out how we can help you.