What if machines could have human-like intellect?
That’s the promise of cognitive analytics. It is a new field of data analytics that combines artificial intelligence (AI), machine learning (ML), natural language processing (NLP) and cognitive science. Cognitive analytics systems understand, learn and interact with humans in natural ways.
The market for cognitive analytics is estimated to be valued at US $2.32 billion in 2023 and will reach US $7.9 billion by 2033. – Future Market Insights
With cognitive data analytics services, you can gain valuable insights from complex data and improve operational efficiency. Cognitive analytics is still in its early stages of development, but it has the potential to revolutionize many industries and aspects of our lives. Let’s explore the technology in depth.
How does cognitive analytics work?
Cognitive algorithms can use a variety of machine learning techniques, including deep learning, to learn from data and make predictions. Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the structure and function of the human brain, and they are capable of learning complex patterns from data.
Cognitive analytics systems can also use natural language processing (NLP) to understand and extract meaning from text data. NLP is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP techniques can be used to identify entities, relationships and sentiments in text data.
1. Data collection and preparation: The first step is to collect and prepare the data that will be used for cognitive analytics. This data can come from a variety of sources, such as customer databases, social media, and sensor data. The data needs to be cleaned and formatted in a way that can be easily processed by cognitive analytics systems.
2. Knowledge base creation: Cognitive analytics systems use a knowledge base to store information about the world. This knowledge base can include information about entities, relationships, and concepts. The knowledge base is used to help the system understand the data that it is processing.
3. Data analysis: Once the data has been prepared and the knowledge base has been created, the cognitive analytics system can begin to analyze the data. This involves using machine learning and other AI techniques to identify patterns and insights in the data.
4. Output generation: The final step is to generate output from the cognitive analytics system. This output can be in a variety of formats, such as text, images, or audio. The output should be presented in a way that is easy for humans to understand and use.
Benefits of customized cognitive analytics solutions
- Data-drive decision making: Cognitive predictive analytics can help organizations make better decisions by providing them with insights into their data that they would not be able to find on their own.
- Increased efficiency: It can help organizations automate tasks and streamline processes, which can lead to increased efficiency and productivity.
- Reduced costs: Organizations can identify areas where they can reduce their expenses and focus on areas that generate profits.
- Enhanced innovation: Cognitive analytics can help organizations develop new products and services by providing them with insights into their customers and the market.
- Improved customer services: Predictive cognitive analytics for industry insights helps companies to improve customer experience.
Cognitive analytics best practices
1. Understand the distribution of your data and identify outliers. This can be done by using a variety of statistical calculations, such as mean, median, mode and standard deviation. Outliers are data points that fall significantly outside the normal distribution. They can be caused by errors in data collection or entry, or they may represent genuine anomalies in the data.
2. Be aware of and report noise in your data. Noise is random data that does not contain any useful information. It can be caused by factors such as measurement errors or environmental interference. Noise can affect the results of cognitive analytics models, so it is important to be aware of it and report it in your findings.
3. Analyze and visualize your data to understand its structure and identify patterns or relationships. This can be done using a variety of tools and techniques, such as statistical analysis, data mining and machine learning. By understanding the structure of your data and identifying patterns and relationships, you can better understand the underlying phenomena that it represents.
4. Wedge your data into smaller subsets to identify patterns and relationships that you might not be able to see in the entire dataset. This is a common technique in data mining and machine learning.
5. Assess the importance of your findings and their implications for your business. This is known as practical significance. Cognitive analytics models can generate a lot of findings, but it is important to focus on the ones that are most important and relevant to your business.
6. Compare your data over time to identify any trends or changes. This is known as time series analysis. By comparing your data over time, you can identify patterns and trends that can help you make better predictions and decisions.
Correlation between cognitive analytics and different types of data analytics
Cognitive analytics is closely related to all these types of data analytics. It can be used to enhance descriptive analytics by providing more detailed and insightful reports. It can also be used to improve diagnostic analytics by helping to identify the root causes of problems and opportunities that might be difficult to detect with traditional statistical techniques.
Cognitive analytics can also be used to improve predictive analytics by developing more accurate and sophisticated forecasting models. Finally, cognitive analytics can be used to improve prescriptive analytics by providing more comprehensive and actionable recommendations.
How cognitive analytics is being used in conjunction with other types of data analytics:
- Retailers might use cognitive analytics to analyze customer data and identify patterns in purchasing behavior. This information can then be used to develop more personalized marketing campaigns and improve the customer experience.
- Manufacturers might use cognitive analytics to analyze sensor data from their production lines. This information can then be used to identify potential problems and prevent downtime.
- Healthcare providers might use cognitive analytics to analyze patient data and identify patterns that could indicate the risk of developing certain diseases. This information can then be used to develop personalized treatment plans and improve patient outcomes.
Real-life uses of cognitive analytics
University of North Carolina
Researchers at the University of North Carolina tested IBM Watson (powered by cognitive analytics) for Genomics on 1,018 patients. In 99% of the cases, Watson for Genomics agreed with the physicians’ diagnoses. However, in over 300 cases, it identified additional actionable mutations that the board had missed. Watson for Genomics was able to complete this analysis in under three minutes per case.
Uber
Uber introduced a new security feature called Real-Time ID Check. This feature periodically prompts drivers to take a selfie before they go online to accept ride requests. It uses Microsoft Cognitive Services to instantly compare the selfie to the driver’s photo on file. If the two photos do not match, Uber will temporarily suspend the driver’s account while the situation is investigated.
Airbus
Airbus uses cognitive analytics to analyze data from aircraft sensors and maintenance records to predict when aircraft components are likely to fail. This helps Airbus schedule preventive maintenance and avoid unscheduled downtime. In turn, the company is improving the safety of its aircraft to prevent plane crashes.
Adopt cognitive analytics consulting for improved decision making
Cognitive analytics is a rapidly growing field with the potential to make a significant impact on the world around us. Cognitive analytics is already being used in a variety of ways to improve business performance, make better decisions, and provide better customer service. As cognitive analytics technology continues to develop, we can expect to see the emergence of even more innovative and groundbreaking applications.
The future of cognitive analytics systems is even more sophisticated and capable. They will be able to learn from more data, identify more complex patterns, and make more accurate predictions. This will make cognitive analytics an even more powerful tool for businesses and organizations of all sizes.
Softweb Solutions offers a wide range of data analytics capabilities, including data warehousing, data mining, machine learning and deep learning. We help you collect, clean, analyze and visualize your data to gain valuable insights and make informed decisions. Contact us today to know more.
