How to prioritize domain analytics within your organization Domain analytics refers to the collective set of analytics that are applied across all industry verticals and business processes. Data and analytics leaders should optimize their domain-specific capabilities for achieving success in their industry.

Even though analytics is becoming ever more pervasive, in several organizations, most of it is still carried out in isolation across business domains. This has given rise to the requirement…

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4 ways to introduce AI in your business AI encompasses several complex cognitive technologies such as neural or deep learning networks, natural language processing, and machine learning algorithms. Introducing AI in business can be helpful as it helps in bringing radical cost reductions and opening up new kinds of opportunities.

Many businesses now understand what AI and machine learning is. They have learned that by introducing AI in business, they can use data to make predictions and it is the…

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The role of a Chief Analytics Officer in an organization Companies that want to enable enterprise-wide data analytics require analytics leadership and talent that can work efficiently and in a smarter manner with the information they have, and also exhibit the capability of exploiting new sources of information. This requirement has given rise to the role of Chief Analytics Officer.

Many organizational leaders instinctively turn to the office of the CIO to deliver business insights. But, usually, CIOs are not responsible for delivering business insights. Rather, they are responsible for…

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Causal inference in AI Causal inference in AI refers to the process of drawing a conclusion based on the causal connection amongst the conditions of the occurrence of an effect. The main goal of causal inference is to analyze the response of the effect variable when the cause is changed.

Causality is based on how we view and react to the environment around us, to our decision making, and to the advancement of science. Causal inference in AI has advanced…

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Applying Deep Learning to natural language processing Language is the medium that humans use for conversing. Giving machines the ability to learn human language with natural language processing has given rise to several new products and possibilities that were not previously imaginable.

Natural language processing (NLP) is one of the most important technologies present in the information age. Understanding complex language utterances is a crucial part of artificial intelligence. Applications of NLP…

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Machine learning for anomaly detection In data mining, anomaly detection is referred to the identification of items or events that do not conform to an expected pattern or to other items present in a dataset. Typically, these anomalous items have the potential of getting translated into some kind of problems such as structural defects, errors or frauds. Using machine learning for anomaly detection helps in enhancing the speed of detection.

Intrusions are those activities that can damage information systems. Intrusion detection has been gaining broad attention.  Anomaly detection can be a key for solving intrusions, as while detecting anomalies, perturbations…

Digital BusinessIOT
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