3 shifts in the modern data environment and what it means for IT leaders

Providing organizations with reliable data for better decision-making is an undertaking that has not fundamentally changed in decades. Despite massive technology advances and new tactics, the
IT organization managing data infrastructure today still has the same overall mission: moving data from its moment of creation and making it accessible and understandable by decision-makers at the moment of need.
However, while the objective has stayed the same, the obstacles to successfully create and maintain a source of analytical truth within a business have become exponentially more difficult.
Perhaps the biggest hurdle in recent years within the modern data environment has been new sources of data that generate unprecedented amounts of output, often with very little (if any) structure. From clickstreams, server logs, and social media sources to machine and sensor readings, the onslaught of data from these channels has been overwhelming—literally. From an economic and performance point of view, traditional enterprise data warehouses (EDWs) simply cannot keep up with this data tidal wave.
This has sparked a complete re-think of data capture and analysis strategies and given rise to a new generation of data storage solutions aimed at schema-less capture, hardware scalability, and
the moving of computing capability closer to (if not on top of) data stores themselves.
Though still young by relational database standards, these newer, non-relational solutions have gained serious traction in recent years and matured rapidly to support some of the largest and most complex corporate enterprises in the world. While this has been done largely as a means to complement existing enterprise data warehouse infrastructures, it never the less creates a more complex data ecosystem for IT to manage.

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