Big Data 2.0: Cognitive analytics and why it matters

Big Data has been all the rage in recent years as businesses have come to realize that data is a commodity much like gold or silver. Data-driven insights are providing businesses with new competitive advantages, enabling them to gain richer insights into their customers and thus to innovate and add new products and services quicker than ever before. No longer the domain of high-end enterprises with large budgets, businesses of all size are now able to use a wide range of open source tools and resources to spin new insights on all the data at their disposal.




In connection with the enormous increase in Big Data, new developments in emerging technologies like machine learning, artificial intelligence, and cognitive computing (think IBM Watson) have also brought us more sophisticated ways to mine all of this data. The new field that has emerged as a result of these latest advances is known as Cognitive Analytics and it’s proving to shake up and redefine Big Data in amazing new ways.

Here are some reasons why anyone who is currently spending a lot of time thinking about Big Data and related trends, or moving the needle on their strategy, needs to give attention to Cognitive Analytics.

1. Cognitive analytics represents the new generation of Big Data insights

Cognitive analytics is defined by one source as a new approach to information discovery and decision making “inspired by how the human brain processes information, draws conclusions, and codifies instincts and experience into learning.” In other words, Cognitive analytics extends the capabilities offered by the first generation of Big Data tools and leads to more practical decision-making, which retailers, healthcare providers, consumer goods providers, and financial service leaders (to name a few) will demand in increasing numbers.

2. Cognitive analytics represents a new paradigm in computing

Cognitive analytics is really an extension of the new field of cognitive computing, or the science of developing computers that can “think” and learn without pre-programmed instructions. Cognitive computing too represents an intersection between machine learning, natural language processing, and advances in processing power and storage networks delivered at low costs.

3. Cognitive analytics is driving artificial personal assistants

The newest personal assistants on the market such as Apple’s Siri, Google Now, and Microsoft Cortana use natural language processing, predictive analytics, machine learning, and big data to provide instant, context-based information ranging from the nearest dentist to top restaurants, and much more. All of these capabilities are integrally tied in to cognitive analytics, which will turn into a critical point of competitive advantage for businesses of all sizes in the months and years to come.




4. Cognitive analytics will be among the technologies that disrupt the future of jobs

Research shows that many CEOs are underestimating the systemic and deep impact that “smart machines” (devices using cognitive computing systems to make decisions and solve problems without human intervention) will have through 2020, as well as the potential for them to replace millions of middle-class jobs in the decades to come. Even more sobering, some experts suggest that the impacts of machine learning will extend to the C-suite as well. Cognitive analytics is integrally related to these technologies, and will play a major role in fueling the automation revolution.

5. Cognitive analytics will create new learning opportunities

IT and business professionals will need to keep their jobs skills relevant and updated through the pursuit of competencies and cognitive tasks that keep up with the growing trends represented by cognitive analytics. This means employees should start training in the development of higher order skills such as coding, statistics, visualization, linguistics, information management, cognitive computing, and Big Data.

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