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:

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