The field of Life science includes all those activities related to the pharmaceutical industry, biotechnology, the industry for the production of medical devices, health services, the industry of clinical trials. It is an area in which models based on Artificial Intelligence open up scenarios destined to revolutionize the paradigms of reference of traditional medicine. At the same time, it is an area where data processing regulations are very stringent, laying down rules for corporate data governance frameworks.
Life science big data governance
All research and development activities base their success on practices of knowledge and information sharing, for this reason Big Data and the application of mathematical models and algorithms of Artificial Intelligence and Machine Learning represent a great opportunity for the development of medicine of the future, both for industries and for patients.
Traditional medicine will soon be overtaken by a multidisciplinary approach that involves collaboration between doctors, mathematicians, engineers and computer scientists to relate biological processes, physical events, lifestyles, environment and diseases, statistical and individual data, so as to offer increasingly personalized care and prevent the onset of diseases rather than cure them.
Life sciences, healthcare, and medtech increasingly need solutions that enable the collection, manipulation, aggregation, and analysis of huge amounts of data quickly and accurately. The management of Big Data becomes a fundamental issue for clinical trials, for the creation and production of new drugs, to develop new technologies at the service of medicine. As well as integrating different sources into a single Data Lake.
Alongside the management of big data for research and development, predictive, prescriptive and augmented analytics models become crucial for the success of new products and solutions: from design to marketing, Life science projects involve large capital and resources, and like all companies need to make effective strategic decisions to achieve success and thrive. Business Analytics is therefore the established tool for the management of clinical trials, the optimization of production activities, the support to the field force, etc.
RWE (Real World Evidence) based on RWD (Real World Data) is on the horizon and is already being used by government and industry decision-makers in addition to the most enlightened and innovative pharmaceutical companies to improve the evaluation and understanding of the effectiveness of drugs and therapeutic pathways. FDA (Food and Drugs Administrations) docet. To this important aspect we will dedicate a specific article.
Security of sensitive and health data
The peculiarity of the Life science sector is to manage, more than others, sensitive patient data, health information of individuals or entire communities, information related to HCP (Health Care Professional) as HCO (Health Care Organization). It goes without saying that the aspect of Data Governance assumes a decisive importance: creating a correct data governance framework is the pre-requisite for any company that wants to extract value from data, but it becomes a binding condition for organizations in this sector.
In Europe, the data governance for Life Science must respect the most stringent links of AIFA (Italian Agency for Medicines) and EMA (European Medicines Agency that unfortunately has not been established in Italy – Milan), in addition to the European regulation GDPR, as well as the Data Governance Act, and provide for all those aspects at all secondary such as the protection of intellectual property and the proper exchange of information between doctor, patient and suppliers. Even across the ocean the question is sharp: private interests often overlap with public interests, hence the need to adopt flexible models, able to comply with current regulations and change with them in the not too distant future.
A global approach is therefore necessary, according to a network innovation logic, encouraging organizations and companies in the sector to establish valuable partnerships to achieve their goals more quickly, reducing costs and risks.
The DataOps methodology, to which we will reserve a future article, can help in defining a correct approach of tools, processes and methods.
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