September 26, 2022
What is Advanced Analytics?
Advanced analytics provides the foundation for automating decisions by applying next generation methods to solve business problems. The methods utilized produce quantitative, sophisticated insights that are unlikely to be obtained through traditional approaches like business intelligence (BI). Diagnostic, predictive, and prescriptive analyses leverage artificial intelligence techniques, such as machine learning to make sense of and learn from data.
Through the traditional techniques of Descriptive Analytics and BI, we develop insights through reviewing reports and dashboards of historical data and trends that show us what has happened. With Advanced Analytics we leverage data science and machine learning technologies to analyze and model systems for simulation to surface root causes (Diagnostic Analytics) and predict (Predictive Analytics) and prescribe (Prescriptive Analytics) future outcomes or actions.
All are beneficial to the business for different reasons. Descriptive Analytics can drive new insights from already available data whereas Predictive Analytics can allow organizations to understand trends in the data and how those trends influence outcomes. Prescriptive Analytics can enable organizations to ask and answer the question “what do we do next?”.
But this is just the surface, there are lots of changes happening in the market as companies bring traditional and advanced analytics together, organizing information in new and interesting ways.
One exciting way of leveraging these principles is through Simulation. So, what is Simulation?
Advanced analytics identifies patterns, creates change scenarios, makes predictions, and proposes actions based on outcomes. Simulation provides a way to examine alternative outcomes and scenarios before, during, and after executing a plan.
If the outcome of an analysis leveraging advanced analytics points to the need to make changes, how that change is implemented is just as important as the analytics. Simulation provides a way to test adjustments to the system and to determine future impacts.
Both Advanced Analytics and Simulation are important in assisting Life Sciences organizations and the Benefits of Leveraging Advanced Analytics in the Life Sciences Industry allow organizations to:
- Accelerate Drug Discovery and Development
- Increase The Efficacy of Clinical Trials
- Personalize & Create Targeted Medications
- Reduce Cost and Increase Drug Utilization
- Leverage Social & Search Engine Listening to Capture Data of Interest
- Drive Effective Sales & Marketing Operations
- Streamline Compliance
- Improve Operations & Employee Training
Trends regarding Advanced Analytics & Simulation
The following are the key trends driving emerging technologies across the industry:
- There is a strong desire to leverage legacy data, external and real-world data to drive increasingly advanced predictive analytics, models, and simulation activities, such as digital twins, trial simulations, signal surveillance, etc.
- A shift in thinking and technology to support:
- Federated Learning (i.e., training models across decentralized systems to eliminate the need to bring raw data in-house)
- Democratization of data sciences (i.e., creating citizen data scientists)
- Automation of AI/ML model development, selection, training, parameter tuning, and deployment
- Insight engines beyond just enterprise search to enable users to tap into previous outputs and decisions rapidly
Key Considerations when utilizing Advanced Analytics & Simulation
- Build predictive and prescriptive modeling capabilities with big data analytics powered by AI/ML across R&D.
- Advanced Analytics Infrastructure
- Develop platforms and systems to enable an advanced analytics model
- Setup data intake and data management architecture to streamline data availability and processing capabilities for the advanced analytics
- Implement self-service analytical tools and automate the preparation and provisioning of data
- Partner for federated modeling capabilities as well as for platforms and technologies
- Knowledge Management and Governance
- Build skillsets in IT and data science, as well as core R&D functions, to increase AI/ML knowledge
- Use insight engines to surface new information from already available data
- Enable more users to discover and explore insights to help drive decision making with greater efficiency
- Build data governance models to ensure ‘FAIR’ data to enable access, reuse, AI and/or advanced simulations from transactional systems, data warehouses or data lakes.
- Leverage AI/ML technology to automate analytical tasks, such as
- Data preparation
- Insight generation
- Analytical model development
- Surfacing data to end users
Why it Matters to You
Advanced Analytics and Simulation will play a significant role in a digital transformation of the life sciences industry as we move into the future. These technologies provide major benefits in multiple areas of the business.
In this blog post we discussed:
- What Advanced Analytics is and how it differs from the traditional techniques of Analytics and BI.
- What Simulation is and its role in assisting in the process.
- Key benefits of these technologies in life sciences.
- The trends associated with increased appeal to leverage Advanced Analytics and Simulation.
- Key considerations when looking to utilize these technologies.
To learn more about other emerging technologies, watch our webinar replay on the topic – Emerging Technologies’ Impact on Digital Transformation in Life Science
Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community. Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.