Data management processes in the life sciences industry are in a state of significant flux as two key trends converge:
The data explosion
The quantity of data, the number and diversity of data sources – as well as the complexity of data in the life sciences industry – is increasing at a mind-numbing pace.
Companies must mine ever-larger amounts of information (claims data, specialty pharmacy data and more) for commercial insights. The increasing availability of anonymized data from electronic health records is a significant driver of this proliferation of data. Additionally, as data vendors improve their products and offer more visibility into the nuances of individual markets, they give life sciences companies valuable insights but also more information to manage, process and analyze.
Further, data in the industry is expansive and complex. One factor contributing to this trend is that companies can more easily link data from different sources today, making data sets more interdependent. Properly organizing and analyzing data is challenging, and even if analysts don’t introduce errors, complexity can slow down their analytics processes or lead them to adopt ineffective analytics practices. Poor data management can make a company’s data systems unstable and render the insights the company pulls from data unreliable.
The cloud revolution
The rapid advancements in data management technology are also driving change and creating new opportunities for life sciences commercial teams. The traditional data management model within life sciences companies is heavily siloed.
By keeping data in silos, a company limits its ability to access the most beneficial insights from its data. Further, a siloed approach to data management has proven nearly impossible to scale for a growing organization that demands actionable insights from significant amounts of information. The good news is that with the emergence of enterprise cloud technology, companies can begin to move toward data centralization. A cloud-powered central hub for data management, processing and analysis is closer to reality than ever before, and companies need to start making the transition from silos to a more centralized data management operation today.
In this time of transition, companies still need discrete technology systems – from platforms for data ingestion and aggregation to warehousing to reporting and business intelligence. To build more centralized data management processes, they must begin challenging long-held assumptions, overhauling traditional practices and planning how technologies will work together to support unified data systems.