The practice of analytics in life sciences

Gone are the days when people used to walk into hospitals only when they fell ill. Today’s ‘smart’ customers have health information available at their fingertips. They are looking at expert advice and proactive care from the health industry.

Life sciences companies are trying to keep pace with the customer. They are moving from treatment to preventive scenarios. But are they doing enough? How can they use the vital patient information available to them to the best of their advantage to manage patients’ health outcomes? Just like the other industries, it is time that the life sciences companies adopt data analytics and leverage the insights strategically to their advantage.

Analytics is playing a key role in helping life sciences industry manage the rapidly changing environment and better manage the challenges. Analytical solutions have grown tremendously over the last decade, specifically, in terms of their sophistication and the resulting business impact they create. There is a wide range of analytics solutions being deployed in life sciences industry. While basic reporting continues to be a must-have, advanced, predictive and prescriptive analytics are now starting to generate powerful insights.

  1. Reporting. The most basic version of analytics solution that focuses on building data repositories and reporting the current situation using simple and uni- or bi-variate data. Typical examples in life sciences include adverse event reporting and PPSA-based reporting
  2. Descriptive analytics. Generating actionable insights on the current situation using complex and multi-variate data. Typical examples in life sciences include marketing, Return on Investment (RoI) measurement, customer journey analysis, and customer satisfaction analysis
  3. Predictive analytics. Predicting the likely future outcome of events often leveraging structured and unstructured data from a variety of sources. Typical examples in life sciences include customer lifetime value analysis, revenue forecasting based on health outcomes, and prediction of adverse event occurrence
  4. Prescriptive analytics. Prescribing action items required to deal with predicted future events using data from a variety of sources. Often associated with simulations in various business scenarios. Typical examples and life sciences include Electronic Health Records (EHR) analysis for insights into early-stage drug development, marketing strategy planning, and guidance to medical practitioners on the best medical procedure/approach

The application of life sciences analytics falls into three key areas:

  1. Regulatory compliance / internal reporting
  2. Marketing/sales support
  3. Product/service enhancement

Challenges in analytics in life sciences

The potential represented by analytics also comes with significant challenges, particularly around strategy, governance, and timeliness.


Solutions must support the key functions necessary for processing the data. The criteria for evaluation may include availability, ease of use, scalability, ability to manipulate data at various levels of granularity, ability to analyze data without IT intervention and with the users’ preferred tools of choice, privacy and security enablement, quality assurance, and transparency. The data management strategy should identify an organization’s pain points and address them through disciplined yet agile phased execution. The result should be a timely and cost-effective strategic approach that provides incremental business benefits at the conclusion of each phase.


The important managerial issues of data stewardship and data quality have to be considered and woven through an organization’s continuous data acquisition and data cleansing. Life sciences and healthcare data is rarely standardized and is often fragmented or generated in legacy IT systems with incompatible formats. Without a well-developed governance program and robust operations, organizations struggle with inaccurate and poor-quality data, leading to untrustworthy results and decisions. Organizations need to develop the tools necessary to effectively and confidently manage their data assets in specific information environments.

Real-Time Analytics

Finally, to ensure the most current and applicable insights, real-time analytics is a key requirement in life sciences and healthcare. The lag between data collection and processing has to be addressed. Also, the dynamic availability of numerous analytics algorithms, models, and methods is necessary for large-scale adoption. Organizations need to implement delivery tools and technologies that not only seamlessly interface with big data platforms, but also drive real-time data analytics.

The benefits of analytics in life sciences are manifested in significant areas such as early detection of prescription and treatment patterns, strategizing the intent of the patient to real world results and most importantly achieving the operational excellence to drive through the intellectual journey of patient centricity.

The pharma world needs to transform today’s health system to reduce healthcare costs, improve patient outcomes and enable access to health information. This requires that organizations transform from being traditional ‘pharma players’ to ‘health players’. The smallest change in one area has a cascading effect through the entire health system. Therefore organizations must embrace the potential of signal, detection and prediction enabled by technology.


Jun 02nd, 2018