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Technologies will help the pharma companies to guide human decision-making so that pharmaceutical sales and marketing teams can look to the future and past while processing data.
FREMONT, CA: Pharmaceutical companies have always had access to a steady stream of data to analyze what has happened previously and forecast prescription trends in the future.
Business intelligence (BI) departments have assisted this throughout with rapid and accurate reporting in an environment where BI resources have added an increasing array of chart styles and functionalities in recent years.
Technology, particularly artificial intelligence (AI) and machine learning is prepared to deliver new ways of analyzing and processing data, enabling the pharmaceutical industry to improve its analytics usage.
Business intelligence analytics today
Pharma companies may use BI to monitor sales success over time using key metrics such as market share, contact rates, and other endpoints. While making plans, it is necessary to know what worked in the past, but the numerous retrospective statistics that have been available to pharma to date will only indicate events that have occurred in the past.
The types of knowledge accessible to those in the pharmaceutical industry who evaluate sales and marketing success have changed dramatically in recent years.
Traditional NHS prescribing data has been supplemented with information on biosimilar adoption across the health system, real-world data, and other outlets, while the data sets accessible to pharma have expanded.
Depending on the size of their territory and the number of competitor products or packages in the markets, a typical pharma sales rep may now obtain up to 4,000 data points each month due to the emergence of big data.
New BI technology for pharma
With the growing size and number of datasets available, modern technologies will play a crucial role in 'noise cancellation for BI,' enabling those in the pharmaceutical industry to cut through the white noise and get accurate data. It's here where machine learning will succeed, carrying on some of the heavy lifting that the data requires to make sense of the numerous data points it provides.
At the same time, implementing AI to data sets will begin to discover hidden trends in a way that is not possible when a person has to click through 100 bricks or 200 practices and examine every package or product prescribed to see if anything interesting has occurred. There are numerous concealed patterns in the data that the human eye will miss, but the machine will not rest until they are discovered.