We know that the ability to derive actionable insights from Big Data is a major challenge facing enterprise decision-makers.
In particular, we see this frustration in industries where real-time intelligence can make a significant difference not just in operational cost savings, but also in proactive, optimization of business value. In the real-time enterprise, real-time analytics must be integrated with operational metrics to guide intelligent management of activities and processes, identifying risks and opportunities - taking action before the impact on business value.
We know there's a gap between the data and action - a gap which can be defined as a semantic gap between the sensor and the pattern-based strategy that makes sense of the unstructured data - images, video feeds, waveforms, etc.
The challenge of Big Data Analytics is to Mind the Gap. Despite the technical nature of much of the AI advances in this field, organizations must become strategic in their pursuit of solutions in this space, or risk falling behind their competitors. The goal of intelligent value creation is business results: faster learning time, quicker analysis, better informed decisions and actions - all resulting in accelerating time to value.
The future of institutional innovation depends on how well they Mind the Gap as a continuous learning process, not as a static, technology-based, improvement initiative.