Ready or not, here it comes. The transformative potential of data and marketing science has reached the healthcare industry and – according to what we learnt at our EMEA Marketing Science Summit in Zurich last Wednesday – we’d better shape up.

“Digital integration is the number one priority in healthcare,” came the resounding pronouncement from our keynote speaker, Dr Jamil El-Imad, Chief Scientist at NeuroPro.ch. It’s a sentiment that we certainly believe in.

So, in that light, what can take away from our 2019 Summit? Here are five key learnings.

  1. Connecting the Data Dots

Yes, data sources in the healthcare industry are siloed even within organizations, but that shouldn’t limit what you can achieve. Take these two innovative examples discussed at the event:

The trailblazing Swiss Brain Data Bank (SBDB) is accelerating scientific progress by enabling researchers to collaborate across digital databanks to improve diagnosis and treatment of brain disorders.

VirtaMed is transforming medical education with ‘flight simulators’ for surgeons. These simulators allow surgeons to train in a controlled, virtual environment and receive data-evidenced performance feedback. 

  1. Measure Selectively and Manage Expectations

With so much data, it’s tempting to ‘measure everything’. But this can be hugely counterproductive: it’s best to focus on actions and outcomes. Take these learnings from sport, for instance. Mo Wootten, a Sport Intelligence Analyst at UK Sports, discussed what they found from collecting sleep data. Not only was it not always useful to athletes, who already know if they don’t sleep as well before a major competition, but for some people it can be detrimental and a source of anxiety.

Jörg Corsten, Digital Medical Engagement Principal at Roche, warned against exaggerated expectations of AI and machine learning, reminding us that “we don’t have a general AI –nowhere we can just put in all the data and out comes the right answer.” Put another way, AI today resembles Siri – good at solving task-based problems – rather than HAL, the sentient AI from 2001: Space Odyssey.

  1. Acknowledge Your Bias

Our experiences and beliefs create unconscious biases, which, in turn, shape the way we see the world. We need to remain alert to potential bias in our datasets: metrics, tools and algorithms are created by people, so are vulnerable to the same foibles and biases that affect us.

Mo Wootten shared an example from the US criminal justice system, which integrates data models to predict the likelihood of re-offending. Tellingly, the algorithms used were shown to be racially biased.

The (big) data we have access to today can help us mitigate both problems: by triangulating from different data sources and perspectives – and consciously acknowledging our own cognitive biases – we can arrive at powerful insights.

  1. Blend Data and Storytelling – but Keep it Simple

Data-driven storytelling has the power to cut through information overload. Underpinning our stories with the right data creates relevance, but it is no easy feat. As Sam Knowles, Founder and Managing Director of Insight Agents, put it: “meaningfully bringing together the fire-and-ice worlds of storytelling, data and science is an art form.”

During the summit, Knowles shared his rules for compelling, data-driven storytelling as outlined in his book Narrative by Numbers. He highlighted that it is the combination of narrative skill and intelligent interrogation of data sources that creates real impact and, ultimately, influence.

So how do we do this? By keeping our story simple, being selective with our data and tapping into emotion.

  1. Get Ready for What’s Next

Digital data is increasingly fundamental to healthcare decision-making. In medical training, for instance, some European countries already embed digital performance data generated by virtual practical tests into certification and continuous education.

But not everyone is ready to embrace our data-rich environment. For some, the integration of real-world data helps to optimise healthcare (e.g. in disease registries) but others perceive it as challenging ‘clean’ (and controllable) clinical trial data.

Data privacy is not going to go away. Leaders in our field will be those who see the upside, rather than the challenge. There is an opportunity cost of doing nothing and falling behind.

Thinking this way, we may then find there are digital solutions already doing things that seem at first impossible, like reading facial expressions without compromising anonymity and data protection. This kind of solution already exists: it is used by the SBDB to conform with data privacy legislation while enabling ground-breaking, collaborative research at scale.

The 2019 Zurich Summit illustrated that it will take creativity and courage to make the most of our data-rich environment. It is when human brains and digital data connect to tell meaningful stories that strategy-changing insights can be unlocked. As one speaker noted, “data is a servant to decision-making, it should never be the master”.