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Analytics and Insight

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PCQ Bureau
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The application of information technology in the enterprise has been mostly driven by the need to increase productivity and improve enterprise operations. Most of these have been enterprise automation applications such as ERP, business process automation and transaction processing. As technology and information sources have advanced the rapid growth of enterprise optimization applications has also increased. These advancements are driven by analytics technologies — processing a large quantity of relevant data and providing insights that are most relevant to decision-making that impact the performance of an enterprise.

These applications are driven by underlying technologies that sift through large volumes of relevant data, correlating and analyzing patterns and trends at a rapid pace and turning all of this into insights that are delivered to human decision makers. Many applications also provide recommended actions; along with supporting information that are delivered to the systems or people that can effectively implement them

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Predictive analytics connects data to effective action by drawing reliable conclusions about current conditions and future events. Our ability to apply such predictions has improved rapidly, especially as more relevant information — current and historical — is available for systems to work with. Today, using historical and real-time information about traffic conditions in a city, one can predict the time a certain bus will take to arrive at its destination. In the future, one can expect to see such information used to redirect traffic and to take actions proactively to avoid congestion on roads. A telecom company can predict who is likely to become irate with the changes in their service and take proactive actions to handle their most valuable subscribers.

The accuracy of such predictions depends centrally on the accuracy of the modeling algorithms that are used to model the data and draw conclusions about possible future outcomes. This in turn depends on the availability of large enough volumes of data where a variety of interdependencies have been observed before.

With the evolution of Deep Q&A technology, exemplified by the Watson system, this problem is becoming much more tractable. Watson has the ability to process very high volume of data, in particular unstructured content, can derive facts from content, establish correlations across them, and more importantly, provides a small number of choices with associated confidence on the answers. This last part is essential for human decision-making. Once this technology gets applied to other domains, especially to domains such as healthcare, we can expect the quality of human decision-making to improve significantly.

As analytics technologies evolve to handle greater variety of data and offer more accurate and reliable insights, the possibilities for organizations — private enterprises, governments and others — to optimize their decisions and deliver better services and products become endless.

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