Across almost every industry today, 21st century companies are transforming into data-driven companies. In many ways, this transformation is challenging, not just because of big data or, data’s volume, velocity and variety but also because of the growing need for data integration agility. Complex data silos afford only an incomplete picture into data and they slow down the ability to provide access or gain timely insights. In industries such as life sciences, we must also give notice to the bevy of operational, regulatory and compliance requirements that pose additional access, governance and sharing challenges. Rapid, non-stop change and growth makes agility and time-to-value an even more challenging hurdle. Now, a strong emphasis on analytics and data discovery for new insights is introducing additional challenges in how data is leveraged into the fabric of the organization, too.
Within the life sciences literature, navigating and understanding data has been described as “the greatest challenge to unlocking knowledge and scientific discovery.” Unlocking knowledge and scientific discovery, in this context, requires that analysts and researchers have access to complete, high quality and actionable information in a way that is agile and that leverages available tools and technologies to drive analytics and discovery. With that pretext, today’s analytic challenges for life sciences companies can be separated into three distinct categories:
The Integration Challenge the ability to agilely and quickly unify multiple data sources to provide researchers, analysts and managers the full view of information without incurring massive costs of physical data consolidation.
The Management Challenge the guidance and deposition of context and metadata, and a sustainable and reliable infrastructure that defines and access and permissions while addressing governance challenges.
The Discovery Challenge a set of challenges that balance the need for data discovery while maintaining proper IT oversight and stewardship, including concepts of user data democratization, self-sufficiency, establishing a single version of the truth, scalability and incorporating visualization to communicate of discovery insights.
The answer to these challenges, however, isn’t the development of new tools or technologies. In fact, the old ways replication, transformation or even the data warehouse or new desktop-based approaches to analytics have achieved limited or siloed success; they simply don’t afford an agile enough process to keep up with the insurgence of data size, complexity or disparity. Nor should life sciences companies rely on the expectation of increased funding to foster additional solutions. Rather, they should turn to collaborative and transformative solutions that already exist.
First, data abstraction through a semantic layer supports timely, critical decision-making as different business groups become synchronized with information across units, thereby reducing operational silos and geographic separation. The inclusion of a semantic layer centralizes metadata management, too, by defining a common repository and catalog between disparate data sources and tools. It also provides a consolidated location for data governance and implementation of underlying data security, and centralizes access permissions, acting as a single unified environment to enforce roles and permissions across all federated data sources.
Data stored on the cloud can be easily and agilely abstracted with centralized context for everyone enabling global collaboration. Several life sciences-based use cases have proven that using the cloud drives collaboration, allowing various functions within life sciences companies to work more iteratively and with faster momentum. Ultimately, where data resides will have a dramatic effect on the discovery process, and trends support that eventually more and more data will be moved to the cloud. Moving abstraction closer to the data, then, is paving the road for future life sciences innovations.
Providing users with tools that leverage abstraction techniques keeps data oversight and control with IT, while reducing dependency on IT to provide users with data needed for analysis. Leveraging this self-sufficient approach to discovery with visual analytic techniques drives discovery by bringing data to a broader user community and enabling users to take advantage of emerging visual analytic techniques to visually explore data and curate analytical views for insights. Visual discovery makes analytics more approachable, allowing technical and nontechnical users to communicate through meaningful, visual reports that can be published (or shared) back into the analytical platform to encourage meaningful collaboration. Self-sufficient visual discovery and collaboration will benefit greatly from users not having to wonder where to go get data - everyone simply knows to go to the one repository for everything.
“Challenged by regulation and ultra-competitive sales and marketing tactics, data governance and analytic agility have traditionally been at odds in industries like life sciences,” noted Birst VP of Product Strategy, Southard Jones. “Cloud BI platforms are bringing together consumer-driven user interfaces and agile data management to offer a new way for life sciences companies to achieve a data-driven competitive advantage.”
Ultimately, by choosing abstraction for unification, embedding business context into data through the inclusion of a semantic layer, leveraging cloud technologies, and enabling business users with self-service tools that offer robust analytic capabilities including advanced visualization, life sciences companies can continue on their journey to becoming even more data-capable organizations.
Read how choosing abstraction, the cloud and visualization is meeting data challenges for life sciences companies here.