Big data is infiltrating every type of business across all types of industries. Life science industries generate a massive amount of large and complex data daily. This structured and unstructured data comes from a variety of different sources and requires a means of analysis to uncover hidden business value that helps gain a competitive advantage. Within enterprise data are patterns of human behavior that provide useful business intelligence. Working with data from diverse sources requires the extraction and analysis of information to make data-driven business decisions. Take a look a why data analytics are crucial in life sciences and the benefits of using them.
Accurate risk assessment in the life sciences industries helps reduce the spread of epidemics, increase life expectancy, and prevent fatalities. Advancements in big data analytics make it easier for business users to identify trends earlier and faster, resulting in the creation of more expedient and effective solutions. Big business data allows organizations to identify trends in data that present potential hazards, evaluate the severity of the hazard-associated risk, decide how to eliminate the hazard, and determine how to control the risk if the hazard can’t be eliminated.
An optimal way to overcome the obstacles of working with different types of data from diverse data sources is to utilize data virtualization. Subsequently, data virtualization offers a modern data layer that business users can access, combine, transform, and deliver datasets expediently and cost-effectively. Data virtualization technology provides fast access to data throughout the enterprise, whether it’s in traditional databases, cloud storage, or data silos, for a fraction of the physical warehousing and extract, transform, load (ETL) time and cost. Common data sources that can be virtualized through data virtualization software include Excel and flat files, data warehouses, data lakes, XML docs, cloud data, web services, and IoT data. There are several benefits of data virtualization including business value acceleration, business insight improvement, development cost avoidance, and data management infrastructure cost reduction.
Assess the Effectiveness of Clinical Trials
A fundamental aspect of the pharmaceutical drug discovery process is clinical trials. Clinical trials are conducted to introduce a new device or drug to the masses and can be used in tandem or alone to treat patients and/or extend their life. Trials determine if a new type of diagnosis intervention can better identify a specific medical condition. They are used to evaluate and approve new drugs, devices, or surgeries that can be used to treat a disease, condition, or syndrome. Lastly, clinal trials are conducted to test prevention methods such as medications, vaccinations, and lifestyle changes. Big data offers advancements in better patient recruitment, effective engagement, efficient trials, and high-quality results. This in turn results in improved predictive modeling of drugs and biological processes and improved safety and operational procedures.
Disease control, food safety, nutrition research, and dietary guidelines are fundamental to public health. The large amounts of data that the food industry generates provide key insights that help improve human health, well-being, and the environment. The International Life Sciences Institute is a global, non-profit organization that prioritizes scientific integrity to develop sound science that benefits society. ILSI specializes in connecting scientists from academia and public and private sectors to collaborate in a neutral forum on scientific topics. This institute promotes the advocacy of science and health issues of mutual interest to improve the science behind the promotion of public health and safety as well as the environment.
Improve R&D Efficiencies
The pharmaceutical industry has a notoriously long research and development (R&D) process. Bringing a single drug to the market can take more than 10 years of research and billions of dollars. Big data enhances and speeds up the R&D process by improving drug discovery, the design of clinical trials, and the detection of negative drug reactions. Big data helps optimize innovation, improve the efficiency of clinical trials and research, build new tools, and help regulators determine more individualized approaches. The pharmaceutical industry can effectively use data to identify potential drug candidates and develop them into approved, effective medications faster. Data analytics in life science industries helps improve risk assessment, assess the effectiveness of clinical trials, and improve R&D efficiencies.