Analyzing Pharmaceutical Data at Rapid Speeds

Gaining Critical Insights and Competitive Advantage Through Fast Analytics

Julie SkeenAugust 14, 2019

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The pharmaceutical industry is a vital part of the healthcare system. Not only are pharmaceutical companies on the cutting edge of research and development when it comes to diseases and drugs, they are also responsible for distributing medications and establishing processes to ensure the right patients receive the right medications when needed. And increasingly, much of their work is fueled by data and analytics.

Pharmaceutical companies are replete with data today. They have massive amounts of patient and clinical information at their fingertips, providing fertile ground for meaningful data analysis. They’re using medical records, insurance claims and myriad other data sources to explore how diseases develop, to uncover patterns and risk factors based on demographics and genetics, and reveal potential new uses for existing drugs.

On a more routine basis, organizations ensure that the proper information is disseminated to pharmacies and healthcare providers to assure that patients receive the most effective medicine, at the right time, in the right dosage.

From an operational perspective, pharmaceutical executives now realize they can generate additional insights from their data to improve critical areas of pharmacy practice, including managing health care plan spending, monitoring patient use of prescription medications and lowering R&D costs. Still, none of this is possible if the pharmaceutical company is grappling with inefficient processes and tools to prepare data for pharmacy employees to analyze.

The Data Prep Predicament

Data preparation is the process of gathering, combining, cleansing, structuring and organizing data so it can be analyzed as part of data visualization, analytics and machine learning applications. Traditionally, the IT department leveraged extract, transform and load (ETL) tools that required coding expertise and other technical skills. Non-technical staff lacked the specialized skills required to leverage these tools, and relied on IT to field all data requests, prepare data and generate analysis and reporting.

But with ever-increasing quantities of pharmaceutical data, and the need for analytics growing at an exponential pace, IT departments simply can’t manage massive volumes of data requests. Backlogs, combined with inflexible data prep and analytics tools, can leave data consumers waiting weeks for results—frustrating both IT and business. And when delays can have a real-world impact on the health of patients, no one can afford to wait.

Agile data prep is critical for pharmaceutical analysis because it not only solves the biggest issues with legacy ETL tools, it also enables a broader range of data users to engage in data analysis via a self-service model. Enacting agile data prep requires a data intelligence platform that delivers speed with flexible data handling, essentially eliminating data modeling time.

The solution must include a self-service interface that allows users to address new questions as they arise and eliminate the need for IT involvement. To speed up the process, it should also provide a library of interfaces and adapters to give users immediate access to data from virtually any source. A single, visual workflow interface enables rapid improvements to data quality and business logic. These features empower users to easily manipulate data, blend data from different sources and analyze data to yield highly accurate results. In the pharmaceutical industry, self-service data prep is critical to rapid data analysis.

Pharmaceutical Data in Action

 A large pharmaceutical company used ETL tools to deliver data to thousands of pharmacists and other staff. However, because of the technical nature of the tool, users had to submit all data requests to IT. Each request required scarce and highly-skilled technical resources to respond. When the results were provided six weeks later, they were outdated and irrelevant.

By implementing a data intelligence platform, the company cut down data request response time from six weeks to six hours. This reduced the burden on IT resources, freed up highly skilled IT experts to work on other projects, reducing costs and increasing efficiencies.

 Are you a pharma company searching for additional information about analyzing data at rapid speeds? Check out the case study below.

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