Everything you need to know about cognitive analysis

To provide a scenario And find the answers hidden in a lot of information , Cognitive computing combines a variety of applications . The use of cognitive analysis and intelligent technology makes most data sources available for decision-making and business intelligence analysis programs .

What is cognitive analysis ？

Everyone is trying to find the answer to the question of what cognitive analysis is and what intelligent technology is . stay IT Everyone working in the industry is aware of , AI was just beginning , There are many more in the future . This is exactly what happens when cognitive analysis is introduced . It is a technology mainly used to connect all data sources to the analysis processor platform . Cognitive analysis wants to know that it considers all types of data in the whole context . Start with the basics , Let's take a closer look at the various components of cognitive analysis .

Analysis with humanoid intelligence is cognitive analysis . This may involve understanding the scene and meaning of the sentence , Or recognize some items in the picture given a large amount of information . Cognitive applications can become better over time , Because cognitive analysis often combines machine learning and artificial intelligence technology . Simple analysis cannot find some connections and patterns that cognitive analysis can find . Companies can use cognitive analysis to track customer behavior trends and new developments . In this way , Companies can predict future results and adjust their goals to better perform .

Predictive analytics uses data from business intelligence to create forecasts , Including some aspects of cognitive analysis .

Basis of cognitive analysis

Analysis is nothing more than computerized examination of data , Cognition refers to a series of psychological operations performed by the brain . Since cognition is connected with human mind , That is nothing more than the application of intelligence , Similar to human intelligence . In order to calculate various forms of data , This is different from AI 、 machine learning 、 Combining semantics with deep learning .

Understand data that is usually unstructured and scattered around the world , It is one of the most important challenges facing the company on a global scale . We have cognitive computing because it is almost impossible for the human brain to process such a large amount of data . Enterprises can use various tools and applications to infer the context of their data , And provide analysis driven information by using cognitive computing .

These conclusions lead us to data analysis , This includes descriptive analysis . As we know , Both normative analysis and predictive analysis have a history of ten years . These technologies have helped some intelligent technologies gain attention today . AI Conference on 1956 Held at Dartmouth College , It has made great contributions to understanding the importance of current contemporary technologies such as cognitive analysis .

The study found that , Organizations that use data to support projects rely heavily on sources of unstructured data , E-mail 、 Trading data 、 Customer database 、 stay MSWord Documents prepared in and other such worksheets , Such as IDG Titled “ Big data and analytics ” In this article : Insight into initiatives and strategies to promote data investment ,2015 year ”. Sources of unstructured data also include open source data , For example, posts on social media 、 Census data and patent information . therefore , It is inevitable to adopt intelligent technologies such as cognitive analysis . The cost of not managing these unstructured data is very high , Therefore, many companies can afford the cost-effective tools and applications that use cognitive analysis technology today .

benefits

Basically , It promotes a technology that allows and improves consumer interaction , So as to accelerate the development of enterprises . Here are some of the most significant advantages .

Customer interaction

Cognitive computing is useful for consumer interaction in three areas .

• Enhanced customer service
• Provide customized services
• Ensure faster response to consumer needs

From the perspective of productivity , The four areas listed below are its advantages

• Better judgment and Planning
• Significantly lower costs
• Improve the learning experience
• Better governance and security
• Business expansion

Besides , Cognitive analysis promotes enterprise success in the following ways ：

• Increase sales in new markets
• Launch new goods and services

How it works ？

We have introduced what it is , A glimpse of its evolution , And some of its most significant benefits . Now? , Let's look at the operation and application of cognitive analysis . It follows a certain gradual approach , Such as XenonstackInsights A quick guide to cognitive analysis tools and architectures .

• It is important for the whole data field or what we call “ The knowledge base ” Conduct a thorough search , To finally locate real-time data .
• After obtaining real-time data , It will be displayed as an image 、 voice 、 Provided in the form of text and video , These data are compatible with advanced analysis tools , It can be used for subsequent decision-making and business intelligence .
• It extracts patterns and insights from a batch of data and uses them for later use , Similar to the way the human brain works .
• These programs include several different components , Including neural networks 、 Deep learning 、 machine learning 、 Semantics and artificial intelligence .

according to Gartner Vice president of research RitaSallam That's what I'm saying , If enterprises want to significantly affect their growth and make wise decisions , We should take advantage of cognitive analysis . according to Sallam That's what I'm saying , Early adopters of this technology may have more advantages than other enterprises . Enterprises must thoroughly understand different models , To focus on the value of the whole company .

Why is it adopted ？

The difficulty that large enterprises encounter in developing algorithms is a major factor in adopting cognitive analysis . A tailored technology must be created to perform this , Because it involves searching a lot of data . therefore , Machine learning and cognitive analysis work together , Make it very useful and successful for enterprises . Due to the application of cognitive analysis , We see two main impacts . Because the search performance is greatly improved , Users now find it easy to view files and information . The performance of the whole network and other applications have been significantly improved .