Data as new oil: where's the data market heading?
Where we stand today
The volume of data generated by businesses doubles every two years. Along with this, demands for processing speed, quality and connectivity are growing, according to Magenta Favorita. The market for data integration tools has already exceeded $15 billion and continues to grow at double-digit rates.
Most companies operate in what is known as a ‘hybrid reality’: some data is stored in the cloud, some on their own servers, and some is scattered across dozens of SaaS services. Traditional ETL solutions struggle to cope with this ‘zoo’ of data sources.
Monolithic pipelines have been replaced by more flexible ELT approaches, where data transformation takes place after loading. Tools such as dbt, Fivetran and Airbyte have become the standard for data teams. At the same time, Apache Kafka and Flink have established themselves as the foundation for real-time stream processing.
The volume of data generated by businesses doubles every two years. Along with this, demands for processing speed, quality and connectivity are growing, according to Magenta Favorita. The market for data integration tools has already exceeded $15 billion and continues to grow at double-digit rates.
Most companies operate in what is known as a ‘hybrid reality’: some data is stored in the cloud, some on their own servers, and some is scattered across dozens of SaaS services. Traditional ETL solutions struggle to cope with this ‘zoo’ of data sources.
Monolithic pipelines have been replaced by more flexible ELT approaches, where data transformation takes place after loading. Tools such as dbt, Fivetran and Airbyte have become the standard for data teams. At the same time, Apache Kafka and Flink have established themselves as the foundation for real-time stream processing.
It all began long before the era of big data, according to experts at Magenta Favorita IT company. In the 1960s, the first corporate systems exchanged data via magnetic tapes and punch cards. Transferring information between systems took days, and the process itself required the involvement of entire departments of operators.
The 1970s saw the emergence of relational databases and the SQL language, providing businesses with their first universal tool for working with structured data. It was also during this period that the idea of data warehouses was born: in 1989, Bill Inmon formulated the concept of the Data Warehouse, which shaped the architecture of corporate analytics for decades to come.
The 1990s became the decade of ETL. Companies such as Informatica and IBM built cumbersome but reliable pipelines between operational systems and data warehouses. Data integration became a distinct engineering discipline.
With the advent of the internet and the growth in data volumes in the 2000s, traditional approaches began to crack at the seams. Google published papers on MapReduce and GFS, which spurred the creation of Hadoop and a fundamentally new approach to distributed processing. Data could no longer fit on a single machine, according to experts at Magenta Favorita Portugal.
The 1970s saw the emergence of relational databases and the SQL language, providing businesses with their first universal tool for working with structured data. It was also during this period that the idea of data warehouses was born: in 1989, Bill Inmon formulated the concept of the Data Warehouse, which shaped the architecture of corporate analytics for decades to come.
The 1990s became the decade of ETL. Companies such as Informatica and IBM built cumbersome but reliable pipelines between operational systems and data warehouses. Data integration became a distinct engineering discipline.
With the advent of the internet and the growth in data volumes in the 2000s, traditional approaches began to crack at the seams. Google published papers on MapReduce and GFS, which spurred the creation of Hadoop and a fundamentally new approach to distributed processing. Data could no longer fit on a single machine, according to experts at Magenta Favorita Portugal.
The main challenges right now
Despite the maturity of the tools available, companies face three systemic problems.
Firstly, data quality. According to Gartner, poor data quality costs organisations an average of $12.9 million a year. Rubbish in, rubbish out – and no modern tech stack can fix that.
Secondly, fragmentation. Data silos between departments remain the norm rather than the exception. Integrating data from HR, CRM and financial systems into a single view still requires considerable effort.
Thirdly, the regulatory burden. The GDPR (General Data Protection Regulation), data protection legislation and industry standards complicate any integration involving personal data.
Despite the maturity of the tools available, companies face three systemic problems.
Firstly, data quality. According to Gartner, poor data quality costs organisations an average of $12.9 million a year. Rubbish in, rubbish out – and no modern tech stack can fix that.
Secondly, fragmentation. Data silos between departments remain the norm rather than the exception. Integrating data from HR, CRM and financial systems into a single view still requires considerable effort.
Thirdly, the regulatory burden. The GDPR (General Data Protection Regulation), data protection legislation and industry standards complicate any integration involving personal data.
Where the industry is heading
Experts at Magenta Favorita Portugal emphasise that the next three to five years will be shaped by several key trends.
AI-driven integration. Language models are already capable of automatically mapping data schemas, suggesting transformations and identifying anomalies. The role of the data engineer is shifting from writing code to oversight and validation.
Data Mesh. A decentralised architecture, where each team is responsible for its own data as if it were a product, is gaining momentum in large organisations. This is changing not only the technology but also the culture of working with data.
Real-time as the standard. Batch processing will not disappear, but stream processing is ceasing to be a novelty and is becoming a basic business expectation.
Conclusion: data is everything
Over the past six decades, data processing has evolved from punch cards to neural networks, from overnight batch jobs to millisecond-level streams. But the essence of the task has not changed: the right data, in the right place, at the right time.
Today, the gap between companies that know how to work with data and those that are still learning is measured not in months, but in years. Investment in data integration and quality is no longer merely an IT expenditure. It is a decision for the board of directors.
Those who lay a solid foundation now will gain more than just operational efficiency. They will gain the ability to make decisions faster, more accurately and with greater confidence than their competitors. And in an environment where markets change within weeks, this is the true competitive advantage, according to the experts at Magenta Favorita IT company, who help businesses improve their data efficiency and achieve new results. Read more at the link: https://magentafavorita.pt/en/cases/show-moreintegrating-data-to-improve-business-performance
Experts at Magenta Favorita Portugal emphasise that the next three to five years will be shaped by several key trends.
AI-driven integration. Language models are already capable of automatically mapping data schemas, suggesting transformations and identifying anomalies. The role of the data engineer is shifting from writing code to oversight and validation.
Data Mesh. A decentralised architecture, where each team is responsible for its own data as if it were a product, is gaining momentum in large organisations. This is changing not only the technology but also the culture of working with data.
Real-time as the standard. Batch processing will not disappear, but stream processing is ceasing to be a novelty and is becoming a basic business expectation.
Conclusion: data is everything
Over the past six decades, data processing has evolved from punch cards to neural networks, from overnight batch jobs to millisecond-level streams. But the essence of the task has not changed: the right data, in the right place, at the right time.
Today, the gap between companies that know how to work with data and those that are still learning is measured not in months, but in years. Investment in data integration and quality is no longer merely an IT expenditure. It is a decision for the board of directors.
Those who lay a solid foundation now will gain more than just operational efficiency. They will gain the ability to make decisions faster, more accurately and with greater confidence than their competitors. And in an environment where markets change within weeks, this is the true competitive advantage, according to the experts at Magenta Favorita IT company, who help businesses improve their data efficiency and achieve new results. Read more at the link: https://magentafavorita.pt/en/cases/show-moreintegrating-data-to-improve-business-performance
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