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"Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway."

- Geoffrey Moore, Author of Crossing the Chasm & Inside the Tornado

Instead of simply listening to the loudest voices, an adaptive institution, wherever possible, looks into its data. It uses the data to glean information, and it uses this information to formulate new business wisdom to optimally track and correct course. Such adaptive organization needs to determine whether or not it agrees with the data. If not, it needs to recalibrate the data collection paradigm and redefine its metrics.

To forecast change, and then to adapt to it, is the key to survival in this new era of accelerating advances in science and technology. You don't want to be left on the wrong side of the next Copernican revolution, whatever it may be for your sector.

Technologies and methodologies like big data analytics, randomized experiments, machine learning, natural language processing, and data visualization can be employed to glean such insights. Collection strategies, storage technologies, cloud mining opportunities, and non-relational databases (NoSQL) are some of the enablers.

Data can now be gathered by cheap and numerous information-sensing mobile devices, aerial sensors, software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The capacity of an individual to store information is doubling every three years or so. Each year about three million terabytes of data is created, and our data gathering continues to explode towards the zeta byte range.

Enterprises that utilize AI-enhanced applications are expected to become more diverse as the needs for the ability to analyze data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage. With the advent of web-enabled infrastructure and rapid strides made by the AI development community, the application of the artificial intelligence techniques in real-time business applications has picked up substantially in recent years.



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