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Part 2- Strategic communication in a volatile world


In part 2 of this 3 part series, data and insight lead Thomas Stoeckle from Dot I/O Health discusses the data analytics process which incorporates AI based data analysis tools and design thinking to assist with strategy in the Life Sciences, Pharma and Healthcare sectors.

Data Acquisition

Every data-driven process of insight creation starts with acquiring data. But today it is less and less about the ability to collect the data, and more about connecting the dots among the never-ending proliferation of data, from any place, in any form.

In strategic communication, it usually starts with content and media channels. Today, a pragmatic and agnostic approach to sourcing content reflects an ongoing, dynamic shift in content acquisition and distribution. It is good practice to utilise online content collection and analysis tools that allow both to apply the latest innovations in artificial intelligence and machine-learning.

It is critical never to stop asking questions: are we looking at what was, or is? Or are we anticipating what might happen? Are we analysing online content in the relevant languages? Which stakeholder groups demand attention? Who and what influences those stakeholder groups? What larger trends or events can impact our model?


Whilst the first instinct is to look externally — what is the world outside thinking and saying about us? — it is equally important to be on top of internal communication. An internal data audit can be critically revealing, for example if you are working within a large pharmaceutical organisation with complex communication structures. In a world of ever expanding data volumes, there simply is not enough time to have eyes and ears everywhere. Think about the amount of market reports or internal decks which reference other data sets, that pass through your inbox on a weekly basis? (In later articles we will look more specifically at making your internal data work harder for you).

Whether external or internal data acquisition or a combination of both — the trick is connecting the dots in meaningful ways and creating an ever richer ‘data mosaic’.

For the unique purposes of a ‘forensic analytics’ approach to help clients connect dots and make better decisions in uncertain times, we have condensed the journey from content and data to impact into four major stages. Each stage requires technology and ‘machine power’, but it ultimately depends on the quality of human oversight, and insight.


It uses planning tools such as the logic models developed by the W.K. Kellogg Foundation. A logic model, as per the introduction on the website, is “a graphic which represents the theory of how an intervention produces its outcomes. It represents, in a simplified way, a hypothesis or ‘theory of change’ about how an intervention works”. This may sound complex, but it is essentially just a visualisation of the flow of decision steps and inputs from the beginning to the end of a process.

Essential to such an approach is the active involvement of the client from the outset. It is the only way to ensure the shared understanding of business objectives that is critical for a successful partnership. From that understanding, an analytics team builds the data collection and analysis workflows that will yield the insights which inform client decision-making: for better campaigns, better products, better processes.

1.Strategic Data Analytics with Design

During the planning phase, our consultants hold several briefing sessions to formulate an action plan based on the client’s business objectives. A shared understanding of those objectives is necessary to design and conduct an analytics process that generate the rich results which inform accurate anticipation of relevant business trends, and enable the creation of unique, relevant content.

During this phase, we conduct preliminary research which focuses on breadth and context. This is shared with the client as part of the briefing process. This helps us ask the right questions to ensure answers that are required to optimise outcomes. This includes scope, breadth and depth of the analytics (for example, markets, languages, source types, topic focus etc).

To conclude this stage, we lean on design thinking, where we map out the market ecosystem (see image below) and it’s dependencies based on our current understanding using a human centred approach. Our initial data is added to the blueprint, but at this stage there will be many gaps in our ‘mosaic’ to fill. This is good because it helps identify gaps and identify the types of analytics and data required.

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The image above shows an example of the blueprints we create to map a market ecosystem using data analytics, visualisation and design thinking principles- which take a human (or patient) centred approach. This tool is an important cog in our data analytic process, allowing us to simultaneously examine detail and the bigger context as we solve strategic challenges.

2.Production — Building the Mosaic

In the production phase, content is collected from a broad range of sources, depending on the specific brief (this can include traditional print media content, broadcast content, online text, image, video content, and publications). We work with the latest technological advances in content acquisition, processing and analysis. In all these processes, we follow the principle of using as much automation and machine power as possible, and as little human oversight as necessary. However, high quality standards are ultimately based on human judgment.

3.Knowledge — Insight to Anticipation

Data analytics processes fall into the three categories of descriptive (what happened), predictive (what will happen), and prescriptive analysis (how can we influence what will happen). These categories come together in Anticipation. This is not about ‘data Olympics’, but about being practical, useful and usable: for example, regular updates on how trends are forming and evolving (this can focus on a number of environmental or stakeholder contexts, including market environment, legislative environment, political environment, socio-cultural environment). Research focused on Anticipation provides the right insight at the right time to inform good decision-making.

4.Creation, Impact and Iterate

The modern data driven consultancy generates concrete, practical outputs in the form of specific strategy, messaging and positioning, as well as new solutions.

Interventions are designed to recode the current ecosystem. If we come back to our blueprint, we can now insert our intervention and continually measure the intended (and unintended) impact behaviours within our model.

The final step is to refine on an ongoing basis. The aim must always be too iterate in a collaborative way with clients to maximise performance.

Read Part 1 here or visit for more information. Dot I/O Health is a data analytics firm providing next generation data science driven market research and strategic advisory for the pharmaceutical and health industry.


Macnamara, J., 2018. Evaluating Public Communication. Exploring new models, standards and best practice. Abingdon, Oxon: Routledge.

Macnamara, J. and Gregory, A., 2018. Expanding Evaluation to Progress Strategic Communication: Beyond Message Tracking to Open Listening. International Journal of Strategic Communication, 12 (4), 469–486.

van Ruler, B., 2015. Agile public relations planning: The Reflective Communication Scrum. Public Relations Review, 41 (2), 187–194.


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Next generation data science driven market research and strategy for the pharmaceutical and health industry.

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