Cognitive Computing

Cognitive computing is an evolution of technology that attempts to make sense of a complex world that is drowning in data in all forms and shapes. Organizations standardized business processes and managed business data more efficiently and accurately than with manual methods. However, as the volume and diversity of data has increased exponentially, many organizations cannot turn that data into actionable knowledge. The amount of new information an individual needs to understand or analyze to make good decisions is overwhelming. The next generation of solutions combines some traditional technology techniques with innovations so that organizations can solve vexing problems.

A cognitive computing system consists of tools and techniques, including Big Data and analytics, machine learning, Internet of Things (IoT), Natural Language Processing(NLP), causal induction, probabilistic reasoning, and data visualization. Cognitive systems have the capability to learn, remember, provoke, analyze, and resolve in a manner that is contextually relevant to the organization or to the individual user.

DataSwarm enables Cognitive Data Science applications

• Handle huge amount of data in many different forms and assimilate all sorts of data and knowledge that is available from a variety of structured, semi-structured, and unstructured sources

• Analyze industry-specific data (typically unstructured) that is constantly expanding

• Correlate a variety of data sources to determine context, patterns, and anomalies

• Find a way to match the data with deep expertise

• Analyze large amounts of data to support decision making, such as next best action

• Build system which learn and change as business conditions change

• Leverage highly distributed and cost-effective computing services to make large-scale cognitive computing operational

• Process streaming data coming from equipment sensors to medical devices to temperature sensors to stock market financial data to video streams and IoT at high speeds. Streaming data is useful when analytics need to be done in real time while the data is in motion, because the value of the analysis (and often the data) decreases with time. For example, if you can’t analyze and act immediately, a sales opportunity might be lost or a threat might go undetected