Today many people in the life science industry understand the importance of data analytics in driving growth and innovation. But have you ever stopped to consider what the future of this technology might hold? What if data analytics could do more than just inform decisions and drive revenue? What if it could transform the way we approach healthcare and improve patient outcomes?
The truth is that the possibilities for data analytics in the life science industry are endless. From personalized medicine to predictive modeling, this technology has the power to revolutionize the way we think about healthcare. But with great power comes great responsibility. As we move forward into the future of data analytics, it’s important to consider not just what we can do but what we should do. In this blog post, we’ll explore some of the exciting possibilities for data analytics in the life science industry. So buckle up, and get ready to explore the future of data analytics in the life science industry!
Learning from the Past and Embracing Data Analytics
The Romans used to say, “Historia magistra vitae.” This basically means that mistakes are bound to be repeated if didn’t fix or at least found as such on time. If the ancient Romans knew this, what is our excuse not to learn, observe, take notes (aka collect data), and then analyze in order to make intelligent decisions? Easier said than done, some may say.
If we take a look at the controversial glass of water that is somehow constantly not half full nor half empty, we, as a team, would always say it’s almost full. Because we are a group of optimists and tend to find positives in almost every situation. A great example of this would be in 2020 – we witnessed something amazing in terms of data. The pandemic, a reoccurring event in human history, shook us in many aspects and showed the raw power of data. We used to seek it every day. We followed it around and made important decisions based on it.
And by “we”, we mean the world, not just us, data analytics experts. This event taught us many things, but most importantly, to ask the right question – what if? What if another pandemic hits? What if there is a breakthrough in medicine, and we miss it because we don’t have enough data to support it or even notice it? What if we make the same mistake?
Healthcare management depends heavily on data
In today’s world, where every decision is based on data, effective healthcare management relies on the ability to collect, analyze, and act on information. With a wealth of data at their fingertips, healthcare professionals can make informed decisions that lead to better patient outcomes and streamlined operations. Specifically, healthcare management depends heavily on data for the following purposes:
- to create a proper patient journey analysis and identify the best treatment options
- to enhance the process of clinical trial and decision-making with minimal manual effort
- to identify high-risk patients
Just to name a few. Of course, this wasn’t initiated by the pandemic, but the need for real-time assessment of data became really urgent. Now the question is actually not “what if we don’t have data” but rather “what if we don’t have enough time”? Implementing data-driven models is now crucial for many sectors and not just in healthcare. This inevitably brings advancements in Big Data Analytics, Data Science, and Artificial Intelligence which leads to a transformation in the way businesses are run. Also, begs a new question – not “should we use data” but “how.”
Data as a Service (DaaS): Sharing and Analyzing Data Efficiently
Artificial intelligence is evolving, to say the least. It revolutionizes business and gives us more confidence to use machine learning, robotics, and automation. Today’s clever AI and machine learning methods can deal with small data sets, unlike traditional techniques. It gives us a better shot at forecasting and management which in healthcare ultimately means saving lives.
Also, with data democratization, more members within organizations are empowered to interact with data and make better decisions. But it’s not just the people that have more freedom – IoT devices are capable of speed, agility, and greater flexibility because of advancements in the field, especially with edge computing and 5G. This gives us the opportunity to perform real-time analytics and enable autonomous behavior. We can now process massive amounts of data and derive insights – valuable insights that we can act upon.
Also, we can predict better thanks to augmented analytics – a new trend that uses NLP (natural language processing) and machine learning for the automation and processing of data. We have the power not only to observe but ask relevant questions. Automation in data analytics is key because it lets us, humans, be less involved (ergo, make fewer mistakes). The automation of data analytics processes can have a significant impact on the productivity of many sectors.
But knowing is not enough. We also need sharing. DaaS, or Data as a service, is a cloud-based software tool used to analyze and manage data, such as data warehouses and BI tools, which can be run from anywhere, anytime. It allows not only access but using and sharing. The usage of DaaS in big data analytics is making business review tasks for analysts easier and sharing data across departments and industries faster. Since more and more businesses are turning to the cloud to modernize their infrastructure and workloads, DaaS has become a common method of integrating, managing, storing, and analyzing data.
Having all those tools makes us more capable of planning the future and predicting events and results. In chess, they say the following: Tactics is knowing what to do when there is something to be done, and strategy is knowing what to do when there’s nothing to be done. This means you need both, and what those two things have in common is data. Knowledge, based on data. Apriori and posteriori. You need empirical evidence and experience – your own or borrowed from someone else, but credible and verified, which in data analytics means minimum repeat. When we analyze large datasets, find hidden patterns, unseen trends, and connections, and make conclusions out of them, we can define a problem, collect, arrange, and clear data, interpret the result, and then suggest ways to act upon it.
Like many other things, the future of data analytics is already mapped out – it will get so fast that it will happen now. “Now” means real-time-visualizating-itself-now, not just “today.”
Literally, it will come alive – we will be able to access, explore and analyze data that will be visualized before our eyes and compiled from different sources, updating itself by the hours, minutes, and seconds. It is very exciting and inspiring to look for more fields to apply it.
Data analytics in the life science industry
Data analytics in the life science industry is not news but rather a must. In order to research, develop and manufacture pharmaceuticals and medicines, medical devices, biomedical technologies, nutraceuticals, and other products that improve the lives of organisms, you need data. No need the emphasize the importance of Big Data in the industry because the adoption of analytics solutions is growing fast enough. The technological advancements over the past few years are speeding up, and many companies now consider digitization as a strategic move – it allows them to improve their productivity in R&D, manufacturing, and compliance management. This minimizes errors and allows for precision. The use of genomic data permits the development of personalized medicine and advanced analytics – for predictive modeling. We can not only identify but apply the best treatment.
What are the challenges of data analytics in healthcare?
The challenges the sector faces are mostly in terms of the cost of implementation and data privacy, but they are surmountable. We are increasingly focusing on technological
advancements such as the use of Al, machine learning tools, and techniques for the analysis of life science data. So what if the future repeats the past, and we end up not using the advancements we made? Because this is the real threat. We have the tools and the posterior knowledge of how to use them. What are we waiting for?
Since day one of B EYE our team is on a mission to help businesses overcome data challenges by using the amazing power of data analytics, planning, and automation. Our WHY is to make companies win the long game by utilizing their own data and making strategic decisions on time. Not only that we witnessed the growth of many companies, but we also helped them achieve it. How? By following data.
Ethics and Responsible Use of Data Analytics in Life Sciences
As the potential of data analytics in life sciences continues to expand, it’s essential to address the ethical implications and ensure the responsible use of this technology. This includes addressing privacy concerns, ensuring the quality and accuracy of data, and maintaining transparency in data handling and decision-making processes. The following aspects should be considered when implementing data analytics in the life science industry:
Data privacy and security
Ensuring that personal and sensitive information is protected and handled according to regulations, such as GDPR and HIPAA, is critical to maintaining the patient trust and avoiding legal repercussions. Life science organizations must invest in robust security measures to protect data and maintain compliance with evolving privacy regulations.
Bias and fairness
As data analytics and AI technologies are used to inform decisions in healthcare and life sciences, it’s essential to recognize and address potential biases in data and algorithms. Developing strategies to mitigate data bias can help ensure that all patients receive equitable treatment and care.
Transparency and explainability
Life science organizations must be transparent about the data sources and methodologies used in their analytics processes. Providing clear explanations of how data-driven insights are derived can help build trust in the technology and ensure that stakeholders understand the basis of decisions made using data analytics.
Collaboration and data sharing: Encouraging collaboration between organizations and fostering an environment of data sharing can help accelerate innovation and improve patient outcomes. Establishing standardized data formats and sharing protocols can facilitate the exchange of information and enable the scientific community to harness the full potential of data analytics.
In conclusion, the future of data analytics in the life science industry is promising and full of potential. As the sector continues to embrace data-driven approaches and AI technologies, it is crucial to address the challenges and ethical implications of these advancements. By investing in data privacy and security, addressing biases, fostering transparency, encouraging collaboration, and developing a skilled workforce, the life science industry can harness the power of data analytics responsibly and effectively to improve patient outcomes, streamline operations, and drive innovation.