The Life Sciences industry is in panic mode right now, thanks to increasing competitive pressure, rising customer expectations, tightening regulatory requirements and the breakneck pace of technological change. Enterprises are challenged to become more efficient at developing products and more responsive to customer expectations, all while adjusting to the demands of GDPR and other transformative regulations. Meeting these requirements, for most Life Sciences companies, means committing to a digital transformation strategy that will enable them to extract business insights and value from the vast amount of data that is housed in various repositories and file systems.
Read on to see what challenges Life Sciences companies face as they attempt to change the way they leverage their unstructured data to maximize their potential.
Digital transformation in the Life Sciences industry
The need for Life Sciences enterprises to digitally transform arises from a variety of interlocking factors. Namely, competitive and regulatory pressures combined with internal process challenges. New competitors are moving in, there is ever more market and customer feedback available to be considered, process efficiency and agility are improving dramatically, effective patient care is moving to center stage, and patients are becoming more engaged, demanding more for less cost and effort.
The need for Life Sciences enterprises to evolve into more agile, data-driven organizations is not in dispute. However, the journey is long, and companies find themselves challenged by the process. Despite new technologies being constantly introduced, this evolution has yet to materialize.” A recent McKinsey report is even more alarmist, arguing that the pharmaceuticals industry needs to re-imagine the way companies work, and that Big Pharma may be doomed to fail if it doesn’t transform itself.
Digital transformation is data driven
At the root of an enterprises’ ability to utilize all of their data – and derive its maximum value – is the need to access and analyze both the structured and unstructured data they possess. For most Life Sciences companies, this means a complete digital transformation.
Structured data, neatly housed in rows and columns in known databases, is usually a small segment of the overall data universe within a company. In fact, whether you’re dealing with electronic trial master files, lab notebooks, IDMP, contracts or clinical data, chances are good that 80% of an enterprise company’s content is unstructured—meaning it’s not machine readable. It can’t be used in data analytics, and it often can’t even be found, because it’s not searchable.
This is an increasingly significant problem given the rate at which data is copied and moved within an enterprise. It ends up in file shares, inherited M&A content, copies of information spread across repositories, as email attachments and in many other places. This proliferation of unstructured data, across the millions of pages of data an enterprise takes in each year, can make the problem of tackling it completely overwhelming. Especially if companies continue to use old-school, manual approaches to dealing with their unstructured data.
How digital transformation benefits Life Sciences enterprises
In Life Sciences, enterprises are driven to improve patient outcomes, accelerate innovations, and mitigate risks while reducing costs and inefficiencies. Generally speaking, achieving these goals requires that companies use data driven techniques to improve in three key areas.
1. New Products and Services
Finding ways to get products and services to market faster has always been a key business driver. The improvement of the product development process is facilitated by the shift from paper to high-quality electronic submissions—which accelerates review and approval cycles, speeds up research, drives innovation, and facilitates collaboration. A streamlined and automated digital approach helps maximize the potential returns before patents expire. It also reduces potential costs—which can be significant.
That number balloons up to an average of $8 million for blockbuster drugs. More importantly, improved digitized workflows help speed up the delivery of life-saving compounds, devices and services to the people that so desperately need them.
Digital transformation also benefits merger and acquisition activity—which usually involves the intake of huge amounts of data in the form of IP, contracts, and research. Much of that incoming data is unstructured, coming in the form of paper or emails or PDFs, and this prevents the acquirer from accurately identifying the true value of what they are purchasing. Even worse, they can’t evaluate the potential risks lurking in all that dormant data. Even up to three years after a merger or acquisition, some companies struggle to fully leverage their new IP, and many aren’t yet able to assess if all their clinical trial results are correct.
2. Customer Experience
The Life Sciences sales model is being challenged by rising expectations based on experiences customers have on a daily basis with companies in other industries. Quicker and better support, online purchasing solutions, lower costs and increased responsiveness from companies like Apple and Amazon are setting a new and increasingly high bar.
To satisfy these rising customer expectations, “around the pill” digital solutions are necessary to augment the foundational business models. These solutions require real-time responsiveness, which comes from automated processes to improve cost, reactions, and agility. Innovation and the ability to deliver higher quality care while reducing costs means being able to share and access data anywhere, anytime and with any device.
Within the Life Sciences industry, regulation is increasing—from drug identification requirements (IDMP), to drug serialization requirements, or the EU data privacy law (GDPR) which has significant impact on patient data in clinical trials. All of these create additional cost burdens.
Consider just one example: the case of a patient who participated in a trial fifteen years ago and now requests that they be forgotten (per GDPR.) The organization that conducted the trial must identify and remediate all of that customer’s PII. That is not an easy task for an enterprise that has no idea what PII is hidden in its unstructured data. It’s likely they’ll be unable to fully comply, exposing them to the risk of significant fines.
Across the board, protecting the privacy of patient data is a top priority. Patient privacy and compliance must be continuous and automated, in order to be truly effective, but you can’t protect what you can’t find, so the first step is making sure all of your data is searchable.
The need for an effective data capture and file analytics process
Digital transformation is an ongoing process, not a one-off project that can be completed on a deadline. Succeeding at this digital evolution means leveraging and exploiting the complete set of data, including that which is hiding in an organization’s dark and hidden corners. It has to be made clean, accessible and machine consumable.
- Automating manual processes to improve internal and external customer experience
- Understanding what you have and cleaning up the ROT (Redundant, Outdated and Trivial data)
- Standardizing and enhancing data to ensure accessibility and content re-use
- Enriching, classifying and analyzing data to accelerate decision making
Ultimately the implementation of such a process will enable Life Sciences enterprises to leverage the latest technologies, increasing their competitiveness by obtaining new, high-value business insights. They will be better able to predict trends and identify the smart ways forward, avoiding the need to react to sudden and unforeseen situations.
Digital transformation is an ongoing process—a journey that Life Sciences enterprises will eventually have to undertake. The good news is, the journey doesn’t have to be complete for an enterprise to start to realize its benefits. As with most ventures in life, the important thing is to get started. And the best way to do that is for an enterprise to tap into the vast volumes of dark and dormant data it certainly possesses. Using data-driven insights from advanced analytics, enterprises will be better equipped to increase pipeline and commercial value for the organization. As they engage in the digital transformation process, companies will find themselves able to drive better, faster decisions through information governance, management, and analytics.