A new age of decision making in pharma

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This month in Second Opinion we’re taking on one of the biggest buzzwords in business: Data.

Take 100 leaders from every major industry in the economy and ask what they believe will be the biggest trend in the next decade, and almost all of them will say ‘data’ or ‘AI’. In fact if any of them don’t, we at Second Opinion would consider eating our shoes.

Certainly in the pharma world, data is set to be a crucial catalyst for commercial operations for the next five years. Keep reading to understand why it’s so important in this part of the value chain, and also how to move past the hype cloud to concrete next steps.

Information Is Power

There’s certainly no shortage of advisors and start-ups looking to explain or deliver first class data collection and analysis approaches, but from what we can tell not enough of them are asking pharma to consider the direction of this journey.

Why are you collecting this data? What do you want to be able to do with data, whether in terms of customer experience or clinical development? What kind of leverage are you expecting from big data? Where exactly can it take you?

The answers to these questions are often incredibly unclear in commercial operations. Keep reading though, and we’ll see if we can help with that.

Data in R&D is exciting, but not the whole picture

When we picture data or AI in the healthcare industry, what do we think about? Drug discovery models? Care pathway optimisation? Though it may not be as evocative a headline as R&D, the use of data to drive efficiency in the operations for launched medicines is an opportunity that many industry leaders are at risk of missing out on.

Why aren't more people talking about this?

In a recent survey of Life Sciences industry professionals, GlobalData revealed that only 27% of respondents see big data as having a role to play in optimising marketing and sales. That percentage seems shockingly low and suggests that the true potential of data has not been fully understood by commercial operations in global pharma.

Plans built on data-driven understanding, not hope

Transforming operations around data allows for an increase in information, agility and feedback around decision-making. So, what does this future look like?

The portfolio of commercial activities will be allocated to maximise value of outcomes, instead of efficiency of inputs.

Spend on commercial activities will be allocated based on known effectiveness of activities and not anticipated outputs.

The content of engagements with HCPs will be informed by what you know they are looking for, rather than predictions.

This doesn’t have to be AI, where machines make decisions automatically, but rather through using systems and analysed data to empower your teams to deliver success in their markets.

If you want to understand this in greater detail, read our piece here, which explains how data-driven decision-making is like telling your kids to buy milk (bear with him on that).

A Creeping Realisation

As understanding of the importance of data in competitive organisations increases, so does the proportion of industry leaders waking up to its necessity. Forrester data from 2018 shows that while only 29% of cross-industry leaders saw data and analytics as having a greater than 10% impact on the bottom line, 49% expected it to deliver that impact within two or three years.

For businesses taking on big data projects, a research study by BARC group found that the impacts on bottom and top lines can be significant – averaging a 13% increase in revenue and 16% reduction in costs.

The study caveats that only 10% of respondents (n=32) were able to perform this analysis, so there may be some gaps in the results, but the key finding remains – transformations built around data both make and save money for companies. It also shows that the majority are still on a journey to getting this right.

What Else We've Been Reading

Walking the tightrope of data-centric transformation

This article by McKinsey looks at the use of data in Life Sciences across the value chain, and provides a succinct set of guidelines on how to embed effective and holistic analytics capabilities within your organisation.

While overly generalised for the most part, these guidelines do highlight the delicate nature of building data into your organisation. If this change is undertaken with too much focus on systems and processes, there’s a risk of losing both colleague engagement and alignment with the overall vision, resulting in a transformation that’s not compatible with your company as a whole.

On the flipside, if your change doesn’t adhere to the detail required to make sure the mechanics work robustly and consistently, teams may become disenfranchised with the lack of realised outcomes. This can harm future initiatives to deliver data-centric transformation.

These guidelines might help you walk that tightrope, but they’re certainly not a definitive blueprint for transformation – for that you need to look more closely at the objectives and desired outcomes of your organisation.

Decision-taking before decision-making

A key part of building data into your governance is decision-making. Who makes decisions, with what data, at what time, and in which forums?

An important centrepiece for this subject is the relationship between data and decision-making, and how they feed one another. What we mean by this is acknowledging that the decision-making process can often be hampered by making decisions as we take them.

So often in business, there are scenarios that result in decision-paralysis which, combined with limited available intelligence, result in “best we can do in the time we have” decision-making. Understanding that data and analysis can be used to make decisions before they need to be taken, speeds up the decision-taking process significantly, and supports a more agile and informed approach to governance.

Decision architecture is a complicated subject with numerous angles to explore, but the important message is this – if you move to a data-centric approach to commercial operations, you can consider how decisions will be made using the data before it hits your spreadsheet and urgent decisions need to be taken.

Ultimately, however, this subject is all about humans making decisions. In our experience the biggest challenge that companies face are capabilities and understanding, not the quality of the data. The more informed your people are, the better decisions they will be able to make. Make sure that you know this and are able to effectively leverage the rapidly growing available data in healthcare. It will put you one step ahead.

If you have any feedback, or want to hear more about anything mentioned in this article, please don’t hesitate to get in touch!