By 2023, IT investment in Europe is projected to reach more than $1 trillion, according to a recent report by Gartner. This increase represents an increase of 5.5% compared to 2022. In particular, spending on data centers has experienced growth of 3.1%, exceeding $46 billion.

These data highlight the continued importance of information in business decisions and business expansion. In this context, the Spanish company PUE, specialized in consulting and implementation of Data and Machine Learning projects, has identified 5 keys that will be fundamental for data management in 2024. These keys seek to help companies optimize their data processes and make the most of its potential.
The implementation of machine learning

In the coming year, companies will continue to rapidly adopt the latest innovations in machine learning, especially focusing on large language models (LLM). These models use machine learning techniques to understand complex natural language patterns from vast amounts of data.

Although natural language processing (NLP) will continue to be predominant in many use cases, companies will step up the integration of LLM, such as OpenAI's GPT and Google's BERT. This will result in improvements in the quality of interaction with suppliers and customers, automation of natural language processing tasks, development of prototypes and applications in this field, as well as advances in information extraction and the generation of creative content.
Consolidation of the lakehouse as a storage route

More companies will adopt data lakehouse architectures in storage, merging the advantages of data lakes and data warehouses. They take advantage of the raw flexibility of data lakes and the structured analytical capabilities of data warehouses, providing a more complete and balanced approach to data analysis.

This evolution will improve data governance, offering efficiency, time reduction and process optimization. All this, supported by new advanced functionalities such as Apache Iceberg.
Enterprises will accelerate adoption of machine learning solutions and large language models
Creating data spaces to share information between entities

The practice of sharing information in data spaces between entities is gaining relevance in business collaborations. These virtual environments allow the consolidation of data sets from various sources under common rules, facilitating collaboration between companies. These spaces improve efficiency, transparency and collaboration in sectors such as retail, where they personalize the "customer journey" with specific offers based on data on interests and purchasing habits.

In the healthcare sector, the sharing of patient history data improves control and monitoring of the recovery process, as well as accelerates the research and development of drugs and treatments by allowing collaboration between medical institutions, pharmaceutical firms and laboratories.
Design of specific data products for data governance

Companies will also explore creating their own data products to improve information governance and address the specific needs of their businesses. These tools go beyond simple data storage, transforming it into useful information that optimizes processes.

The development of data products involves the creation of new platforms that facilitate the management, access, quality and analysis of data within the organization, thus generating value in strategic decision making. For example: interactive panels and dashboards to view data in a more intuitive way, forecasting solutions with production models to anticipate changes in the market, monitoring and alert systems to prevent stockouts, and virtual assistants that integrate customer service services. .
Data federation as a solution in hybrid and multicloud environments

In terms of integration, data federation is emerging as the leading trend to address the challenge of providing access to distributed data without physically consolidating it into a single central repository. This strategy maintains the autonomy of data at its source, being an effective solution for companies with complex processes in hybrid and multicloud environments.

Data federation maximizes its usefulness without the need to physically move it, resulting in cost reduction, resource optimization and acceleration of procedures.

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