Q 541. What is Cognitive Analytics? How is it different from other traditional analytics approaches - descriptive, predictive and prescriptive? Provide some examples where Cognitive Analytics is being deployed to support your answer.

 

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Cognitive Analytics is a type of advanced analytics that uses machine learning algorithms, natural language processing, and other artificial intelligence (AI) techniques to analyze data, identify patterns, and draw insights. It aims to emulate human thought processes and simulate human-like intelligence to provide more meaningful insights.

 

Compared to traditional analytics approaches, such as descriptive, predictive, and prescriptive analytics, cognitive analytics goes beyond these approaches by incorporating more complex and sophisticated techniques. Descriptive analytics focuses on describing what happened in the past, predictive analytics uses historical data to make future predictions, and prescriptive analytics provides recommendations to optimize outcomes. In contrast, cognitive analytics not only identifies what happened, but also provides a deeper understanding of why it happened and what could happen in the future.

 

Here are a few examples where Cognitive Analytics is being deployed:

 

Customer Service: Cognitive analytics is used in customer service to analyze customer interactions, including emails, chats, and phone calls. It can help identify patterns in customer behavior, such as the most common issues that customers face or the most frequent questions they ask. This insight can help companies improve their products and services and provide better customer support.

 

Healthcare: Cognitive analytics is used in healthcare to analyze patient data and improve diagnosis and treatment. For example, it can help doctors identify patterns in patient symptoms and medical history, and make more accurate predictions about disease progression.

 

Fraud Detection: Cognitive analytics is used in finance to detect fraudulent transactions. It can help identify patterns in transaction data and flag suspicious activity, such as unusual spending patterns or large withdrawals.

 

Marketing: Cognitive analytics is used in marketing to analyze customer data and create personalized marketing campaigns. It can help identify the most effective marketing channels, messages, and offers for different customer segments.

 

Overall, Cognitive Analytics is a powerful tool that can help businesses gain deeper insights and make more informed decisions. By using sophisticated AI techniques to analyze data, it can provide more meaningful and actionable insights that traditional analytics approaches may miss. 

 

Cognitive Analytics is a kind of Predictive analytics, where we can analyse huge data base and other cognitive uses of data can lead to predictions for business objectives. While descriptive analytics is to describe the population data with some parameters like mean, median, mode etc. Predictive analytics is to forecast future outcomes and prescriptive analytics is to provide recommendation based on analysis.

 

Examples: Cognitive analytics mimic human brain and based on AI. Here huge database including pictures text comments etc are used to create algorithms. Based on algorithm it predict or suggest outcome like Google assistance, SIRI and Chatbots are based on AI.

Cognitive Analytics

 

Cognitive analytics is a type of data analysis that involves the use of advanced technologies, such as artificial intelligence and machine learning, to analyze data and identify patterns, insights, and anomalies that would be difficult or impossible to detect using traditional analytics approaches

Difference between Cognitive and other traditional analytics approaches

Cognitive	Descriptive	Predictive	Prescriptive
uses advanced algorithms and machine learning to analyze data in real-time, with the goal of identifying patterns and insights that are not immediately apparent.	primary purpose of descriptive analytics is to summarize and describe historical data, providing insights into what has happened in the past	primary purpose of predictive analytics is to make predictions about future events or trends based on historical data	give detailed differences between cognitive analytics and predictive analytics
uses more advanced statistical and machine learning techniques to identify patterns and relationships in data.	Uses simple statistical analysis techniques to summarize data, such as measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation).	uses statistical and machine learning techniques to identify patterns and trends in data, with the goal of making predictions about future events.	uses mathematical algorithms and optimization techniques to identify the best course of action, given specific constraints and objectives
can also use structured data, but it is better suited for analyzing unstructured data, such as text, images, and social media posts.	typically relies on structured data, such as data from relational databases or spreadsheets	relies on structured data, such as data from databases or spreadsheets	relies on structured data, such as data from databases or spreadsheets
provides predictive insights, identifying trends and patterns that are likely to occur in the future	provides descriptive insights into what has happened in the past, such as identifying trends and patterns in historical data	generates predictions about future events based on historical data	generates recommendations for action based on the results of predictive analytics
uses machine learning and artificial intelligence to provide automated interpretation and insights, making it faster and more accurate than descriptive analytics.	relies on human interpretation to understand the insights generated from the analysis	relies on human interpretation to understand the predictions generated from the analysis.	generates recommendations for action based on the results of predictive analytics
Used in banking industry to monitor transaction patterns, identify unusual activities, and flag them as potentially fraudulent	used to identify historical patterns of fraudulent activities in order to develop risk mitigation strategies for the future	can be used to analyze stock market data and identify stocks that are likely to increase in value in the future.	can be used to detect fraud in real-time and provide recommendations for specific actions to be taken, such as flagging suspicious transactions for further investigation
In the e-commerce industry, cognitive analytics can be used to analyze customer interactions with a website to identify patterns, such as what products are frequently purchased together or what type of content is more appealing to the customers	can be used to identify trends in customer behaviors and preferences.	can be used to identify customers who are likely to churn, allowing marketing teams to develop targeted retention strategies	prescriptive analytics can be used to identify the most effective marketing channels and messaging based on customer data.
can be used to identify trends in customer behaviors and preferences.	descriptive analytics can be used to analyze the prevalence of certain diseases in a population or identify risk factors associated with certain conditions	Can be used to forecast future health risks or outcomes based on historical data. For example, predictive analytics can be used to identify patients who are at high risk of developing a particular condition, allowing healthcare providers to develop targeted interventions	can be used to identify the most effective treatments for a particular condition based on a patient's medical history and other relevant data.

 

Cognitive analytics is a form of advanced analytics that uses artificial intelligence (AI) and machine learning algorithms to extract insights from large and complex datasets, including unstructured data such as text, images, and video. It goes beyond traditional descriptive (provides a summary of past events), predictive (which uses historical data to predict future outcomes), and prescriptive (provides recommendations on the best course of action to take based on a given set of data.) analytics approaches, which focus on analyzing structured data and makes recommendations based on historical patterns and rules. Instead, cognitive analytics uses natural language processing, data mining, and other AI technologies to simulate human thinking and reasoning, enabling it to identify complex patterns, trends, and insights that traditional analytics methods cannot detect.

Below are some examples of how cognitive analytics is being deployed in various industries:

1.Healthcare: Cognitive analytics is being used to help diagnose and treat diseases, such as cancer. By analyzing large volumes of patient data, including electronic medical records, images, and genomics data, cognitive analytics can help doctors identify early warning signs of cancer and provide personalized treatment plans.

2.Financial services: Cognitive analytics is being used to help prevent fraud and financial crimes. By analyzing customer transactions and behavior's in real-time, cognitive analytics can detect patterns and anomalies that indicate fraudulent activity, enabling banks to take immediate action to prevent losses.

3.Retail: Cognitive analytics is being used to personalize shopping experiences and increase customer engagement. By analyzing customer data, such as purchase history, browsing behavior, and social media activity, cognitive analytics can provide personalized product recommendations and marketing messages, increasing the likelihood of sales.

4.Manufacturing: Cognitive analytics is being used to optimize supply chain operations and improve product quality. By analyzing sensor data from machines and equipment, cognitive analytics can identify patterns and trends that indicate potential maintenance issues, enabling manufacturers to take preventative action to reduce downtime and improve product quality.

In summary, cognitive analytics is a powerful tool for extracting insights from large and complex datasets, and it is being deployed in various industries to improve decision-making, increase efficiency, and enhance customer experiences.

 

Cognitive Analytics is an intelligent technology that covers many analytical techniques to analyze large data sets and give structure to unstructured data. To put it simply, cognitive analytics searches through the data that exists in its knowledge base to find solutions that make sense for the questions when asked. This can include understanding the context and meaning of a sentence or recognizing certain objects in an image set with large amounts of information. Cognitive analytics frequently uses artificial intelligence algorithms and machine learning, allowing a cognitive application to improve over time.

 

We know that both Prescriptive Analytics and Predictive Analytics are 10 years-old technology. Thanks to these technologies, today we see many intelligent technologies attain a strong grip. A deep understanding of information helps companies draw from the wide variety of information sources in their knowledge base to improve the quality of enterprise knowledge, and competitive positioning and deliver a deep and custom-made method to customer service.

 

Real-Life Applications of Cognitive Analytics

 

The medical industry is nowadays started to use cognitive analytics to match its patients with the best possible treatments. Some day-to-day examples of cognitive analytics which are in use today such as Apple’s Siri

 

What are the benefits of having cognitive analytics?

 

Depending on the stage of the workflow and the prerequisite of data analysis, there are five main kinds of analytics – descriptive, diagnostic, predictive, prescriptive, and cognitive.  These 5 types of analytics are typically implemented in stages and nothing is said to be better than others. They are supporting, and in some cases additive i.e., you cannot employ the more cultured analytics without using the more necessary analytics first.

 

Descriptive Analytics

 

This is the most naive stage of analytics and for this purpose most organizations today use some type of descriptive analytics. The easiest way to describe it is the process of gathering and interpreting data to define what has occurred

Ex - Most reports that a business generates are descriptive and attempt to summarize historic data or try to explain why one event in the past differed from another.

 

Diagnostic Analytics

 

At this point, you shall start to answer some of those “why” questions. Historical data can be compared against other data to answer the question of why something happened in the past.  This is the process of collecting and interpreting diverse data sets to identify variances, detect patterns, and define relationships.

Ex - Approaches that use diagnostic analytics include alerts, drill-down, data discovery, data mining, and correlations.

 

Predictive Analytics

 

Predictive analytics largely is a type of business intelligence that uses descriptive and predictive variables from the past to analyze and categorize the likelihood of an unknown future result. It brings together a number of data mining methodologies, forecasting methods, predictive models, and analytical techniques to analyze current data, assess risk and opportunities, capture relationships, and make predictions about the future.

 

Prescriptive Analytics

 

Prescriptive analytics is the next step in the evolution of analytics where we take:

 

·         The data we gathered in the descriptive stage that stated to us what happened,

·         Merge it with the diagnostic analytics that will tell us why it happened,

·         Combine those with the predictive analytics that will tell us when it may occur again.

Cognitive Analytics

Cognitive analytics brings together a number of intelligent technologies to achieve this, which includes semantics, artificial intelligence algorithms, and a number of learning techniques such as “deep learning” and “machine learning”. Applying such methods, a cognitive application can get smarter & become more effective over time by learning from its interactions with data and with humans.

 

An organization can use cognitive analytics to observe its customer behavior patterns and evolving trends. By this, an organization can predict future outcomes and plan its objectives accordingly to improve its performance.

Cognitive analytics as the word “Cognitive” indicates relates to being conscious mental activities as in thinking, reasoning, remembering, imaging, and learning. Cognitive Analytics refers to human like intelligence while performing certain task and bring together artificial intelligence (AI), machine learning algorithms, deep learnings and Data Analytics. Cognitive analytics simulates human mental thought process to learn from the data and extract the hidden patterns from it.

 

There are 5 main types of analytics are considered – descriptive, diagnostic, predictive, prescriptive and cognitive. Descriptive analytics refers to the process of generating and interpreting data to describe what has occurred. It uses the raw data and, through data aggregation or data mining, provides valuable insights into the past. Predictive analytics as evident from the names forecasts potential future outcomes. It uses descriptive and predictive variables from the past occurrence, analyses and identifies the likelihood of an unknown future outcome. Prescriptive analytics is the next step in the progression of analytics wherein one draws specific recommendations to influence desired future outcome using the combination of data, mathematical models, and various business rules. Cognitive analytics brings together all intelligent technologies of analytics so that the software can learn by itself, draws conclusions, and assist in decision-making. Banking & finance, Retail and Healthcare are currently the three major business sector using Cognitive Analytics. Few examples of Cognitive Analytics in use today include Microsoft's Cortana, Apple's Siri, and IBM's Watson. Another example is Royal Bank of Canada that uses AI and ML to scan clients transactional histories and usage patterns to provide them with more personalized solutions. Companies are using cognitive analytics to refine product pricing based on purchase records and market trends and thereby increasing the possibility of customer acquisition and leading to revenue growth. Doctors are using to empower better decision-making for better treatment, greater cost-effectiveness, patient empowerment, and better health and fitness.

Very informative answers to an interesting question. Guess everyone's interested in Data Analytics 

 

Best answer has been provided by Anupam Goswami.