Over the years, data analytics has evolved from diagnostics to predictive to prescriptive, and the next major paradigm is more likely to be geared towards cognitive analytics. It aims to harness the power of high-performance processors and artificial intelligence algorithms to reveal complex patterns hidden in massive volumes of data. In fact, the cognitive analytics market is expected to grow from $11918.12 million to $83599.41 million by 2028, growing at an annual growth rate of 38.36% with growing demand for AI solutions. The blog explores why businesses should tap into cognitive data analytics, business outcomes, along with real-world use cases.
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Cognitive analytics is an intelligent technology that combines artificial intelligence, deep learning neural networks, machine learning, and Natural Language Processing (NLP) to simulate human sense processing. It applies these technologies to process large volumes of structured and unstructured data from diverse sources and recognizes complex patterns using algorithms for insights, decision-making, and context-based hypotheses.
Cognitive computing goes beyond traditional analytics tools to leverage techniques such as pattern recognition, natural language, and “human sense” processing to simulate human thought processing. As demand for real-time decision-support systems in business decision-making intensifies, cognitive analytics has moved to the forefront in high-stakes sectors such as healthcare, financial services, supply chain, and cyber security.
It is used to bridge the important gap between large volumes of information and the need to make decisions in real-time. Cognitive analytics uses various technologies like ML, NLP, and others, its process follows an organized pattern common sequence:
It runs a search across the entire knowledge base of structured and unstructured data that is gathered from complex and diverse data sources such as IoT, sensors, equipment, and others to navigate real-time data.
The data, once collected, needs to be cleansed, organized, and prepared for analysis. This step ensures that data is in a format that is suitable for processing and also involves accumulating and integrating the data with existing or historical data.
The next step involves data engineering using AI and ML algorithms to identify patterns, correlations, and anomalies if there is any error in the data. New variables and features are created within the data to enhance its quality and relevance.
An appropriate cognitive analytics model is chosen depending on the nature of the problem. This could involve machine learning algorithms, deep learning networks, or natural language processing techniques.
The next step is to train the selected cognitive data analysis model using the gathered data. The cognitive model learns the patterns and contextual relationships within the data, allowing it to make predictions or generate insights.
Next, it’s time to evaluate algorithm’s performance using a new dataset. This helps ensure the model is accurate and can generalize well to new, unseen data. If it is found to be performing well, it is deployed into a production environment, and the model is available to users so that they can use it to generate insights from their data in real-time.
Cognitive analytics system’s self-learning capability is one of its core elements and as it processes more data, it refines its algorithms, becoming more accurate and efficient over time. Cognitive analytics systems follow this process to uncover valuable patterns, generate insights, and assist in making data-driven decisions across various domains and industries.
A major reason for adopting cognitive data analytics was enterprises’ difficulty and challenges to deal with traditional data analytics tools. These tools and their involved methods were restricted in their ability to process complex and unstructured data, identify patterns and trends that are invisible to the human eye, automate manual tasks, and provide insights in real-time.
Moreover, creating algorithms to search through and process vast amounts of structured and unstructured data required a mandatory methodology apart from significant investment in resources and infrastructure, enterprises began to use cognitive computing data analytics to bridge machine learning and data mining.
Thus, among the major reasons why organizations are turning to cognitive analytics is its ability to analyze emerging patterns, spot business opportunities and handle critical process-centric issues – all in real time. It helps enable personalized recommendations, reduce business costs and streamline workflows.
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Cognitive analytics pushes the boundaries of traditional business practices and allows businesses to leverage a range of capabilities to unlock value from a range of data sources, both structured and unstructured. Here are some of the areas where early adopters are using cognitive data analytics’ capabilities to accelerate business outcomes, from increased customer engagement to improved productivity and efficiency to rapid business growth.
Cognitive analytics allows you to better understand your customers and their needs to deliver experiences tailored to their specific needs. As a result of offering personalized product recommendations, tailored marketing messages, and or responding quickly to resolve customer issues, customers show higher engagement and loyalty – which are key to long-term business growth.
Businesses can leverage cognitive data analysis to add intelligence to the growing volume and velocity of data on customer behavior, demographics, and purchase history. The ability to quickly probe volumes of data to find actionable insights through reasoning, predicting, and providing recommendations can speed up discovery and help boost decision-making to reach and acquire customers.
Another significant positive impact of cognitive analytics is real-time visibility and intelligence, as it greatly changes the strategic conversations that the sales and marketing teams have about the prospects and customers. The sales approach shifts from taking a historical view or making decisions based on assumption to one that is focused on proactively monitoring the market. Cognitive analytics can significantly help companies improve customer experience by identifying customer needs, translating into higher engagement and acquisitions.
Cognitive analytics combines several intelligent technologies such as cognitive computing, AI, ML, NLP, and deep learning to automate and apply human-like intelligence to routine and time-consuming tasks. With its self-learning capabilities to draw futuristic inferences and insights from existing data and patterns to get smarter and more effective over-time by learning from its interactions with data and humans. It can be used to optimize efficiency across operations in business, from supply chain, sales processes to customer service operations. This leads to significant improvements in productivity and efficiency.
Cognitive analytics allows businesses to combine data across public and private content, such as financial reports, forecasts, ratings, and financial histories, with in-house subject matter expertise to generate breakthrough approaches to assess, manage and price risk.
This enables sectors, especially those that are data-intense such as financial institutions, to identify and assess wide range of risks, including financial risks, operational risks, and strategic risks. Using cognitive computing and machine learning technologies, cognitive analytics can even predict the likelihood and potential impact of these risks to the business in today’s volatile risk landscape. Cognitive analytics can then render insights, risk mitigation strategies and data-driven recommendations far faster than humans can do to reduce the risk exposure of the business. It thus helps financial institutions increase market agility to meet risks, improve security and compliance while reducing risks.
Cognitive analytics enables businesses to identify trends, patterns, and correlations that enable better decision-making and planning. It helps analyze and identify different customer segments based on their demographics, preferences, and behaviors, which helps develop targeted marketing campaigns, products, and services. Armed with a better understanding of the needs of individual customers, companies can create personalized customer experiences and, identify unmet needs new opportunities and develop new products or services. Thus, with cognitive analytics, businesses can do more than improve productivity.
We have already covered the depth and breadth of cognitive analytics; let us walk you through real-life use cases and examples to understand the benefits of implementing cognitive computing efficiently.
In the banking and finance sector, including banks, insurance providers, financial intelligence units, and credit agencies, companies commonly use cognitive analytics in key areas such as,
One of the most significant uses of cognitive analytics in healthcare is the ability to provide better patient care. Other common use cases include drug discovery and disease diagnosis.
To track the customer’s behavior, expectations and experiences cognitive technologies is transforming the retail sector for both online and offline establishments.
Let’s have a look at cognitive analytics examples of industry players who have early adopted and implemented it successfully leverage its capabilities.
KPMG uses AI-driven azure cognitive services to forecast financial trends, identify potential risks, and optimize strategies for growth. Apart from using AI-powered audit tools to improve audit process’s efficiency, it used cognitive analytics for risk management and automated tax compliance.
RBC, Canada’s largest bank, leverages AI, ML and cognitive analytics through NOMI Forecast to enhance client’s experience and enable them to safely manage their money. Powered by AI models, the app uses deep learning and cognitive analytics to deliver personalized insights for RBC clients. In addition to the award-winning Nomi Forecast, RBC also offers a suite if cutting-edge AI solutions including NOMI Insights, NOMI Budgets and NOMI Find & Save.
Subtle Medical leverages feature cutting-edge deep learning algorithms that improve the efficiency of image acquisition while elevating the patient experience. The use of cognitive analytics enables up to 60% faster PET and MRI scan times, drastically improving the hospitals’ productivity.
As an early adopter of AI, USAA uses cognitive analytics to find patterns that indicate major life events and designs end-to-end service offerings around these major life events. It further uses MLOps to increase automation and curate personalized experiences for customers.
Rishabh Software offers cognitive analytics consulting to enable businesses to build intelligent AI solutions that accelerate digital transformation. We take pride in providing result-oriented cognitive data analytics services that leverage the latest in AI, ML, NLP, cognitive computing to fulfill your business needs. As a Microsoft Gold partner, we offer your business easy access to the latest cognitive computing tools within the Microsoft ecosystem through our cognitive data analytics offerings.
Our team of highly skilled and experienced AI engineers offer data engineering services to help you transform data into actionable analytics. We have a proven track record of offering AI-powered data analytics solutions that use ML and deep learning algorithms. We have successfully served diverse sectors such as Healthcare, life science, fintech, and manufacturing.
We will help you successfully deploy the right solution across different levels of your organization to leverage a competitive advantage.
A: To adopt cognitive analytics successfully, businesses can follow a structured approach:
A: Cognitive analytics, while promising transformative insights, faces several challenges that organizations must navigate:
A: Cognitive analytics sets itself apart from traditional data analytics in several ways:
Handling Unstructured Data
Detecting Complex Patterns
Generating Insights and Predictions:
A: Here are some of the top cognitive analytics tools, categorized by the type of NLP they use:
Other cognitive analytics tools
Although the choice of cognitive analytics tool depends on your specific needs and requirements, OpenNLP and GATE are two of the most popular open-source NLP tools.