Dark Analytics: Shining a Torch for Pharma

Dark Analytics: Shining a Torch for Pharma


Dark analytics can unlock a wealth of new clinical and commercial insights—but how can you make it work for you?

Dark data and dark analytics have the potential to generate huge business insights, but much of this unstructured data is currently unknown or untapped within the pharma industry. Most pharma companies are yet to see the potential of dark analytics let alone understand the technologies and skills necessary to access it. Dark analytics can contribute across pharma's business with applications ranging from improving patient adherence, remote diagnostics and health outcomes to building evidence, understanding HCP prescribing behaviour and refining sales strategies.

In Dark Analytics: Shining a torch for pharma we interviewed leading experts to help you really understand dark analytics, identify how and where it will have benefits and what you need to practically do to unleash the power of data analytics in your business.

Experts explore the dark analytics landscape

Which commercial and clinical areas of pharma's business will benefit most from dark analytics?

How can dark analytics reveal a deeper understanding of HCP and patient behaviour?

Will dark analytics techniques complement or challenge traditional market and clinical assessment models?

What type of data can be mined from social media and online communities and what type of analytics is needed to gain commercial insights?

What skills, capabilities and functions are necessary for pharma to tap into dark data and analytics?

Where does machine learning and artificial intelligence (AI) fit into dark analytics and what tools or techniques is needed to analyse data in the deep web?

What are the cultural, regulatory and business leadership challenges that could frustrate the development of dark analytics?

What dark analytics experts say…

The implications of dark data for pharma are profound. So many obvious decisions made by commercial leaders are based on traditional ways of working or long-term assumptions due to the fast pace of the industry, especially in the commercial cycle. However, the implications are not so obvious that we see thoughtful, analytical approaches that thoroughly challenge assumptions.

PA Dark Data Team

For commercial insights, you can use doctor data to model prescription behaviour. You can record longitudinal data from a cohort of doctors on their prescribing and other behaviour. Then you can predict whether an individual doctor will switch away from a specific drug to another one. You can then personalise the sales methods for that doctor at that point in time. This can be very effective, at least in our case, where we've experienced a 43 percent increase in sales using this predictive model.

Dr Andree Bates

Information strategy is critical to overcome barriers in dark data. You must know the data, understand the value of the data, create the governance to freely use data where it is needed, and then apply the right level of resourcing to the data. If pharma companies are able to develop this strategy, the future reward is gaining three to 10 times more than their current market capitalisation.

Herman Heyns

What to expect

A detailed report exploring the rapidly-developing world of dark data and the opportunities and challenges pharma must overcome if dark analytics is to accelerate strategically vital areas of their business

An examination of 7 key issues that pharma needs to understand and respond to

23 targeted questions put to dark data analytics experts

Their perceptive responses that yielded 41 current insights supported by 52 directly quoted comments

Expert contributors

The report harnesses critical insights from knowledgeable experts who completely understand the current and future potential of dark data and dark analytics.

Dr Andree Bates is the CEO of Eularis, a company that specialises in using artificial intelligence algorithms and analytics to solve strategic and marketing problems throughout the pharmaceutical lifecycle.

Herman Heyns is the CEO of Anmut, a world leader in DataActivism, helping organisations understand the value of data and build better information strategies for the delivery of value to stakeholders.

PA Dark Data Team represented by Dr Shaibal Roy, Managing Consultant and Dark Data Lead, Martin Knoebel, and Michael Carver. PA's Dark Data team specialises in helping companies utilise advanced analytics with dark data sources such as open social media to deliver tangible business value and drive hard outcomes.

What is dark data/analytics anyway?

Dark data can be any information that organisations collect, process and store as well as online videos, forum chats, content in anonymous websites and other data in the depths of the web. Dark data is raw and unstructured and requires the latest AI and data analysis tools to investigate it. Dark analytics is the general term for transformational technologies that can mine dark data to reveal strategically and tactically important clinical and commercial insights.

Why choose FirstWord FutureViews reports?

FirstWord's FutureViews reports analyse in detail significant emerging technology and market trends that pharma executives need to understand if they are to manage the opportunities and challenges that lay ahead. These concise and highly focussed reports:

Are based on primary research with experts whose knowledge and current experience is proven

Present clear expert insights free from secondary source information and spurious observations

Include only latest research and content–we don't reuse or recycle content

1. Subject synopsis
1.1 Sources
2. Research methodology and objectives
2.1 Which experts were interviewed and why?
3. Key insights summary
4. Issues and insights
4.1 Appraising pharma’s maturity in dark analytics
4.1.1 Issue summary
4.1.2 Questions
4.1.3 Key insights
4.1.4 Supporting quotes
4.1.5 Intelligence exhibits
4.1.6 Sources
4.2 Dark analytics for idle data
4.2.1 Issue summary
4.2.2 Question
4.2.3 Key insights
4.2.4 Supporting quotes
4.2.5 Sources
4.3 Dark analytics to maximise the value of the deep dark web
4.3.1 Issue summary
4.3.2 Questions
4.3.3 Key insights
4.3.4 Supporting quotes
4.3.5 Intelligence exhibits
4.3.6 Sources
4.4 Dark analytics technology, tools and techniques
4.4.1 Issue summary
4.4.2 Questions
4.4.3 Key insights
4.4.4 Supporting quotes
4.4.5 Intelligence exhibits
4.4.6 Sources
4.5 The utility of dark analytics for insight generation in the real world
4.5.1 Issue summary
4.5.2 Questions
4.5.3 Key insights
4.5.4 Supporting quotes
4.5.5 Intelligence exhibits
4.5.6 Sources
4.6 Identifying and overcoming barriers to dark analytics
4.6.1 Issue summary
4.6.2 Questions
4.6.3 Key insights
4.6.4 Supporting quotes
4.6.5 Intelligence exhibits
4.6.6 Sources
4.7 Navigating the future of dark analytics
4.7.1 Issue summary
4.7.2 Questions
4.7.3 Key insights
4.7.4 Supporting quotes
4.7.5 Sources

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