We create more than 2.5 million terabytes of data every day. The total amount of data produced, captured, and consumed globally will increase dramatically, and it is projected to reach over 250 zettabytes in 2027.
This data comes from everywhere: purchase transaction records, smartphone GPS signals, sensors used to collect climate information, content posted on social media platforms, images and videos captured by satellites, and more.
To make sense of this data and uncover hidden patterns and insights, it is important efficiently analyze it. Today, almost all companies use data analytics to reduce expenses, make evidence-based decisions, develop and market new products and services, and identify new opportunities.
Over the years, data analytics has evolved from Descriptive (what happened in the past) to Diagnostic (why it happened) to Predictive (what is most likely to happen in the future) to Prescriptive (what can be done to affect future outcomes).
The next major paradigm shift is more likely to be toward Cognitive Analytics, which will harness the power of high-performance processors and Artificial Intelligence algorithms to uncover complex patterns hidden in massive volumes of data.
Below, you will learn why Cognitive Analytics is important, how it works, what are the future applications and market size, and which companies are heavily investing in cognitive analytics tools. Let’s start with a basic question.
What Exactly Is Cognitive Analytics?
Cognitive Analytics is a smart technology that covers various analytical methods to examine structured and unstructured data. It can search through complex datasets and find answers that make sense for the questions posed.
Think of cognitive analytics as a system with human-like intelligence. Instead of just scanning the data, it understands the context, interprets the tone of a sentence, and recognizes specific objects in images or videos.
Cognitive Analytics often utilizes artificial intelligence, machine learning, neural networks, and semantics to uncover intricate patterns and insights (that conventional analytics cannot). It improves itself over time by continuously learning from data and human-machine interactions. This means the more you feed data, the more intelligent the analytics becomes.
Most companies use cognitive analytics to track customer behavior patterns and identify potential opportunities. This way, they can estimate future outcomes and make data-driven decisions to enhance performance.
Some segments of cognitive analytics overlap with predictive analytics. These segments generally belong to information in business intelligence that is used to predict future events.
Note: Cognitive Analytics is also a name of a private firm, as well as a trademarked name for business services. Though market analysts generally use this term when talking about big data and business intelligence.
How Does It Work?
Cognitive analytics is a data-forward approach that begins and ends with what’s inside the data. It uses advanced techniques that process the entire data (all types and at any scale) to reveal unprecedented and unexpected insights. While cognitive analytics systems may use various algorithms to extract actionable insights, their fundamental architecture remains the same, and they all follow a basic sequence of procedures:
1. Search and Gather Data
It searches the entire “knowledge base” to pinpoint real-time data. This data can come straight from sensors, IoT devices, robots, and equipment. It is then organized and integrated with existing or historical data from previous quarters.
Some cognitive analytics systems show data via intuitive dashboards, equipment user interface, and other representations. An easy-to-understand data visualization tool can significantly reduce the time required to find common patterns and carry out repairs.
2. Analyze data
The next step is to carefully analyze text, audio, and video to understand natural language and human interactions. To do this, cognitive analytics combines techniques of artificial intelligence, machine learning, neural network, and semantics.
It primarily relies on the Deep Learning Neural Network, a subset of machine learning. A neural network mimics the way the human brain operates. It consists of multiple layers (an input layer, an output layer, and hidden layers in between) that are connected via nodes, and these connections form a ‘network.’
These interconnected nodes function much like neurons in the human brain. They read all the incoming data and recognize complex patterns using algorithms. The network can cluster and classify raw data, find correlations, and continuously learn and improve over time.
3. Generate and evaluate the Evidence-based hypothesis
The identified patterns and insights are then assessed to find whether they are evidence-based hypotheses. If there is concrete evidence to support the hypothesis, the system evaluates it to determine its potential value. This helps in identifying possible future threats and opportunities for improvement.
4. Adapt and learn from user selections and responses
Since cognitive analytics utilizes artificial intelligence, it can adapt its behavior according to the users’ interactions. For example, if someone is constantly seeking a specific type of information, the system will gradually learn to provide similar information without wasting a lot of computing resources.
Cognitive analytics is applied in enterprises and organizations to bridge the gap between massive amounts of data and the need to make better decisions. The detailed insights into the business help companies improve their products, provide more personalized services to customers, and see how their competitors are performing.
For large companies, cognitive analytics have been proven very effective in three areas:
1. Customer Engagement
The elusive 360-degree view of the customers’ behavior allows companies to create a seamless, friction-free customer experience. Also, it allows companies to develop a deeper understanding of users’ buying preferences, anticipate their needs, and ensure better user segmentation.
2. Productivity and Efficiency
Prioritize and resolve issues faster, leverage crucial information from user conversations, automate actions for common requests, reduce expenditure on less-important resources, and empower team members to respond quickly and dynamically. And since cognitive analytics merges past information with current data to continuously improve the outcomes, you can quickly adapt to external changes and market trends.
3. Business Growth
Cognitive analytics allows enterprises to expand the business into new markets, find new opportunities in specific locations, mine untapped data sources, and innovate new products and services. It also makes it easier for analysts and market researchers to unlock the value of big data and solve problems in their business premises.
Over the past decade, several tech giants and startups have invested hundreds of millions in the emerging field of cognitive technology. Google, Microsoft, IBM, Amazon, Cisco, HPE, SparkCognition, and Data Robot are among the top contributors.
According to the study conducted by IBM, the early adopters have already seen positive outcomes from their cognitive initiatives — from better customer engagement to product innovation and business growth.
These early adopters leverage various cognitive capabilities, ranging from machine learning and natural language processing to unlocking values from different data sources, both structured and unstructured. Some companies develop custom platforms, while others utilize APIs to deploy cognitive technology into their systems.
IBM investigated over 600 early adopters who put cognitive technology to work. Their report shows that cognitive capabilities play an important role in the company’s growth. About 65% of early adopters believe cognitive technology is crucial to their business strategy and success, while 58% say it is an essential part of their company’s digital transformation.
The New Era of Analytics
Since we are entering an era where everything is more likely to be connected, businesses will require cognitive analytics and tools to maximize their growth and adapt themselves according to the customers’ needs.
More specifically, cognitive tools can help companies truly understand customer behavior patterns, identify root causes of customer dissatisfaction, and find innovative ways to enhance customer experiences. Cognitive analytics can also be a great fit in optimizing operational costs, increasing customer retention, and decreasing resolution time significantly.
Real-Life Examples of Cognitive Analytics
Cognitive analytics is now being used in almost every industry to identify patterns, potential threats, and opportunities for improvements. For example,
Banking and Finance
Cognitive computing can radically change the banking and finance sector. Unlike conventional systems that model customer behavior based on past actions and treat all customers the same, cognitive systems make more precise and timely analyses of individuals based on their age, income, and transactions.
Cognitive analytics can help banks customize produces and services for each customer, intelligently target high-net-worth individuals, offer better investment plans, and apply proficient knowledge to solve issues quickly.
All in all, this emerging computing paradigm can be applied in three areas:
- Customer service: Since a cognitive system understands the problem, it can provide accurate solutions without redirecting customers to different departments and thus saving everyone’s time.
- Decision-making and advisory: It can enhance the loan underwriting process, which requires a deeper understanding of businesses to make data-driven decisions.
- Investment consulting: Cognitive systems allow banks and non-banking financial sectors to quickly go through investment sources to explore the latest market trends, evaluate risks, and provide up-to-date advice to customers.
Artificial intelligence and deep neural networks make it easier for cognitive systems to continuously extract data from various sources and acquire knowledge through advanced analytics. The neural network improves itself with every iteration, allowing the system to learn to predict threats and create proactive solutions.
With increasing cyberattacks and a shortage of skilled cybersecurity specialists, a tool like cognitive analytics works wonders. It can process and analyze massive amounts of structured and unstructured data (to identify various types of threats and risks) that would be next to impossible for a traditional analytics system.
The healthcare-related information (such as radiology images, lab results, pathology reports, electronic health records, clinical research, and more) is difficult to analyze and share because it is fragmented. This is where cognitive systems come in handy. They can smartly and quickly parse through different types of information to help healthcare professionals.
The insights offered by cognitive analytics make it easier for healthcare providers to understand the health of individual patients, make informed decisions, and provide more personalized care.
Today, cognitive computing is used in numerous oncology centers worldwide. In MD Anderson (Houston) and Memorial Sloan Kettering (NYC), for example, cognitive analytics is used to assess petabytes of information (including published clinical studies, clinical trial data, and best practice data) to make diagnoses and treatment decisions.
Power and Energy
The oil and gas industry continuously faces challenges locating, refining, and distributing cruise oil and its byproducts. With cognitive analytics, technical professionals and skilled engineers can make important decisions like which location to explore, how to allocate resources, what products should be produced first, and how to make existing facilities more efficient.
Cognitive platforms are already being utilized by many utility companies in the United States, such as Mega Energy, Arizona Public Service Electric, Gexa Energy, Portland General Electric, and Avangrid. These systems provide several benefits, from forecasting loads to mapping rate structures against individual cost models.
Logistics and Retail
Cognitive analytics is useful at every stage of the Logistics and Supply chain, from warehouse management and automation to packaging and palletization. It analyzes data from both internal (inventory, point of sale) and external (market trends, weather) ecosystems. The aim is to improve supply chain efficiency, minimize cost, and provide valuable insights to businesses so they can respond quickly to the dynamically changing scenarios.
Cognitive analytics also plays a crucial role in the retail industry. It can be integrated with eCommerce platforms to extract data about customers and their buying behavior. For offline stores, cognitive systems can help retailers optimize inventories and provide more personalized products at attractive prices.
Education and Learning
Cognitive computing can yield better results for students and educators. It can identify underperforming schools and teachers, predict student performance in specific disciplines, and provide personalized study material.
In 2003, the Miami-Dade County public schools in Florida adopted a Cognitive Tutor Software to provide feedback to students as they are working through problems. It has been found that this software is far more effective than the conventional curriculum alone.
Well-Known Cognitive Analytics Tools
Now that you know the real-life applications of cognitive analytics, let’s explore different kinds of cognitive tools used in various fields.
OpenSMILE (Open-source Speech and Music Interpretation by Large-space Extraction) automatically extracts features from audio signals and classifies speech and music signals. It is widely used for speech recognition, emotion recognition, speaker identification, chord detection, and beat tracking. It can also detect the speaker’s states, such as intoxication, depression, or vocal pathological disorders.
Since the software program is mostly written in C++, it runs on all platforms smoothly, including embedded platforms like macOS, iOS, Windows, Android, Linux, ad Raspberry Pi.
2. IBM Watson
Watson applies more than 100 different techniques to analyze natural language, find sources, create hypotheses, score evidence, and combine sand ranks hypotheses. Basically, it applies advanced natural language processing, advanced reasoning, knowledge representation, and machine learning algorithms to the field of open domain question answering.
With Watson, businesses get access to a complete portfolio of AI capabilities, whether it’s tools for detecting emerging trends or developing applications for customers. It saves time and automates more processes, so employees can focus on growth and new opportunities.
NuPIC (Numenta Platform for Intelligent Computing) is an open-source AI platform that implements HTM (Hierarchical Temporal Memory) learning algorithms. It is suited for a wide range of problems, especially anomaly detection and prediction of streaming data sources.
4. Iris by Apixio
The United States produces more than 1.3 billion clinical care documents annually; about 80% of this data is unstructured and difficult to access. That’s where Iris comes in handy. It’s a cognitive computing platform that extracts valuable insights from vast amounts of clinical data and other information in the health system.
Iris’s analytics engine utilizes machine learning and natural language processing technologies to generate patient profiles and evaluate risks. It can group similar patients and cases to provide evidence-based patient data. And since data changes over time, the platform continuously monitors the patients’ information to tell healthcare professionals what works and what doesn’t.
5. Aila by Enterra Solutions
Aila combines advanced mathematics and artificial intelligence to reason and solve problems in a way similar to humans. It can analyze the company’s internal and external data, draw inferences and produce valuable insights on-demand. Unlike traditional business analytics, Aila can handle the high-volume, multivariate world of Big Data.
It can answer some of the trickiest questions —
- Are there opportunities to shift expenditure next year within market segments to improve ROI?
- Are any of our clients’ service-level objectives at risk? If yes, how can I mitigate that risk?
6. Ignio Cheetah by TCS
Ignio Cheetah is a cognitive automation product suite for IT operations and workload management. It can instantly detect and predict abnormal IT conditions that need attention, reduce false alerts, gather similar or correlated events, and prioritize alerts based on business impact. And since the system learns from its own experience, it becomes better with time.
Ignio is already being used by many large corporations, including Fortune 500 and Global 2000 companies, which are innovators and leaders in their respective fields. The platform manages more than 1.5 million technology resources autonomously for 50+ clients.
7. Cortex Certifai
Cortex Certifai ensures that the machine learning models are free of algorithmic biases to reduce skewed decision-making and reasoning. It generates a composite trust score, called AI Trust Index, that measures automated models based on data quality, performance, robustness, fairness, explainability, and compliance.
This Trust Index helps businesses evaluate the tradeoffs and usual contention between performance and risk. Stakeholders can dig into every evaluation parameter to find potential improvements. It can be applied to any black-box model, including statistical models, predictive models, and machine learning models.
8. SparkCognition EPP
SparkCognition EPP uses proprietary AI and machine learning algorithms to prevent never-before-seen cyberattacks from disrupting business operations and identify phishing threats from PDFs, documents, and direct-to-memory PowerShell attacks. Its lightweight cognitive agent learns the typical behavioral patterns of all endpoints within a company and alerts the security team about potential attacks.
The system integrates well within existing security architecture, providing seamless operations in the background for up to 100,000 managed endpoints running macOS, Linux, or Windows. It has been proven to outperform conventional and next-gen endpoint protection providers.
Cognitive computing has attracted a lot of data scientists and researchers over the past decade. The recent studies include:
Detecting psychophysiological parameters for a cognitive-emotional conflict
Russian researchers have developed a platform to identify remotely diagnosed parameters (using video messaging tools such as Skype), which can objectively show the presence of a cognitive-emotional conflict during a dialogue. This platform uses neural networks to automatically mark people’s emotional reactions in the video data.
An ACT-R computational cognitive model
Although the mental rotation ability is a crucial spatial reasoning skill in human cognition, very little is known about how it works when playing video games. Italian researchers have modeled mental rotation (via an ACT-R-based cognitive model) to analyze whether and under what conditions such ability is used in the TetrisTM game. The outcomes show significant insights into the activation of mental rotation during game dynamics.
Cognitive Analytics for anomaly detection
The scientists at the Centre for Research and Technology Hellas, one of the largest research centers in Greece, have developed a cognitive analytics platform that can process, analyze, and exploit resourceful machine data to support the growing requirements of the manufacturing industry. It is capable of monitoring complex anomalies and handling unforeseen conditions. Plus, its user interface utilizes advanced visualization techniques to present information in an effective manner.
What’s the future of Cognitive Analytics?
According to the Research and Markets report, the cognitive analytics market size will reach $33 billion by 2027, growing at a compounded annual rate (CAGR) of 37%. China is projected to reach a market size of $5.5 billion by 2027. Among the other noteworthy geographic markets are Japan, Canada, and Germany, each forecast to grow at 33%, 32%, and 25% over the 2020-2027 period.
The market’s primary growth drivers include the adoption of big data and cloud computing technologies, as well as the introduction of sophisticated analytics techniques that are boosting the demand for efficient cognitive analytics solutions.
Frequently Asked Questions
How many types of analytics are there?
Modern data analytics can be divided into five categories:
- Descriptive analytics: is the simplest form of analytics that describes what happened and what is currently happening.
- Diagnostic analytics: takes one step further by answering why did it happen. It compares coexisting trends and determines relationships between variables where possible.
- Predictive analytics: forecasts future trends, answering what might happen in the near future. It analyzes past and current data to make predictions about future trends.
- Prescriptive analytics: takes various factors into account and suggests actionable takeaways. It helps users make data-driven decisions.
- Cognitive analytics: combines the power of statistics, artificial intelligence, and machine learning to process massive amounts of data and make sense of context. It presents new opportunities to mine untapped data sources, discover intricate patterns, and provide personalized services.
Why would market analysts use cognitive analytics?
As more and more devices are generating data every second, it is becoming increasingly challenging to collect, process, and analyze all the information stored across the servers and beyond.
Cognitive analytics addresses these challenges: It utilizes advanced statistics and AI models to identify hidden patterns within massive volumes of diverse data. Unlike conventional analytics, which extracts information from structured data in specific domains, cognitive analytics takes a wider and more flexible approach to data discovery. It operates in a similar way to humans to interpret data, understand its context, and uncover its patterns and trends.
What’s the difference between Cognitive Computing and AI?
Artificial Intelligence consists of algorithms that are trained to provide accurate results. These algorithms learn from continuously changing data and self-correction methods to make smart decisions.
Cognitive computing, on the other hand, mimics human behavior and adapts to human reasoning. It tries to solve complex problems the same way humans would solve them. In contrast, an AI model tries to find new ways to solve problems that could be better than humans.