There’s been a major paradigm shift in the field of data and information management now that massive amounts of data are collected in different formats and managed from various system/infrastructure neighborhoods.
Data is exchanged with numerous components of the application landscape, including microservices, databases, cloud file stores, and big data components. In the last two decades, mostly-structured data was collected in OLTP systems then moved to ODS and OLAP systems such as data warehouses and data marts using integration techniques and ETL processes. The old data integration processes are now archaic and unsuitable in this new data landscape.
Today, massive amounts of data—in structured, semistructured or unstructured formats—are produced by various business systems using different technologies. Now is the time to think differently and move to a modern data integration approach that incorporates more advanced ways of collecting, processing, and analyzing data. Businesses need to analyze all these data sets to discover useful information and support decision making in new ways, including AI/ML techniques such as text analytics and computer vision. Modern data integration also incorporates sound architecture principles of reusability, distributed processing, excellent cataloging, and metadata collection along with improving speed to market. This new approach allows us to reimagine the housing market by providing improved insights that lead to smarter actions.