While there are many different definitions of digital transformation in life sciences, the core concepts they all have in common are digital technology and creating new, or modifying existing, ways of working. The underlying business driver is that digital transformation can help organizations evolve and stay competitive in the race to create value for customers/stakeholders.
It’s no secret that digital transformation is hard; the big consultancy firms tend to agree that the risk of failure is somewhere between 70-95%. People, processes, and technology are often described as the main components of successful digital transformation and the first two are by far the most difficult to get right. The disruption to established ways of working needs to be managed with effective change management and an appreciation that manual ways of processing data can sometimes have benefits that are not always obvious on the surface and may need to be replicated digitally in a different way.
One of the reasons why digital transformation is particularly challenging in the life sciences industry is because of the multi-disciplinary collaboration required. The need to establish a common language when scientists, IT/informatics, and data teams work together quickly becomes painfully obvious. Like the witticism that the US and the UK are two countries divided by a common language, these groups can easily be divided by common acronyms. Is an API an 'active pharmaceutical ingredient' or an 'application programming interface'?
In the rush to follow up on investment in data generating technologies such as lab instruments with investments in software and data infrastructure it can be easy to overlook the fundamentals. As the speed and volume of data creation increases, the challenge of getting the right data in the hands of those who need it remains the same.
Data is the fundamental currency of the pharmaceutical industry and like financial currency, it needs to be accessible, interchangeable, and reliable. As Bernard Marr has pointed out, data is changing our world and companies that view data as an asset to be cherished and protected will thrive and survive. Digital technologies provide speed and scale; a good data strategy is the real key to success.
Effective data strategy starts with understanding the connection between data and business goals and needs. A data strategy is not something you can buy off the shelf – it is a cultural shift driven by strategic priorities that requires alignment across your organization.
For pharmaceutical and other scientific research-intensive companies, this requires defining specific data goals and use cases. Leveraging AI/ML during discovery and development is a common aspiration but one of the greatest barriers in many companies is that the data is not ready to use. Data scientists in the pharma/life sciences industry typically spend 90% (or more) of their time cleaning and aligning data sets before they can start doing any meaningful work. While it’s tempting to try using a generative AI program to clean up and sort out your data retrospectively, failing to treat metadata with the respect it deserves is a big mistake. It’s far more effective to adopt a data centric view which considers where each data point and context comes from to create digital continuity from planning through experiment/measurement execution to insights. Simple improvements such as using drop-down lists, type-ahead text, and barcode scanners can ensure clean, contextualized data as it’s being generated.
Underpinning a successful data strategy, that enables your business to be data driven, are the two pillars of data standards and governance. Both are distinct but neither can exist on their own. Data standards ensure that when you record a piece of data, you are using the correct terminology or units of measure consistent across your business. Data governance is the framework and way of working that enables your data standards to be upheld.
Defining roles/responsibilities and getting those roles to work together properly even down to the level of operating instructions, i.e. who does what next, should be part of setting up a data governance process. The good news is that this has already been demonstrated successfully in the teamwork between medicinal and computational chemists, for example. Medicinal chemists already know how to drive programs with computation chemists; they speak a common language and share common objectives.
Every business has a different journey towards being truly data driven and this is the most important aspect of any data strategy initiative – your data is yours; it is unique to your business; and if you use it effectively to drive business decisions it can add value to your operation and enable differentiation in your market sector. ProPharma believes that to truly understand how a successful data strategy can bring value to a business, you need to have first-hand experience of the daily challenges. All our consultants have lab experience and deep scientific domain knowledge and we’ve developed tried and tested methodology that help you understand your current level of data maturity as well as current and future data use cases and a specific, actionable roadmap to realize your data goals.
Get help with your digital transformation journey from the experts at ProPharma. Contact us today to schedule a call with one of our consultants.