Pharmaceutical companies are facing a data explosion. Traditional dashboard solutions can’t keep up with the speed at which data is generated in the pharmaceutical industry. Additionally, dashboards require data expertise to build and use, which limits user autonomy – and user adoption. Advanced analytics in pharma, platforms built with a combination of advanced technologies, help organizations overcome those challenges.
The Tech Behind Advanced Analytics in Pharma
Disruptors are leveraging a range of technologies to bring new capabilities to pharmaceutical analytics, including:
- Deep learning: This type of machine learning uses a neural network of multiple layers of processing to give the platform advanced capabilities to learn from data, recognize speech, uncover patterns, and identify anomalies.
- Natural language query (NLQ): Solutions with NLQ capabilities easily deliver insights by allowing users to ask questions conversationally. With NLQ, users don’t have to learn keywords or how to phrase queries to get the answers they need.
- Natural language understanding (NLU): This technology enables a platform to understand human language, both the literal meanings of words and a user’s intent. When a platform is intended for a life sciences company, however, pre-training is vital. The platform needs to understand the specific terms that pharma company employees use and the types of data they need to analyze. Pre-training gives the platform these capabilities right out of the box and streamlines implementation.
- Continuous cognitive intelligence: When business users query an analytics solution, the answer may lead to follow-up questions – much like having a conversation with a human. Continuous cognitive intelligence remembers what users ask, so the user doesn’t have to repeat phrases, filters, or keywords when they request that the platform dig deeper for insights.
- Slot filling: Platforms that are most user-friendly will detect missing information from queries. The platform will ask the user for clarification so that it can provide the most accurate response.
- Natural language generation (NLG): An analytics platform with advanced capabilities can provide responses that users easily understand, regardless of their data science expertise. Artificial intelligence (AI) can also give the platform the ability to automatically choose the best visualization for the insights and allow users to select the type of visualizations they prefer.
- Zero-code technology: Many pharma business users aren’t IT experts. The best solutions are designed with a no-code environment. This allows users to build their own dashboards, choose visualizations, and share insights, all without coding.
- Embedded and mobile interfaces: Top analytics platforms make it easy for business users to build data-driven decision making into their day-to-day workflows. A platform with embedded and mobile device interfaces allows users to access insights within the business applications they typically use, like Veeva or Salesforce, and when they’re working on a PC, laptop, tablet, or smartphone.
The Benefits of Advanced Analytics in Pharma
Transitioning to an advanced analytics platform makes accessing insights more user-friendly for all pharma company employees. However, it also provides more far-reaching benefits. Advanced analytics in pharma enables improved business outcomes. Sales teams can pinpoint top opportunities, patient services teams can identify patients at high risk of discontinuation, and clinical development teams can quickly establish trial protocols, choose participants, and analyze trial data. With that information, pharma teams can take products to market sooner, maximize revenues, and improve patient outcomes.
By giving business users analytics autonomy, enabling unlimited data sources and volumes, and responding to queries in less than a second, advanced analytics in pharma allows organizations to finally see the needle move on data ROI. Advanced analytics in pharma leverage a combination of technologies, including natural language processing, deep learning, and embedded interfaces. Visit WhizAI to know more about Advanced Analytics in Pharma
