Solving pharmaceutical data research & regulation problems with analytics
4 mins read

Solving pharmaceutical data research & regulation problems with analytics

Nearly every organization today is data-driven, and the pharmaceutical industry is no exception — however, traditional data technologies and methods have failed the industry, leading to poor efficiency and optimization, often leading to high costs.

Researchers hamstrung by data fragmentation

Let’s take a look at pharmaceutical research, for example. One of the primary objectives for researchers is determining what treatments might work, and which don’t. Today, much of the research data they collect is highly fragmented and stored across multiple databases, often not readily available. As a result, researchers are forced to constantly retest drugs that have failed in previous research, and they constantly face the danger of following a line of research down blind alleys — that previous researchers have already found to be ineffective.

Data lake + AI for interactive, predictive research

Modern big data technologies present a new opportunity for pharmaceutical researchers: bringing all relevant research data, past and current, together into one “data lake”. This includes not just the alpha-numeric structured data in the relational databases, but also everything that can be digitized — from freeform reports to videos, still images, and audio recording.

Once all of this data is available in the data lake, it can be analyzed by the latest generation of AI and machine learning tools. And unlike the old relational database tools, this analysis is not confined to a few pre-determined questions. With natural language interfaces, they can allow researchers to ask any question and get an answer backed up by data within minutes. It is designed for interactive research, where each new result leads to new questions that may go in unexpected directions — often arriving at surprising new conclusions. With this set of tools, the initial stage in new drug research moves out of the lab entirely and becomes an exercise in data analysis that takes a fraction of the time. With predictive analysis, the researcher can refine his focus to the few treatments that offer the most promise, and use his time most effectively on those.

Pharmaceutical regulations also hindered by traditional data approaches

The other place where big data can streamline the research process is at the end when the new treatment is presented to the regulators. Traditionally, this has involved a huge amount of manual effort, pulling together information from numerous studies sequestered in multiple databases with different data architectures along with adding legal searches to it. In the future, all of that data will be available in the data lake, making the creation of the final report a comparatively easy matter of simply running a standard analysis to generate the report in a predetermined format.

A common data platform for all Pharmaceutical data

Pharma is already experimenting with big data technologies like Hadoop. The problem is that these technologies do not provide the data management capabilities needed to support sophisticated data research. For instance, they do not keep track of when the data was created, who accessed them, who changed them and in what ways. The result is that Hadoop data lakes too often turn into data swamps, where the important data is lost in the morass.

Fortunately, the Solix Common Data Platform (CDP) brings order to the chaos. It includes the best suite of data management tools on the market — featuring data lake, enterprise archiving, and application retirement capabilities. Solix Data-driven Healthcare is a vertical-specific solution built on the Solix CDP, designed for Pharmaceutical data-driven organizations, in addition to healthcare payers, providers, and medical equipment suppliers.

Data-driven Healthcare

The Solix CDP also features automated data tiering, moving inactive data to cheaper storage (such as AWS) to save money, automatic deletion of data that has outlived its relevance, and legal hold on data that might be important in legal action. Once all the data is in one place and properly managed, everything becomes much easier, faster, and less costly.

For a more detailed discussion of how the Solix CDP running on Hadoop can streamline research and business operations for pharma companies, please click here to download the whitepaper on Data-driven Healthcare for Pharmaceuticals.