IoT Data Integration

Integrate, optimise and enhance the value of business data

Data integration unifies information from across different sources, such as databases, applications, and the cloud, and creates a consistent format for analysis and decision-making. In cybersecurity, it is essential to eliminate silos and inconsistencies and make information more accessible.

This process goes beyond data ingestion to include its transformation, analysis and visualisation, and also supports business intelligence (BI) tools. It is critical to identify threats and vulnerabilities in real time, strengthening security and responsiveness to threats.

What are the benefits of data integration?

Improve data quality

Through transformation and cleansing, data integration helps improve quality by identifying and correcting errors, discrepancies and duplicates. Accurate and reliable data provides a solid foundation for business decisions.

Driving innovation with data

Data integration helps uncover patterns, trends and opportunities that might otherwise remain invisible when data is separated across multiple systems. This paves the way for innovation, enabling the development of new products or services.

Reducing information silos

Data integration brings together information from multiple sources, eliminating information silos – isolated systems that store data without sharing it – and allowing organisations to reduce redundancy and inconsistencies for a complete and consistent view.

Fast access to information

With integrated data, access to analysis is faster, enabling timely decisions and quick responses to market changes, customer needs and new opportunities.

Optimising business intelligence

Data integration is at the heart of any business intelligence strategy. BI tools use integrated data to generate strategic analysis and visualisations that inform business decisions.

Increased operational efficiency

Data integration streamlines business processes by reducing manual input and limiting repetitive tasks. This results in fewer errors and greater consistency of information across the organisation.

Processes bring together data from multiple sources

Data identification and extraction - The first step is to identify all relevant data sources for the integration, which can include internal databases, spreadsheets, legacy systems, cloud services, external APIs and more. Next, the data is extracted using specific extraction tools, which can range from database queries to APIs.

Data mapping and transformation - It is essential to create a mapping schema that defines how data from different systems should be aligned and correlated. The data is then transformed into a common and consistent format through cleansing, enrichment and normalisation.

Data quality validation - A critical stage in verifying the accuracy, integrity and consistency of information. At this stage, errors, inconsistencies or format problems are detected and corrected using quality control processes. Only valid and error-free data can be considered useful.

Loading and synchronising data - Data is loaded into the target system, such as a data warehouse or other designated target. The data is then synchronised to ensure that the information is always up to date and ready for real-time use.

Data governance, security and analytics - Data integration requires governance practices to ensure regulatory compliance and to protect privacy and security. At the same time, data is made available for analysis through business intelligence tools to support business decisions and strategies.

Data integration tools

Data integration systems include many of the following tools:

Data replication tools: These tools are used to continuously replicate data from a source system to a target system and ensure that they are synchronised. They are particularly useful for real-time integration, disaster recovery and high availability.
Data virtualisation tools: These allow you to create an abstract layer that provides a consolidated view of data from multiple sources without physically moving the data. Users can access and query the integrated data as if it were centralised, without worrying about its geographical distribution.
Streaming data integration tools: These tools focus on processing and integrating real-time data from dynamic streams such as IoT devices, sensors, social media and other events. They enable organisations to process and analyse data as it is generated.
Data quality and governance tools: These tools ensure that integrated data from multiple sources complies with quality standards, industry regulations and corporate governance policies. They include advanced data profiling, cleansing and metadata management capabilities.
Change Data Capture (CDC) tools: Tools that specialise in capturing and replicating changes to data in source systems in real time. They are used to keep data warehouses up to date and to support real-time analysis activities, ensuring an always up-to-date view of the data.