Beyond drug development: Four data science use cases helping life sciences organisations stay ahead of rapid change

The life sciences industry is evolving faster than ever. We explore why organisations need to revisit their data science strategies, and the use cases they must embrace to stay ahead of competition.

Life science and data science go hand in hand. From the earliest stages of drug and therapy discovery, through to the multiple rounds of rigorous clinical and in-silico testing, life sciences organisations’ abilities to derive and act on data insights are key to their commercial success.

But, to stay competitive and rise to the top of this incredibly fast-moving industry, no life sciences company can afford to stand still. Teams need to ensure that they’re constantly utilising the right capabilities in the right way to get the most from their data, and applying those capabilities to the most impactful data science use cases.

With the life sciences and healthcare landscapes evolving faster than ever, here are four areas that every organisation should take another look at today – whether that’s doing more with existing strategies or expanding into new use cases.

#1) Enabling excellence in information management

Life sciences organisations manage and gather huge volumes of data across their operations. Much of that data is gathered for a single specific purpose, such as recording research and testing processes, or ensuring compliance with evolving international regulations. And as a result, it gets trapped in organisational silos.

That’s a huge missed opportunity, because most of those data sets have value far beyond their original intended purpose. But, by combining data science and data engineering, life sciences organisations can overcome major challenges like low data quality or a lack of standardisation and bring diverse data sets together in one place to be visualised, understood and acted upon in new contexts.

For example, by building intuitive visual dashboards, operational data that’s traditionally used for reporting can be turned into live process insights that teams can use to achieve operational excellence and improve process efficiency. 

Data engineering also helps teams set clear rules for data governance, and automate the way data moves within their organisation. They can create reporting frameworks to automatically report on new data as it’s created, automate the ingestion of new data sources, and even validate data quality automatically as it moves through data pipelines.

By applying data science techniques and data engineering to organisational information management, leaders can enhance the efficacy and value of virtually any data-driven process or operation across their organisation. In turn, that enables them to accelerate discovery, improve drug quality, and do more with less – all while maintaining compliance and patient privacy, and achieving excellence in medical data management.

#2) Increasing sales and driving commercial growth

Developing profitable drugs isn’t easy. And even when you develop a unique or niche product, your work is far from over. In today’s leading life sciences organisations, sales decisions are just as data-driven as drug discovery processes – ensuring that their choices post production deliver maximum ROI and commercial impact.

Leading pharma giants embrace three key data science use cases to build a brand and enhance sales

  • Dynamic brand forecasting: Data science can help organisations forecast sales and demand with remarkable accuracy. By modelling the impacts of current and emerging trends, teams can choose the right sales strategies for their drugs and therapies, focusing on the right customers and the right target areas, at the right time, to meet and exceed their forecasted sales and growth.
  • Customer and physician insights: To sell effectively, organisations need to constantly understand physician requirements, prescription behaviour and customer sentiment around their drugs and therapies. Data science can help teams analyse factors like physician churn, patient sentiment, and even patient adherence to proactively identify and resolve customer issues, and maximise their lifetime value to the brand.
  • Competitor intelligence: Strong sales strategies are built on a deep understanding of competitor actions and positioning. Data science and analytics can help life sciences organisations assess competitive threats and routes to market against competitors, better understand their own marketing positioning and identify white spaces to maintain/strengthen competitive advantage.

#3) Looking outwards to optimise marketing

Effective drug marketing takes more than just an effective product. It demands a deep understanding of physician and patient needs, competitive positioning and market opportunities. That’s where data science can help life sciences organisations develop a clear picture of all three – enabling them to build highly effective campaigns and make marketing decisions that consistently deliver high ROI.

Data science and analytics enable leading life sciences organisations to:

  • Model marketing mix decisions and use data to forecast the impact of marketing decisions before they make them and choose strategies that deliver the highest returns.
  • Optimise marketing spend across channels and see which ones are generating the greatest ROI, and should be allocated a higher proportion of their budgets.
  • Automatically identify next best actions for all customers and prospects, and optimise omnichannel customer journeys.
  • Build deep customer insights and discover what customers want at a granular level, enabling the continuous optimisation of marketing efforts and continuous product improvement upstream.
  • Understand physician prescribing behaviour and pinpoint how, where, and why physicians are – or aren’t – prescribing their drugs to patients.
  • Gain a clear picture of brand positioning, see how customers and competitors perceive their brand, and see which influencers are shifting perceptions of their brand in the right direction.

#4) Mastering patient analytics

As trends like personalised medicine and telehealth grow, it’s more important than ever for life sciences organisations to understand patients at a granular level. That means collecting as much data as possible related to their health journeys, and using data science to operationalise it in valuable, responsible ways.

For many organisations, the biggest challenge with patient data is ingestion. Gathering it at scale isn’t easy. But with the right data engineering capabilities, the entire collection and analysis process can be automated and digitised. Organisations can build automated patient data pipelines that continuously ingest patient data, and turn it into valuable analytics that teams can use to:

  • Better understand and map patient journeys and discover where their drugs can best fit into them.
  • Identify key influencers in their market and build relationships with them to drive sales and clinician uptake.
  • Segment patients in granular detail based on a range of characteristics.
  • Establish a clear view of patient lifetime value along with the factors that drive it
  • Understand patient adherence patterns and identify opportunities for changes/improvements.

Equipped with that insight, leading life sciences companies can improve the effectiveness of their go-to-market strategies, increase sales, and put individual patients at the core of their development processes – preparing them for the personalised, patient-driven future of drug delivery.

Analytics Centres of Excellence put data at the core of all your operations

There is a huge range of established and emerging data science and analytics use cases that can help life sciences organisations increase commercial performance and maximise the value of their drug portfolio. But to find and embrace those use cases effectively, organisations must become masters of their data.

To do that, many teams have built centralised Analytics Centres of Excellence (ACoEs). ACoEs work constantly to help the organisation apply and utilise its data in new ways, to drive growth and help the brand achieve its strategic objectives.

As emerging trends like telemedicine, AI-driven disease research and diagnosis, and personalised medicine redefine the life sciences industry, these ACoEs will be more important and impactful than ever. By helping their organisation understand patients, physicians and markets at a granular level, they will underpin the successful development and delivery of precision medicines and help their brand rise to the top of this evolving field.

The Smart Cube has helped life sciences organisations build and operate effective ACoEs for many years. We provide proven models, continuous streams of intelligence, and expert resources to help organisations support all of their teams with valuable and reliable analytics insights.

To learn more about our ACoE solution, and discover how we could help you establish your own – or augment one you’ve already built – visit our website, or talk to us today.

  • Dripti Banga

    Dripti is an analytics professional with over 5 years of experience. In her current role, she is responsible for designing and developing research- and analytics-led solutions for Retail, Life sciences and Consumer goods. She is passionate about using data effectively to uncover insights and recommendations, enabling data-driven decision-making. Outside of work, she likes to travel and try different cuisines. 

  • Dripti Banga

    Dripti is an analytics professional with over 5 years of experience. In her current role, she is responsible for designing and developing research- and analytics-led solutions for Retail, Life sciences and Consumer goods. She is passionate about using data effectively to uncover insights and recommendations, enabling data-driven decision-making. Outside of work, she likes to travel and try different cuisines.