Specialised Big Data processing includes technologies, tools, and processing methods designed to analyse data from highly specialised fields, such as geoinformatics (GIS), biotechnology, medicine, finance, or the energy sector. It requires the use of specific algorithms and computing architectures that are adjusted to process data with unique structures and precise analytical requirements.
Specialized Big Data Processing (GIS, Medical, etc.)
Type of technology
Description of the technology
Basic elements
- GIS (geographic information system): Spatial data processing and analysis platforms.
- Medical databases: Health data storage and analysis tools.
- Statistical algorithms: Techniques for analysing biomedical and epidemiological data.
- SCADA systems: Tools for monitoring and managing critical infrastructure, such as energy.
- Financial databases: Storage and analysis of trading and exchange data.
Industry usage
- GIS analysis: Mapping spatial data in an urban environment.
- Medical data processing: Analysis of research results and epidemiological data.
- Energy sector: Monitoring and management of power grids.
- Financial analysis: Processing of trading and exchange data.
- Critical infrastructure: SCADA data monitoring and management.
Importance for the economy
Processing large sets of specialised data enables companies and institutions to obtain precise information and forecasts that support decision-making processes. In sectors such as healthcare or energy, proper data management contributes to improved operational efficiency, better resource allocation, and the introduction of innovative services.
Related technologies
Mechanism of action
- Processing specialised data sets requires dedicated algorithms and specialised software that is able to see the character of the data being processed. In GIS, geolocation data is analysed in terms of its location. In medicine, data processing requires consideration of the sensitivity of information. In the financial sector, real-time data analysis is important.
Advantages
- Accuracy: Precision data analysis in specialised fields.
- Speed: Efficient processing of complex data sets.
- Cybersecurity: Securing sensitive data from unauthorised access.
- Scalability: Ability to expand infrastructure as needed.
- Compliance: Meeting regulatory requirements and industry standards.
Disadvantages
- High costs: Significant financial investment in infrastructure and tools.
- Security risks: Possibility of leakage of sensitive data.
- Lack of standardisation: Interoperability problems between different systems.
- Management complexity: Difficulties in managing specialised data sets.
- Data quality issues: Incorrect or incomplete data can lead to erroneous conclusions.
Implementation of the technology
Required resources
- GIS: Spatial data analysis and visualisation tools.
- Medical databases: Secure systems for storing health data.
- SCADA software: Systems for monitoring critical infrastructure.
- Network infrastructure: Links for transferring large amounts of specialised data.
- Field experts: Specialists in data analysis in sectors such as medicine and energy.
Required competences
- Data engineering: Design and management of specialised databases.
- Data analytics: Processing and interpretation of analytical results.
- GIS data management: Knowledge of spatial analysis tools and techniques.
- Programming: Knowledge of languages for specialised data analysis, such as Python and R.
- Cybersecurity: Protecting sensitive specialised data from threats.
Environmental aspects
- Energy consumption: High energy consumption of extensive processing systems.
- Waste generated: Problems with recycling decommissioned equipment.
- Emissions of pollutants: Indirect emissions from the processing of large volumes of data.
- Raw material consumption: High demand for specialised components.
- Recycling: Difficulties in recovering materials from advanced equipment.
Legal conditions
- Data protection: Health and financial data protection regulations.
- Industry standards: Data processing requirements in sectors such as energy and medicine.
- Data processing regulations: Controlling access to sensitive data.
- Intellectual property: Patents for technologies for analysing specialised data sets.
- Occupational safety: Regulations for safe operation of monitoring systems.