Ad Hoc Analysis on Large Pharma Datasets

MSTRATEGY SYSTEMS

Searching for new technological tools and insights to address complex challenges in the pharmaceutical industry:

Dashboards and advanced data visualizations are essential tools for management and our team at mStrategy may tailor customized solutions for your business need.

But what about those urgent requests that require in-depth analysis and experts’ viewpoints? That’s where ad hoc reporting can come in handy.

Ad Hoc reporting lets you create reports instantly and are made to answer specific questions. Usually, these reports can be created without any technical knowledge so it’s easier for non-technical teams to get insights fast.

One complex challenge that is common in the pharmaceutical industry lies in performing ad hoc analysis on prescription and demand data sources, which often contain hundreds of millions of records. The industry standard for ad hoc analysis has long been Pivot Tables, a feature inherited from MS Excel, the most widely used BI tool. However, Excel is not capable of handling such large datasets, requiring alternative solutions.

Due to high costs and performance limitations, it is also advisable to move away from traditional transactional databases (OLTP), which are not optimized for analytical queries, as well as multidimensional analysis tools like Microsoft Analysis Services. These solutions often come with expensive per-user licensing fees or require high-end hardware to maintain performance at scale.

Today, columnar databases have become the de facto standard for analytics workloads. Originally conceptualized in the 1970s as part of academic research, they are now at the forefront of big data analytics.

This shift has led to the emergence of successful open-source solutions that offer impressive performance using columnar database technology. Some key players in this space include ClickHouse, DuckDB, Weaviate, and FrostDB.

One of our focus at mStrategy is on developing high-performance visualization platforms tailored for large-scale pharmaceutical data analysis. The solution is designed to efficiently process flat files with hundreds of millions of records, supporting both ad hoc analysis and advanced data visualizations for dashboards and KPIs.

Ideally, this implementation will center around a custom-built data visualization platform for market and competitive analysis as well as advanced analytics.