We know data!

At Expert Systems, we know that quality data is crucial for effective decision-making and achieving business objectives. Poor-quality data can lead to incorrect insights, flawed analyses, and misguided decisions. Therefore, data integration must prioritize ensuring the quality of the integrated data. This includes addressing issues such as data accuracy, consistency, completeness, and reliability.

The following are just some of the techniques we use:

• ETL (Extract, Transform, Load): ETL is a process of extracting data from multiple sources, transforming it to fit the target schema, and loading it into a destination database or data warehouse. This technique is widely used for batch processing of data.
• ELT (Extract, Load, Transform): ELT is similar to ETL, but the transformation step occurs after the data is loaded into the target system. This approach is often used in data warehouses where data is first loaded and then transformed using the processing power of the target system.
• Data Virtualization: Data virtualization allows users to access and manipulate data from multiple sources without physically moving or replicating it. This technique provides a virtual layer that integrates data in real-time, offering flexibility and agility in accessing and analyzing data.
• API-based Integration: Application Programming Interfaces (APIs) enable systems to communicate and exchange data with each other. API-based integration involves using APIs to connect different systems and exchange data in a controlled and secure manner.
• Change Data Capture (CDC): CDC is a technique used to identify and capture changes made to data in source systems. It enables real-time or near-real-time replication of data changes to the target system, ensuring that the integrated data is up-to-date.
• Data Replication: Data replication involves copying data from source systems to target systems, usually in real-time or near-real-time. This technique ensures that the target system has a consistent copy of the data from the source systems.
• Master Data Management (MDM): MDM is a process of creating and managing a single, consistent view of master data entities such as customers, products, and employees across an organization. It involves techniques for data consolidation, cleansing, and governance to ensure data quality and consistency.