Cloud Computing

Everything You Need to Know About Cognitive Data Management

Aishwarya Banik

All about cognitive data that integrates several applications to offer context and unearth answers

Everyone searches for an answer to the concept of cognitive analytics as well as the definition of intelligent technologies. The fact that artificial intelligence was still in its infancy and that there was yet much more to come was understood by all IT experts. And that's exactly what happened once cognitive analytics were included. This technology was developed primarily to connect all data sources to a platform for analytical processors. It's incredible how all forms of data are considered in the context of cognitive analytics. After reviewing the foundations, let's delve further into the various cognitive analytics components.

What is cognitive analytics/ data management?

Cognitive analytics is analytics with human-like intelligence. This might entail understanding the context and meaning of a statement or, given a lot of information, picking out certain objects in an image. Since cognitive analytics typically combines machine learning and artificial intelligence technology, a cognitive application can improve over time. Cognitive analytics can find connections and patterns that simple analytics cannot. Cognitive analytics may be used by a business to monitor changes in customer behavior and emerging trends. By using this technique, the business may predict future outcomes and modify its objectives to perform better. Certain components of cognitive analytics are included in predictive analytics, which makes predictions using data from business intelligence.

Fundaments of cognitive analytics

Analytics is only computerized data analysis, whereas cognitive refers to a collection of mental processes carried out by the brain. Since it is linked to the human mind, cognition is nothing more than the application of intellect, which is comparable to human intelligence. To calculate various sorts of data, this mixes deep learning, semantics, machine learning, and artificial intelligence. Making sense of the data, which is frequently unstructured and dispersed internationally, is one of the most important challenges that organizations confront on a global scale. We have cognitive computing because it is practically impossible for a human brain to process such a vast amount of data.

We come to data analytics as a result of these findings, which also includes descriptive analytics. As we already know, prescriptive analytics and predictive analytics are both 10 years old. Many intelligent technologies that are used today have benefited from these technologies. The Artificial Intelligence Conference, held in 1956 at Dartmouth College, had a huge impact on how people understood the importance of modern technologies like cognitive analytics.

The IDG article "Big Data and Analytics: Insights into initiatives driving data investments, 2015" found that organizations using data-enabled projects were found to be heavily dependent on sources of unstructured information like emails, transaction records, customer databases, documents prepared in MS Word, and other such worksheets. Unstructured data might also come from open-source sources like social media posts, census data, and patent records. Consequently, it was inevitable that complex technology like cognitive analytics would be employed. The cost of not handling this unstructured data is very significant, therefore many firms can afford the cost-effective tools and apps that make use of cognitive analytics technologies today.

Market Overview

The market for cognitive data management is expected to grow at a CAGR of 21.7 percent from 2021 to 2026. The rising usage of IoT-based devices, an increase in the volume of complex data, and the advancement of cutting-edge technologies like AI have all led to a rise in the acceptability of cognitive data management on a global scale. A recent Infosys Limited assessment found that AI and Cognitive Data Management are components of a real-world technology tsunami that is having discernible consequences on the business. According to the poll, 86 percent of organizations have "middle" or "late-stage" AI installations and view it as a key tool for their next business objectives. Nearly 75% of respondents claimed that AI had already altered how their companies do business.

  • Cloud data traffic and data creation have increased dramatically as a result of enterprises' faster use of automation and cloud technology. In turn, this increased demand for cognitive data management by fueling the rise of data management software. Additionally, over 90% of enterprises that use the cloud in their operations said that internet traffic has been increasing dramatically.
  • Global IP traffic will increase from an annual run rate of 870.3 exabytes in 2015 to 2.3 zettabytes in 2020, according to a Cisco report. Furthermore, the increased use of cloud technologies by organizations will cause the IP traffic to increase three times between 2015 and 2020, at a compound annual growth rate of 22%.
  • The development of IoT-based technologies is also increasing the volume of digital data, underscoring the necessity of cognitive data management. Cisco estimates that by 2019, IoT will produce roughly 507.5 zettabytes of data (1 zettabyte is equal to 1 trillion gigabytes). Data management, screening, and interpretation are believed to be extremely difficult tasks for corporations. If IoT projects are to give insights to promote improved productivity and profitability, cognitive data analysis will be crucial.
  • In the worldwide cognitive data management market, it is anticipated that the cloud deployment type with a wide variety of functions, including a pay-per-use model, ease of accessibility, and multi-user support, will account for a sizeable market share. The advantages that cloud deployment provides are causing its acceptance to advance more quickly. Forbes predicts that by 2020, the demand for cloud computing would increase to USD 160 billion, growing at a rate of 19 percent.
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