In 2025, 54% of organisations expanded their data teams, while only 5% decided to reduce them. The positive trend is expected to continue this year, with nearly six in ten managers, or 57%, planning to hire more specialists in this area. Research shows that companies are striving to become “data-driven”. This means that, when making business decisions, they rely not on intuition, but primarily on hard data.
For now, specialists responsible for processing data do not need to fear for their jobs, even considering the development of artificial intelligence-based analytical tools and the broader difficulties in the IT labour market. However, despite expanding their data teams, organisations still struggle to make full use of their potential. It turns out that specialists have too little time to analyse information. Why?
A long road to using data in business
Over the past decade, the global amount of information used and processed has increased tenfold. The main reason for this pace is the development of digital technologies and widespread internet access. Organisations are keen to take advantage of the ongoing revolution by constantly collecting their own data and additionally using external datasets. Before such data can be used in business analysis, however, it must be organised and structured. Companies also need to provide and maintain appropriate technological infrastructure, including databases, processing systems and servers.
According to research, these are the activities on which data teams focus most of their attention. As many as 57% of specialists admit that most of their working time is spent organising datasets. Another 22% point to managing platforms or infrastructure. Creating reports and dashboards, which form the basis for business decisions, is the main task of only 13% of experts. As Maciej Wawrzyniak, Director of the Databases and Data Engineering Area at Linux Polska, explains, this problem can partly be solved at the stage of designing data architecture.
“Organisations feed their systems with data from many distributed sources. These may include, for example, databases managed using SQL, text files saved in CSV format or REST APIs used to exchange information between applications. In such a situation, the process of extracting and loading data into a database should be fully automated and optimised. If a company plans to analyse large datasets collected in a data warehouse, they must be further organised and transformed. This can be done, for example, using the open-source tool dbt, or Data Build Tool. It is responsible for the key ‘T’, meaning Transform, in modern ETL/ELT processes. Its work begins after raw data has already been loaded and consists of preparing it for analysis in an automated and structured way, using the simplicity of SQL and the computing power of the engine being used. This makes it possible to quickly search information, present consistent and valuable results, and easily scale when adding further data sources, as we saw during a project for one insurance company. All these elements improve and accelerate the work of teams,” said Maciej Wawrzyniak, Director of the Databases and Data Engineering Area at Linux Polska.
Barriers to a strategic approach to data
It is somewhat paradoxical that, despite an increase in the employment of data specialists, a strategy related to this area was applied in 2025 by only one in four companies. Surprisingly, the percentage was higher in the previous year, at 31%. Among the biggest barriers to developing and implementing a strategy, respondents primarily mention organisational culture, indicated by 52%, and a lack of support and engagement from the organisation, cited by 46%. According to Tomasz Dziedzic, CTO at Linux Polska, these are most likely the factors making it more difficult to organise the work of data teams.
“A strategy should define the organisation’s business goals and the means necessary to achieve them. The key is to select solutions that enable the creation of a secure, reliable, efficient database ecosystem tailored to the company’s needs. If such an ecosystem is actually built, teams can focus on the most important projects related to valuable data analysis instead of constantly struggling with failures, malfunctioning search mechanisms or problems with scaling and organising databases. In practice, a strategic approach to implementing databases or data warehouses translates into increased operational efficiency, improved information management and better use of data in the business decision-making process. Achieving these goals, however, requires a change in organisational culture, which, according to current research, remains the main barrier,” said Tomasz Dziedzic, CTO at Linux Polska.
Could open source be an opportunity for data teams?
Less frequently mentioned, though still important, obstacles to implementing a data strategy include high implementation costs, cited by 36%, and competency gaps within teams, indicated by 35%. Robert Halaczek, Solutions Architect at Linux Polska, believes that in such a situation companies should focus on open-source solutions capable of meeting organisational requirements in terms of security and performance.
“The most popular open-source databases include PostgreSQL, MySQL and MongoDB. Behind each of them is a project community that can support teams when problems arise and show them best practices in data management, which creates an opportunity to close competency gaps within teams. Open-source solutions involve lower implementation, operating and licensing costs, so they also help reduce the financial barrier mentioned in the research. It is also worth noting that these are flexible technologies, which means that a company can modify and develop the system according to its business needs. At the same time, the problem of an organisation becoming dependent on commercial solution providers is solved,” said Robert Halaczek, Solutions Architect at Linux Polska.
Companies are investing money in expanding their data teams, but they still struggle to implement strategies that would make their work easier. The result is that time which specialists could spend on analytics is instead consumed by organising and maintaining datasets. To reduce the impact of this situation, a change in organisational culture and technological transformation are necessary. Only then will companies be able to turn the data they collect into real financial and operational benefits.
