How Can Life Sciences Companies Organize Their Data to Achieve Optimal Outcomes?

Life sciences companies today are flooded with connected platforms, sensors, and devices that stream several zettabytes of data. This interconnectedness in the patient-centric environment has made the transition to a data-driven enterprise a complex journey for any organization operating in the life sciences industry.

What life sciences organizations need to do is consider themselves insight-driven systems instead of siloed departments that operate linearly. Given that the business objectives of most companies in the industry are driven towards the pursuit of precision medicine and high throughput genetically engineered therapies, there is naturally going to be an immense diversity of data that companies need to deal with.

To alleviate this issue, the new generation data-centric life sciences enterprise must rethink how it handles data. The companies can do this by reorganizing their large collections of information into four specific types of datasets to deliver value in an increasingly patient-centric life sciences ecosystem:

1. High-throughput biology data
   
   Grouping together data that encompass and deliver a systems view of the disease portfolios as part of their larger strategy is one of the best ways to organize large collections of life sciences information. Data from high-throughput biology systems include pharmacological, “omics” data, bioinformatic data, interactome data, pharmacogenomic data, biochemical data, and other subvarieties of datasets.
   
   It is important that the diversity of data also includes deidentified patient population data. The universe of datasets can then enable a systems approach to therapeutic development over the reductionist “one therapy-one disease-one gene” approach.


2. Clinical and regulatory data
   
   Clinical and regulatory data form the bedrock of success for all life sciences organizations. However, the distribution of this type of data today is skewed, causing much disarray and complexity in clinical trial procedures.
   
   The focus on capturing clinical and regulatory data extends beyond merely putting together the varied formats of clinical data into the design and management of the clinical trial databases and registries supported by data validation protocols and risk-based quality management. The presence of an integrated data ecosystem around clinical and regulatory data is essential to harness clinical data for upstream and downstream data analysis properly.


3. Core pharma operations/value stream data
   
   The scope of pharmaceutical and life sciences process operations is rapidly changing, causing a shift in how life sciences companies look at data coming from process flows. The capture of process flow data across R&D, manufacturing, and supply chain should largely depend on the organization's overall strategy and what it wants to get out of that data.
   
   This can be defined by the outcomes it predicts and the aberrations it chooses to prevent, depending upon the broader business KPIs for the life sciences organization. These goals are typically centered around improving overall equipment efficiency, reducing costs of R&D, and aligning with lean management principles for manufacturing and supply chains.
   
   
4. Marketing and physician engagement data
   
   Capturing physician engagement data through sales interactions with physicians and core marketing and sales performance data across a portfolio of therapeutic products is foundational to delivering insights on marketing and sales effectiveness.
   
   Traditional approaches to addressing new customer segments by the pharmaceutical and life sciences industry, such as spending as much as $15 billion per year on advertising, have been challenged by a lack of personalization and poor patient and physician engagement outcomes.
   
   To improve marketing results, life sciences companies need to understand patient engagement behaviors across the spheres of medication compliance, treatment outcomes, engagement with their healthcare providers, and the confidence of physicians and patients in the effectiveness of medications. Organizations must also get a deeper understanding of how patients engage and manage disease.
   
   This can all be achieved if life sciences companies view marketing and physician engagement data beyond the capture of inputs on CRM platforms or competitor insights and shift towards real-world evidence data gathering.
   
   It is also essential for life sciences companies to transform into a concierge for physicians and patient groups engaging with the medication portfolio to capture real-time inputs on performance effectiveness, pricing, and other supportive dimensions.

What business are we in?

How organizations choose which datasets and data types to prioritize and pool together should be based on the central definition and question posited by Theodore Levitt in his landmark paper Marketing Myopia: “What business are we in?” Traditional life sciences companies will probably have an answer suggesting they are in the drug development business.

The reality for most life sciences companies today is that they are in the business of delivering wellness and preventive health, as opposed to the traditional approach of delivering symptomatic reactive cures against diseases. The positioning of life sciences companies as stewards of predictive and preventive care brings to the fore the second principle of an effective data strategy: making data actionable and predictive.

In my next blog post, I will discuss how the life sciences industry’s path toward preventive care necessitates an effective data strategy that makes data actionable and predictive. In the meantime, visit our Life Sciences practice to learn more about how you can maximize value from your investments with our cutting-edge services and solutions.