Across all sectors, data integration is becoming increasingly important to managing businesses and staying afloat in a competitive market. However, it’s not always clear what data integration entails, especially for those not well-versed in IT. Even for IT Specialists, the practice in its modern form is relatively new and still being developed. Nonetheless, a basic familiarity with how data integration strategies work can be beneficial to management.
Databases: Use and Construction
In a way, the fundamental unit of data integration is a database—an organized collection of data for storage and easy access. Databases can be organized in many ways—a common schema uses tables that relate records (rows) and attributes (columns), and such databases are referred to as relational databases. The exact architecture of a database is dependent on its purpose, which determines the vital properties of that database. One major application of databases for businesses and organizations is online transaction processing (OTLP) for purchases, shipments and more; this demands high query and write speed, support for large numbers of concurrent users without conflicts and atomicity of transactions such that no incomplete data is written to the database.
Integration: Organizing the Organizers
Data integration takes place at a higher level above databases, utilizing a system to gather data from multiple sources to present as a coherent whole for better comprehension. Various tools and techniques exist, each having their own advantages suited to specific applications. Data virtualization involves remotely accessing multiple data sources in real time, connecting them to one access point without additional storage. A subset of this is data federation, which creates a virtual database with a specific model by which to interpret remote data sources. Conversely, data warehousing actually consolidates all relevant data into a single extant database, extracting and then transforming data into a single format before loading it into storage. Virtualization and federation are most effective for real-time, short-term viewing, while a data warehouse provides a format well-suited to large-scale analysis, especially over long time periods.