The expectation for database systems has expanded. There’s an expectation that leading database platforms should provide a wide range of capabilities while addressing new use cases and workloads.  And then there are the legacy systems, built on older technologies and embedded in the fabric of the enterprise.

The evolving demand for new database functionalities conflicts with the time and resources required to update legacy systems. There are needs for new data types, including support for multiple data models, data visualization, data storage support, and extended capabilities functionalities.

The entire enterprise, especially marketing depend on accurate data. Companies are swimming in a sea of data. Most of it is soiled as in “dirty data”. Most of it is siloed and it’s impossible to make sense of it for actionable measures. The problems are further exacerbated by regulations such as GDPR which are mandating enterprise data governance.

New approaches require modern data architecture that facilitates the orchestration of a data flow pipeline. An underlying data model processes the data where it resides. Essentially the data pushes down and processes up and across various applications. Even with great strides in database and data management, it’s difficult for organisations to address these emerging trends, especially as the build-it-yourself and piecemeal commercial products have been bolted together. Dismantling legacy architecture while growing into the future straddled with new regulations are hurdles to overcome.