What is Cognitive Analytics?
Cognitive Analytics simulate the human thought process to learn from the data and extract the hidden patterns from data. It brings all the data sources such as audio, video, text, images within the reach of Analytics processes that further used for Decision-Making and Business Intelligence.
It applies the Human-like intelligence to tasks such as extracting the full summary of the text rather than individual words. Therefore, Cognitive Analytics makes use of the combination of technologies such as Semantics, algorithms of Artificial Intelligence, Machine Learning, Deep Learning. When these technologies are applied, the respective Cognitive application will become more effective by learning with time from the interactions with the data and humans. A Cognitive Analytics System searches through the entire data that exists within the knowledge base to determine the Real-Time solutions. Give a read to this blog based on text analytics for more understanding.
How Does Cognitive Analytics Work?
Cognitive Computing is highly dependent upon the Deep Learning and Neural Network. Deep Learning based on the architecture which is known as Deep Learning Neural Network that emerges from the Neural Network architecture. You may also read more about Predictive Analytics in this insight.
The structure of the human brain influences the Neural Network. It is made up of neurons connected with the weighted interconnections. The Deep Learning Neural Network is consist of multiple layers of neurons. Learning will take place when the weights updated between the interconnection of weights.
The Learning is composed of three phases –
- Understanding natural language and human interactions.
- Generating and evaluating the Evidence-based hypothesis.
- Adapting and learning from user selections and responses.
Benefits of Enabling Cognitive Analytics
Users can understand regarding the growth of the business from their cognitive initiatives. This method enhances customer engagement that increases the efficiency at the fast pace which further increases the growth of the business. The Benefits are –
- Improved Customer Service.
- Personalized Customer/User Experience.
- Increased Customer engagement.
- Enable the faster response to customer/market needs.
Productivity and Efficiency
- Improved productivity and efficiency.
- Improved decision making and planning.
- Improved security and compliance, reduced security.
- Reduced costs.
- Enhance the learning experience.
- Expanded ecosystem.
- Expanded business into new markets.
- An accelerated innovation of new products/services.
Why Cognitive Analytics Matters?
Technology plays a vital role in improving society. To tackle the challenges of society, the best combination of Human and Machine intelligence required. Therefore, cognitive technology is applied. The Cognitive technique is involved in various domains such as –
- Social Services – With the use of cognitive technology, insight extracted from the dataset. This further helps in building the personalized services plans and understand the vulnerability from a microscopic view. It also provides better provisions for protecting for at risks groups.
- Environment – With the growing impact of human on the natural world there is a need to protect it for future generations. Cognitive Analytics helps in dealing with fundamental problems such as climate change, food availability, water, and energy. Therefore, the government can able to point out the sources of pollution more effectively. It also helps to determine the anomalies or problem areas which further decrease the deforestation, track urbanization, mitigate diseases and better control the ecosystem.
- Public Safety – By using cognitive technology, better insights are extracted to achieve better situational awareness. Therefore, new capabilities are introduced such as combat epidemics, manage disasters and fighting for the crime.
How to Adopt Cognitive Analytics?
The designing of specific algorithms is difficult for the large corporation. The customized search method is required for understanding and functioning within the network for achieving the desired results. The system should be able to learn by previous searches and files selected. Therefore, the combination of machine learning and the cognitive search will be highly useful within the business network. With the emergence of cognitive search within the network not only boost the speed for locating files and information it also emphasizes the functioning of the entire network with the use of well defined specialized applications. You can also more about Continuous Integration and Deployment with TensorFlow and PyTorch in this blog.
Best Practices for Enabling Cognitive Analytics
- Use the full range of calculations for distributions.
- Consideration of data outliers.
- Report noise within the dataset.
- Analyzing and visualizing the data to determine complexity.
- Slicing the dataset.
- Review of practical significance.
- Examination of consistency of data over time.
Cognitive Analytics Tools
- Statistical NLP
- Deep-learning NLP
- Rule-based NLP
- Apache Lucene Core is a full-featured text search engine Java library.
- GPText from Greenplum is a Statistical Text Analysis framework optimized for execution on parallel computing platforms.
- SyntaxNet is an open source Neural Network framework for developing natural language understanding systems.
- Parsey McParseface is a pretrained SyntaxNet model for parsing the standard English language.
- TensorFlow is another software library for Machine Intelligence.
- NuPIC is a platform for cognitive computing, based on a theory of neocortex called Hierarchical Temporal Memory (HTM).
- OpenCV and ImageJ are libraries for computer vision tasks.
- Praat is a tool for speech manipulation, analysis, and synthesis.
- OpenSMILE is another tool for extracting audio features in Real-Time.
A Holistic Strategy
Cognitive analytics utilizes intelligent technologies to make data sources within reach of analytics methods for decision making and business intelligence. To see the best results for cognitive analytics, you can take a look at the below steps:
- Learn about “Text Analytics” in this blog
- Get more insights about “Learning Analytics“
- Read our use case for ” Data Analytics Insights“
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