Everything we read, write, and say generates data. Anything produced, bought, sold, and consumed generates data.
The opportunities that data mining holds have not escaped industries like finances, insurance, law enforcement, and telephony, where players have started leveraging its potential years ago.
The health and life sciences sectors may be late-comers to the game, but the rapid progress we witness will bring them up-to-speed in no time. From genomics to EMRs, from regulatory agencies to pharmacovigilance, from market trends to consumption habits, Big Data promises to help organizations bring better products to market, improve research efficiencies, minimize risk and drive costs down.
4 Immediate Benefits of Big Data in Life Sciences
Improved R&D and Clinical Trials
As data access and data quality improve, pharmaceuticals and biotech companies will be able to take full advantage of the volume and significance of collected information.
Genomics already prove to be a treasure trove for researchers, with its infinite potential for combinations. IoT has entered the labs, and researchers have to double as data scientists to interpret results and patterns reported by hospitals, physicians, academia, and users.
Another advantage of the world digitalization is the speed at which data is available (think real-time). The constantly-renewed datasets facilitate the identification of patient populations thanks to digital biomarkers, health records, and wearable devices, allowing companies to select sites and recruit patients quicker and with a higher chance of success.
Ultimately, the use of predictive analytics from raw data means a shorter time to market and quicker revenue generation.
Price Management & Profit Forecast
As governments’ concerns over the constant increase of healthcare put pressure on them, life sciences firms turn to data to identify areas of improvement. Regulatory agencies are somewhat leading the way with collaborative databases.
By focusing on compliance requirements and mining data from shared databases and post-market surveillance, manufacturers can spot patterns and predict trends, prompting changes and processes improvements.
Data on drug efficacy collected from health practitioners and patients can also serve organizations to either defend their price position or gain an advantage over more expensive competing products.
Accessing and managing analytics on demographic trends, population characteristics, and lifestyle changes will also prove useful to forecast revenue and profits and select new market opportunities.
Knowing that life expectancy continues to increase globally (72 years in 2016), implementing the right technologies and finding the right ways to exploit data opens exciting avenues for life sciences players and motivates them to become part of the solution.
There is more than one use per specific dataset. All data generated, collected, and processed contributes to increased safety and efficacy of medicines.
One significant step toward better health is the emergence of personalized medicine. According to Deloitte, the market of precision medicine is expected to increase over 11% between 2017 and 2024 (CAGR), a growth imputable in part to health care analytics, artificial intelligence, and blockchain. The trendsetter in patient-based medicine is oncology, most specifically molecularly-targeted agents, but great achievements also happened in cardiology and in vitro diagnosis.
Despite the need for more support in research and development, innovative medicines in general “… have made tremendous contributions to public health”, as stated in a 2012 report by The President’s Council of Advisors on Science and Technology.
Improved Health Outcomes
In the wake of precision medicine, it is worth mentioning the role of Software as a Medical Device (SaMD) across the entire life sciences community. The data delivers insights that help screening, diagnosis, adjust treatment, manage chronic disease, and much more. For manufacturers whose devices are supplemented by SaMD, the response to adverse events is quicker, and the clinical data allows us to gain a better understanding of benefits and may reveal new areas of efficacy. Coupled with a more in-depth knowledge of their market, SaMD providers and their partners provide a more efficient and more holistic approach to therapies and better care to patients.
Challenges and Strategic Solutions
With opportunities brought by digital technologies come disruption and risks. Life sciences companies that make the strategic decision to embrace Big Data should ready themselves for the challenges it creates.
Data Protection & Privacy
Integrating third-party technologies to process and analyze data poses a cybersecurity concern. Any breach in databases has repercussions for the organization and the public. As a responsible entity for regulatory compliance, pharma-techs must be able to ensure proper data governance and demonstrate anti-piracy protection as well as the ethical use of internal and external data.
The public is also becoming acutely aware that personal data is collected and exploited everywhere every day, and growing concerns may result in stricter legislative measures from governments.
IT & Management Integration
An IT department’s role is shifting from merely assistant to non-tech savvy employees to a full-scale revenue driver. As such, organizations are experiencing the need to close the gap and review and improve cooperation between teams.
IT staff experiences the other side of the paradigm, having to absorb clinical information that they were not familiar with.
The birth of MedTech has created a new burden for IT personnel to move faster, higher, and farther. The 2019 global life sciences outlook report by Deloitte reveals that only 20% of surveyed biopharma companies consider they are digitally maturing. The organizations that do actively deploy efforts to reach digital maturity “… display higher […]data analytics skills.”
Shortage of Skilled Employees
The increasing importance played by technologies in health care is driving investments and defining the race to beat the competition. As a result, life sciences firms need to reduce their skills deficiencies and are competing not only with one another but also with other tech giants to hire data scientists, developers, and analytics experts. This competition is driving the cost of attracting talent and encouraging them to turn to contingent labor.
As pharmaceuticals and biotechs enter a new decade and embrace the digital age of medicine, they need comprehensive data sciences solutions, from robust data architecture to data management, biostatistics, and programming.