Over last decade, data engineering and analytics have dramatically transformed, allowing organizations the ability to process massive amount of data volumes and generate deep insights.  Many healthcare organizations have adopted these innovations in order to achieve their operational goals of trustable data, insights, and visualization.  While these organizations have achieved some success in becoming more data- and insight-driven, the speed and cost of these efforts have not been sustainable.

To solve this, data analytics leaders have been devising strategies to speed up the journey to analytics and bring in a more cohesive approach for businesses to generate their own insights.  The best strategy they’ve found is when data and analytics platforms are implemented using augmented low-code solutions.

Compared to the traditional approach of ETL or script-based solutions, a metadata-driven, UI-based configurable platform for data acquisition, integration and distribution can drastically accelerate time to analytics.  The UI-based configurable solution also brings agility and the ability to adapt to changing source formats, adding new sources, and rapidly integrate new data assets.

Many organizations have tried implementing metadata-driven solutions, however those efforts haven’t been comprehensive enough to achieve end-to-end solutions.  The ideal solution design has to be fit for purpose, reduce the total cost of ownership, increase user adoption and improve its ease of use.