How is Cognitive Computing different from Big Data and NLP?

SUMIT PANDEY

In this age of digital transformation, big data and cognitive computing are two of the most important technologies that organizations are using to gain a competitive edge. While there is some overlap between the two, they are actually quite different.

In this article, we will look at what cognitive computing is, how it is different from big data and NLP, and the advantages it has over traditional big data technologies.

Cognitive Computing:

Cognitive computing is a type of artificial intelligence that enables systems to not only capture and analyze data, but also to understand and learn from it. This is done through the use of machine learning algorithms and natural language processing techniques.

Cognitive computing systems can not only identify patterns and correlations in data, but also understand the meaning and context of the data. This makes them able to not only make better decisions but also to interact with humans in a more natural way.

Big Data:

Big data is a term that is used to describe the large volumes of data that are being generated by organizations today. The volume, variety, and velocity of data is growing at an unprecedented rate, and organizations are struggling to keep up.

Big data technologies are designed to help organizations capture, process, and analyze this data in order to find insights and make better decisions.

NLP:

Natural language processing (NLP) is a field of artificial intelligence that is concerned with the understanding of human language. NLP techniques are used to convert text into a form that can be processed by computers. This can be done by identifying the individual words in the text, as well as the relationships between them. NLP can be used to extract insights from text data, as well as to interact with humans in a more natural way.

Here are the advantages of cognitive computing vs. traditional big data technologies for these problems:

1. Cognitive computing systems are better at understanding the meaning and context of data. This makes them better at identifying patterns and correlations, as well as making decisions.

2. Cognitive computing systems can interact with humans in a more natural way. This makes them more user-friendly and allows them to extract more insights from text data.

3. Cognitive computing systems are powered by machine learning algorithms and natural language processing techniques. This makes them more efficient and allows them to learn on their own over time.

4. Cognitive computing systems are scalable and can handle large volumes of data. This makes them better suited for big data applications.

Bottom Line

Cognitive computing is a more advanced form of artificial intelligence that enables systems to not only capture and analyze data, but also to understand and learn from it. This makes them better at making decisions and extracting insights from text data.

Cognitive computing systems are powered by machine learning algorithms and natural language processing techniques, which makes them more efficient and allows them to learn on their own over time. They are also scalable and can handle large volumes of data.