What is the next disruption in life sciences analytics delivery?

Despite advances in data management, reporting & analytics technologies, current data analytics approaches in the life sciences industry tend to be deployed under a centrally provisioned model managed by IT/BI teams. These tend to be largely driven by a resource intensive framework and often result in a set of rigid capabilities. While this make sense from a pure IT perspective in terms of infrastructure management, it relegates business users’ needs and demands as an afterthought. With such approaches, business units end up deploying their own custom solutions for data discovery, reporting & analytical applications resulting in a cluttered and incoherent set of capabilities. While this approach may support the business needs in the short term, it presents long term challenges for organizations in terms of scalability, costs and skill development.

Data Analytics Value Chain

So, how do we solve this? As indicated in our previous blog, the solution can only be attained through a strategic convergence of the trinity- IT, Business & Analytics teams. Components of a successful solution in our view are as follows

  • A flexible data management framework: An integrated data management system with pre-bridged connectors to structured and unstructured data sources relevant to the industry is a key requirement to accelerate data analytics deployments. The architecture should provide limitless flexibility in managing data variability & volume as the data structures are subject to periodic change by the vendors. This data management infrastructure should come with capabilities to scale continuously without compromising on data storage & security, recoverability and governance. An ideal system should deliver all capabilities mentioned above at a cost basis that returns a favorable ROI in the long run
  • Analytics modules specific to the industry: Another missing piece today is the readiness of systems to deliver industry specific modules out of the box. The true measure of value of a data analytics installation is in the impact it generates through decisions it powers. In the current setup, life sciences organizations come across two kinds of solution providers- 1) An IT provider with no real understanding of the business use cases and 2) Subject matter expert/ Consultants who don’t have capabilities to implement their recommendations. Building ready modules that has baked in the core principles of the industry & functional idiosyncrasies through pre-defined libraries of KPIs, models and visualizations addresses this gap and benefits business users immensely
  • Ability to collaborate & seek broader input while building predictive tools: One important aspect missing in current set of tools & approaches is the absence of collaboration. The “So-what?” question remains unanswered as stakeholders are unable to collaboratively convert insights into actions and track those to completion. Any integrated analytics system should ensure there is a collaboration mechanism when consuming insights. Ability to set and track concrete action plans are sure to drive adoptability and promote meaningful use of analytics systems.

Such an integrated approach can result in many benefits for all the stakeholders involved. From an IT perspective, it optimizes the overall cost of infrastructure needed and empowers them to serve their business stakeholders in a very effective manner. For business teams, automation of insights with industry specific recommendations eliminates time required to conduct explorations and provides them with the opportunity to make intelligent data driven decisions. For Analytics practitioners, the scalable & collaboration friendly sandbox environment helps them harness more horse power from their existing resources and driving better business value and higher ROI for the organization.