Raj Babu is CEO of Agilisium Global Talent Solutions Company for digital transformation assignments.
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Digital analytics is slowly and steadily transforming the life sciences industry with its ability to process large amounts of complex data and produce deep insights, improving overall ROI for precision medicine and clinical trials.
Traditionally, the healthcare industry has been slow to integrate new innovative digital technologies, but the pandemic has forced companies to move quickly to digital and apply it in every aspect of their work. This is according to a report from Deloitte. more than 60% of life sciences companies are already investing heavily in artificial intelligence (AI) technologies to pursue business goals such as making processes more efficient, creating new services and products, and improving existing products (28%). Another report found that the global life sciences analytics market “is set to grow $15.95 billion in 2021-2025with a CAGR of 11.83% during the forecast period.”
But what are the key ways data analytics is transforming the life sciences industry?
1. Paving the way for personalized medicine
Personalized or precision medicine groups patients based on genomic data, leading to more targeted treatment and better long-term medical outcomes. But to make personalized medicine a success and determine the right treatments for patients, terabytes of clinical and user-generated data must be properly collected, processed and integrated.
One of the best ways to understand all this data in a short amount of time is through advanced data analysis. Data analysis can be used for precision medicine by focusing on patient diagnosis, biomarker discovery, prognosis and disease subtyping. It can combine data from multi-omics, EHRs, and wearable and implantable devices in real time for accurate analysis.
2. More effective clinical trials
Companies in the life sciences industry also use technology to ensure that clinical trials produce quality data that is free of errors, inconsistencies, outliers, and misreported events. The aim is to speed up the drug approval process. But the biggest hurdle has been the large amounts of data in clinical trials increase exponentially.
Data analytics can help analyze large clinical trial data, decide which information is relevant, and draw insights from the vast volumes of data. It can also help to use patient electronic hospital data along with their past medical records to find the right candidates for a particular study, which can save time and improve cost efficiency.
3. Better risk assessment
Risk management is a critical and non-negotiable process in the life sciences industry. Each year, at least a few companies receive warnings from regulatory authorities around the world, including MHRA (UK), EMA (Europe), and FDA (US). Data analytics helps establish risk management as an ongoing and continuous process. By analyzing data, you can gain accurate insights and predict risks before they happen, and take steps to avoid them.
4. Optimized R&D
R&D is typically a long and complicated process for the life sciences and healthcare industry. According to only one study 0.1% of drugs that begin with preclinical testing are eventually approved for human use. At every stage of the process, historical and current data must be analyzed to make decisions about whether to continue certain connections or remove them altogether.
The introduction of data analytics into R&D can enable rapid data processing of large amounts of data and deliver more focused business outcomes. In fact, it is estimated that analytics in R&D “can reduce development costs by at least 15% and accelerate time-to-approval by approximately six months while significantly improving patient outcomes.”
How to choose a data analytics partner
Harnessing the power of data analytics becomes much more achievable when you have the right analytics partner leading you in the right direction. To find the partner that best suits your business, here are some questions to ask.
1. Do they understand your business?
To deliver value and results, your partner needs to have a clear understanding of your business, the key challenges you currently face and why you want to use data analytics. Generic data analytics providers may not be well positioned to help you execute the various business cases in your organization, so look for a data analytics partner who understands the challenges and intricacies of the life sciences industry.
2. Can they help scale your business?
Data in the life sciences sector is growing exponentially. That’s why it’s important to ensure that the vendor has the processing and storage capacity to scale quickly as your business and big data requirements grow.
3. What are the supplier SLAs like?
The data analytics partner should have a clear set of service level agreements (SLAs) and a strong support commitment, including SLAs for time to response, time to resolution, and time to recover in case of disaster recovery. At the same time, you should also be allowed to break or renegotiate the entire contract in case SLAs are not met.
4. Does the seller follow all necessary compliance?
The life sciences industry is required to adhere to numerous data storage legal compliances, and these compliances can also vary depending on where you are located. You must ensure that the data analytics partner has the required level of data security and all necessary compliances that your company is expected to follow.
5. Does the seller have relevant experience?
The goal of partnering with a data analytics company is to bring in new skills and technology solutions that are not available in-house. To successfully manage and analyze big data, you need a data analytics partner who can help you get the most out of big data through technical skills, advanced analytics, and a deep understanding of your specific business challenges.
Despite the initial hiccups, data analytics is the strategy the life sciences industry is pursuing to drive innovation, increase efficiency, and leverage the vast amount of data. However, the promise of data analytics can only remain a promise if organizations cannot find the right technology partners to take the right strategic steps to implement it.
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