Digital Business vs. Digital Transformation: Understanding the Key Differences
Today’s businesses are under pressure to transform their operations and customer experiences to stay competitive. This obsession to keep up
Today’s businesses are under pressure to transform their operations and customer experiences to stay competitive. This obsession to keep up
Time and again digital transformation initiatives fail. Research by McKinsey shows that 70 percent of complex, large-scale change programs don’t reach their stated goals. Some companies are now frightened to go down the route of digital transformation after their expensive ‘big bang approach’ didn’t work, despite a long and laborious process. But it doesn’t have to be that way.
Many companies are still grappling with the complexities of transitioning to a seamless digital business model.
The concept of Digital Twins is being actively investigated by Pharma companies. This technology has great promise as one of the levers to modernize the pharmaceutical industry.
An organization decides to embark on its agile transformation journey because every other organization is doing so.
Transforming data ecosystems is helping this global retailer better understand its customers’ needs and preferences, more accurately forecast fashion trends, manage inventory, and improve the overall in-store experience.
The invisible vectors of change; what to watch for in 2022
The invisible vectors of change; what to watch for in 2022
Changing Consumer Behaviour in the COVID Era : New Opportunities for Banks
DIGITAL BUSINESS IS THE PATHWAY TO GROWTH – FUELLED BY INNOVATION
The nonprofit sector may not be at the forefront of adopting technology. Indeed, many lack digital maturity. They lag behind in the use of the latest innovations.
Having an exact plan in place to manage customer demand is a dream for any business
| Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs. | Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify. |
| Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety. | Supplement trial data with real-world insights for drug effectiveness and safety. |
| Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies. | Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies. |
| Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing. | Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing. |
| Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development. | Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development |
| Remote and Decentralized Trials: Embrace virtual trials for broader patient participation. | Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits |
| Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention. | Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention |
| Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals. | Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals |
| Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment. | Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment |
| Data Standardization and Interoperability: Ensure seamless data exchange among research sites. | Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis |
| Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis. | Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process |
| R&D Investments: Improve the drug or expand indications | Utilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes |