Tunnah’s musings: Does pharma want big data or good data?

This month, Paul Tunnah muses on the role for data in underpinning vital commercial operations for pharma companies and the dangers of pursuing big data over quality data, after reviewing some recent industry survey findings from Cegedim.

Let’s be honest – we can be quite a nerdy bunch sometimes, us pharma folk. We love data and it seems to underpin everything we do as an industry. We often dismiss the touchy-feely ‘gut instinct’ that drives decisions in other sectors; instead favouring evidence-based, peer-reviewed, number-crunching modelled return-on-investment analysis.

So at a time when everyone is talking about big data, surely it will become an integral part of strategic and tactical decision-making for pharma companies?

“Surely it will become an integral part of strategic and tactical decision-making for pharma companies?”

Looking at some of the results from the newly released Cegedim ‘Key Perspectives on Customer Databases‘ research it seems that the answer is ‘yes, but quality is more important than quantity’. The survey, which asked senior commercial industry execs how pharma is using data, the impact of data on their business, what is valued as important and how they are procuring data, generated some interesting results.

Data remains the backbone of commercial operations, with a majority of the respondents seeing it is as important in customer targeting (90%) and territory management / investment allocation (81%), although interestingly it seems to be lagging as a useful driver of customer access, with less than half flagging it here, perhaps due to lack of available information on customer engagement preferences.

However, the really interesting bit of the survey was around what factors these executives valued as important for data to be useful. Data accuracy was by far the leader, with 82% of respondents citing it, whilst data completeness (44%) and coverage (58%) ranked some way behind. So it seems big data is no substitute for good data.

This is also driving which sources the industry views as the most reliable and how it is managing its data needs. The internet may well be the source of the big data revolution, but it is not viewed as a favourable source of information by these commercial pharma execs (figure 1) – a mere 17% viewed the internet as a good route for data validation, with webcrawling placed even further behind (12%).

Figure 1: Proportion of the Cegedim ‘Key perspectives on customer databases’ pharma respondents stating different factors as important in ensuring good data validation.

By contrast, despite our ability to connect online 24/7, good old fashioned phone verification – experts speaking to individuals to check the information held about them is correct – is still viewed as the most important data validation technique, with 63% citing it as important.

When considering the impact of ‘good’ or ‘bad’ data on a pharma company, the importance of accuracy becomes clear. Good data is seen as being primarily a driver of strong sales and marketing effectiveness, but the impact of bad data is much broader ranging. Not only can it be detrimental to commercial operations, leading to inefficient customer engagement, wasted resource and low motivation, but it also impacts way beyond sales and marketing. Of particular interest is the fact that 71% of the survey respondents saw bad data as impacting on corporate reputation, e.g. through ‘spamming’ customers who are not relevant.

“71% of the survey respondents saw bad data as impacting on corporate reputation”

This is perhaps more evidence of the blending of corporate and brand communications, with pharma realising that maintaining a good reputation, which is driven by appropriate engagement, is a key driver of continued access to the right customers. Knowing who to engage with and how to engage with them does, of course, come back to what you know about your customers from your data.

An interesting trend can also be observed in where the industry is procuring its data from and how it is storing it. Whilst historically, pharma has tended to favour building its own databases and maintaining control, over half (53%) of those responding to this latest survey do use outsourced database services. This figure looks set to increase too, with a greater emphasis for future investment placed on outsourcing over developing in-house data management solutions.

So what can we conclude from all this?

Well, it seems that the old adage of ‘never mind the quality, feel the width’ is not necessarily true when it comes to the data that drives critical commercial decisions in the pharma industry. Accuracy is the most important factor here, even at the expense of breadth of data, and the route to ensuring quality information is still seen as the good old-fashioned telephone, despite the noise being made by digiphiles.

“The route to ensuring quality information is still seen as the good old-fashioned telephone”

 

In addition, quality data forms the very underpinning of good engagement practice on both sides – for pharma it drives commercial efficiency, and for its customers, such as prescribers and payers, it means communication is more focussed around what is relevant to them.

So my gut feel is that the buzz around big data is not going to die down any time soon, but we will start to see more scrutiny of big data quality. And that conclusion has got to be right, because it’s based on peer-reviewed, evidence-based survey data.

Until next month, keep number crunching and stay well.

Download the full report, ‘EU Life Sciences 2013: Key Perspectives on Customer Databases’

 

 

About the author:

Paul Tunnah is CEO & Founder of pharmaphorum media, which provides digital content marketing and communications solutions for the pharma sector and also manages the industry leading channel www.pharmaphorum.com, a digital podium for communicating thought leadership and innovation within pharma. For queries he can be reached through the site contact form or on Twitter @pharmaphorum.

Is big data more important than accurate data?