Four core principles for pharma firms seeking to embrace the data revolution
Andrew Dunbar, general manager for EMEA at global digital consultancy, Appnovation, explores the core principles of data analytics and how pharma firms can benefit from this data revolution.
Covid-19 has accelerated digital transformation across most sectors of society, forcing companies to immediately adopt new processes they had planned to introduce incrementally over months or years. For pharma firms seeking to emerge stronger from this paradigm shift, it’s vital to adopt a smart approach to data analytics.
The irreversible transition towards a digitally-powered society and economy was on its way long before Covid-19 struck. But there’s no question that the restrictions enforced by this pitiless pandemic have accelerated that process – often by years.
Nowhere is this more apparent than in human-centred businesses like pharma and healthcare, where digital solutions have been called into action across every link in the value chain. The most obvious example of this is the shift to telehealth during the crisis – but it’s not the only one. Moderna drew heavily on AI/machine learning to devise its successful Covid-19 vaccine while most pharma firms have turned to digital solutions to support sales, marketing and new product launches. At Appnovation, one of our key contributions was the creation of a digital tool that enabled the Canadian province of British Colombia to map the movement of 100,000 healthcare workers; a solution that helped healthcare authorities minimise Covid’s spread in care homes.
On the face of it, these examples seem distinct. But the key thing that unites them is that they all depend on data analytics – the fuel that feeds digital transformation. It has become abundantly clear during Covid, if it wasn’t already, that an ability to distil actionable intelligence from data is now critical for companies going forward.
The good news for pharma is that it is sitting on a mass of data points, generated through billions of interactions with both supply chain partners and end users. While there are sector-specific issues relating to privacy and regulation, the fact remains that pharma has the raw materials at hand to pivot decisively in the direction of digital, thus becoming more nimble, adaptive and efficient than ever before.
The harder challenge, however, is that Covid-19 has forced firms to embark on digital transition quicker than they anticipated. As a result, some will feel that they are behind the curve and need to impose rapid cultural changes. Others will make snap investments that don't pan out. Either way, the important job of transformation could lead to pushback from employees and decision-makers within their companies.
In this highly-charged scenario, the key to tapping into data’s superpower potential is to manage it in a way that is smart and rooted in impact. Below are a few ways that pharma brands can harness the power of data to innovate through the turbulence:
Maximise pre-existing data: Data analytics has the power to revolutionise a company’s performance, but to some extent it is built on a pre-existing attribute – namely data that has been accumulated on the back of carefully nurtured relationships. So, rather than launching into digital product development from a blank slate, pharma brands looking to innovate should be on the lookout for pre-existing data archives that they can pivot from and build on, instead of starting from scratch. For example, the developers behind British Columbia’s healthcare mapping project used existing payroll data to build a rapid picture of the health department’s 1200 care sites – enabling them to balance resources across the system while limiting staff movement in the wake of Covid-19. With BC racing to get ahead of the Covid infection curve, payroll insights provided a quick route into identifying care home hot spots in need.
Simplify data gathering and interpretation: Alongside pre-existing data, it’s important to have mechanisms for generating new data in a clean, usable form. As a result, any digital solution requiring a large number of people to input their data must be very easy to use. Cloud architecture is key to building infrastructure at speed, and a streamlined user journey should be at the heart of product design – making it straightforward for multiple parties to upload their data. A key part of this process is not to stand still once a system has been built – but to keep iterating. In other words, it’s important to absorb lessons from the first release and feed those back into the process - to make it even more friction free for the people uploading data. It’s also crucial that data is converted into a form that surfaces easy to understand and immediately actionable insights. Data isn’t the preserve of data scientists, it needs to be delivered in a way that is digestible to all stakeholders. Tools such as Atlassian and Google Cloud can be really useful in this process, allowing users to understand data in a way that facilitates effective real-time decision-making.
Create impact maps around data: Pharma companies tend to have access to large archives of data but they’re not always aware of how to use these effectively; or else they attempt to manipulate data so as to develop vague concepts of things they think they “should” be doing. An impact map redresses the balance by redirecting the focus onto what needs to be achieved with data and identifying who the likely end user is, rather than jumping straight to a detached vision of the end product. The result of creating impact maps is a series of initiatives that have the potential to deliver meaningful outcomes based on audience needs. This, in turn, forms the foundation for a series of user stories and mini briefs to take forward into development.
Data analytics is also a valuable process for understanding the gap between the experience a company wants its end users to have versus the one they're actually having. It also unlocks insights into how they perceive a company compared to its competitors. So this can also feed into the creation of tools like impact maps.
Adopt a startup mentality: It’s not uncommon for companies to go for broke once they have become digital converts – calling in consultants to oversee a complete overhaul of culture, processes, departments etc. But data-driven digital innovation can be dauntingly complex – especially for firms with large headcounts and long-running legacy relationships. As a result, it’s crucial that companies break their projects down into small, bite-sized steps. Instead of trying to reinvent the wheel each time, they should use data to fail often and fast, and prioritise modular solutions that can, if successful, be scaled rapidly using cloud based architecture. Echoing a point made earlier, once a company overcomes its first major hurdle, it needs to keep iterating and interrogating, always scouring for ways to improve/expand its data for optimal agility.
Final Thought
Many companies have been forced to act faster than they would have liked – but what they’ve realised is that data-powered digital solutions are actually a viable and efficient replacement for what we have traditionally seen as irreplaceable physical interactions. For pharma firms, which are often one stage removed from the end user, this shift to the digital realm represents a genuine opportunity to establish rich new relationships.