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, and images, within the reach of Analytics processes that are further used for Decision-Making and Business Intelligence.
It applies Human-like intelligence to tasks such as extracting the full-text summary rather than individual words. Therefore, it uses a combination of technologies such as Semantics, algorithms of Artificial Intelligence, Machine Learning, and 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 within the knowledge base to determine the Real-Time solutions.
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How does Cognitive Analytics work?
Cognitive Computing is highly dependent upon Deep Learning and Neural Networks. Deep Learning is based on the architecture 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 weighted interconnections. The Deep Learning Neural Network consists of multiple layers of neurons. Learning occurs when the weights are updated between the interconnection of consequences. 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.
What are the benefits of Cognitive Analytics?
Users can understand the growth of the business from their cognitive initiatives. This method enhances customer engagement, which increases efficiency quickly and further increases the company's development. The Benefits are -
Customer Engagement
- Improved Customer Service.
- Personalized Customer/User Experience.
- Increased Customer engagement.
- Enable 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.
Business Growth
- Expanded ecosystem.
- Expanded business into new markets.
- An accelerated innovation of new products/services.
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Why is Cognitive Analytics important?
Technology plays a vital role in improving society. To tackle society's challenges, the best combination of Human and Machine intelligence is required. Therefore, cognitive technology is applied. The Cognitive technique is involved in various domains, such as -
- Social Services - With cognitive technology, insight is extracted from the dataset. This further helps build personalized service plans and understand the vulnerability from a microscopic view. It also provides better provisions for protecting at-risk groups.
- Environment - With the growing impact of humans on the natural world, there is a need to protect it for future generations. Cognitive Analytics helps deal with fundamental problems such as climate change, food availability, water, and energy. Therefore, the government can point out the pollution sources more effectively. It also helps to determine the anomalies or problem areas that further decrease deforestation, track urbanization, mitigate diseases, and better control the ecosystem.
- Public Safety - Using cognitive technology, better insights are extracted to achieve better situational awareness. Therefore, new capabilities are introduced, such as combatting epidemics, managing disasters, and fighting crime.
How to adopt Cognitive Analytics?
The designing of specific algorithms is complex for large corporations. The customized search method is required for understanding and functioning within the network to achieve the desired results. The system should be able to learn by previous searches and files selected. Therefore, machine learning and cognitive search will benefit the business network. The emergence of cognitive search within the web not only boosts the speed for locating files and information but also emphasizes the functioning of the entire network using well-defined specialized applications.
What are the best practices of 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.
What are the best Cognitive Analytics Tools?
- Statistical NLP
- Deep-learning NLP
- Rule-based NLP
- OpenNLP
- GATE
- 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 pre-trained 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.
What are the applications of Cognitive Analytics?
The applications of Cognitive Computing are the following:
- Logistics and Transportation
- Retail Industry
- Cyber Security
- Healthcare
- Banking and Finance
- Education
- Power and Energy
A Holistic Strategy for Cognitive Analytics
- Learn about Text Analytics Techniques and Tools
- Get more insights about the Learning Analytics Platform.
