
AI is revolutionizing efficiency, accuracy, insight and productivity
With data being the pillar upon which pharmaceutical decisions are made, the need for accurate, accessible and actionable data is paramount. This makes data wrangling crucial to the ongoing research, development and deployment of pharmaceutical products.
The industry is paying close attention to how new technologies such as artificial intelligence are making the data wrangling task easier and quicker than ever, leading to improved efficiency, accuracy, insight and productivity.
The importance of wrangled data in pharmaceuticals
Properly wrangled data is the lifeblood of the pharmaceutical industry’s top priorities:
· Quality assurance
Wrangled data assures the highest quality and consistency of data by eliminating errors, discrepancies and duplicates, resulting in increased research and decision confidence.
· Regulatory compliance
Strict data governance standards are more easily addressed, and a well-documented data lineage simplifies and expedites the auditing process.
· Faster decision making
Wrangled data is easily accessed and analyzed in real time, empowering decision makers to quickly respond to emerging challenges and trends.
· Improved collaboration
Easily shared, wrangled data promotes cooperation among various divisions, promoting multi-function insights and quicker innovation.
· Enhanced data security
Patient information and proprietary research data are easy to protect with properly wrangled data. Using consistently applied data security protocols, the risk of data breaches is significantly reduced.
How AI advances the data wrangling process
Artificial Intelligence is positioned to revolutionize the data wrangling industry:
· Automated data extraction
Time-consuming, manual data entry virtually becomes a thing of the past. AI engines can automatically extract, sort and categorize enormous volumes of unstructured data from research papers, clinical trial results, patient records and other external sources.
· Data cleansing
Errors such as missing values, inconsistencies and outliers can be spotted and changed by AI, resulting in cleaner, more reliable data for more precise analysis.
· Data integration
Reducing the risk of data silos, integration from multiple sources creates a comprehensive, analysis-ready dataset.
· Natural-language processing (NLP)
NLP allows AI to understand human language. Extracting information from unstructured text such as articles and clinical notes, NLP can convert the information into structured data.
· Predictive analytics
AI can help forecast data trends, helping team members spot potential issues or opportunities in a timelier manner.
· Generative AI
As generative AI continues to advance, expect to see data wrangling solutions that allow users to create actual dialogs with their data for better, faster and more cost-efficient value discovery.
Together, wrangled data and AI are poised to shape the future of the pharmaceutical industry. Time-consuming and repetitive tasks will be eliminated, allowing data professionals to better focus on strategic functions. The risk of making decisions based on flawed information will be greatly reduced. Hidden patterns and relationships will be uncovered, resulting in better decision making. Maximized productivity will help bring products to market more quickly.
As the pharmaceutical sector continues to embrace AI-powered data wrangling, we expect to see unprecedented advances in the discovery of new drugs and in patient care, thanks to the dynamic duo of data wrangling and artificial intelligence.
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