IBM Watson and Cognitive Data as the Future of Information Data Systems

IBM Watson is slowly becoming an important piece about IBM’s vision of the future of computing. A few weeks ago, big blue announced that is launching another business unit centered on Watson solutions. The investment in this new unit is estimated to be around $1B but, more importantly, it reinforces IBM’s commitment to Watson and cognitive data as the future of enterprise data solutions.

A lot has happened since Watson made news winning the television quiz show Jeopardy by beating legends Ken Jennings and Brad Rutter. At the time, Watson was a sophisticated natural language processing machine but didn’t have a lot to offer in other areas of cognitive computing.

Since the jeopardy days, Watson has added a significant number of services in areas such as data insights, vision processing, image recognition, natural language processing, text analytics and other important areas of cognitive science. More importantly, IBM is making Watson available through the series of APIs via the Watson Developer Cloud which allow developers to leverage Watson is third party applications.

IBM’s efforts around Watson are, undoubtedly, the most important steps to establish cognitive data as a mainstream trend in the technology arena. While big data technologies have certainly disrupted the information management space, data processing applications remain mostly ignorant when comes to understanding and reasoning through the data they store. This is where cognitive data becomes important by helping expert systems enhance, understand and reason through structured and unstructured data sets in order to make intelligent decisions.

5 Reasons Why Cognitive Data is the Future of Enterprise Data

Modern data is becoming more contextual every day. While data sets can be considered static in nature, they have different interpretations depending on contextual aspects such as time, location, environmental aspects, etc. Cognitive computing is a necessary step to make information systems more context aware by augmenting static data sources with dynamic contextual data and reason and learn from it.

Big Data is Just a Lot of Dumb Data

Today, big data systems are becoming an important element of software systems by storing large amounts of static data. Despite the advances in data storage and process, data systems remain essentially unintelligent when comes to understand, optimize, augment and reason through the data they store. In that sense, organizations are constantly building new systems to make data “more intelligent”. Cognitive data presents a powerful alternative to traditional data systems by providing a layer of intelligence to modern information systems.

Data Scientists are not for all Scenarios

Data scientists are the most common answer when comes to gather insights about specific data sets. However, data scientists are fundamentally inefficient in areas such as real time vision analysis, image recognition, speech analysis and other fundamental aspects of cognitive systems. Cognitive data and platforms like IBM Watson will help to expand the capabilities of traditional data science to provide more sophisticated intelligence over traditional data sources.

Video, Images, Text and Speech are Becoming Increasingly Important

Complementing the previous point, data signals such as video, text, images and speech are fundamentally difficult to process by traditional data systems. Platforms like IBM Watson and other cognitive data solutions excel at the understanding and processing of these type of data points making an ideal extension of traditional data systems

Actions are as important as Data Insights

In modern data systems, actions related to the data are typically hardcoded as a bunch of rules within an applications. However, automatically taking actions based on data insights is becoming an increasingly important aspect of modern applications. Cognitive data is a fundamental step towards enabling intelligent decision making based on data insights on software applications.

5 Cognitive Data Scenarios Relevant in Today’s Enterprise

Cognitive science is starting to revolutionize healthcare. The intelligent processing of unstructured healthcare data such as images, videos, speech etc is leading the charge in modern healthcare applications ranging from treatment recommendations to decease pattern analysis. Not surprisingly, healthcare remains the number one vertical for IBM Watson applications.

Public Safety

Cognitive data can help better reason through real data points in the form of video, images, sounds and text commonly encountered in public safety scenarios. Using cognitive data systems, public safety operators can improve their decision making process by interacting with systems that will help them reason through contextual data in their environments.


From fraud detection to financial package recommendations, cognitive data is increasingly becoming relevant in financial systems. Reasoning through large amounts of semi-structured and unstructured data, cognitive data systems can help improve financial decisions such as trading, fraud analysis, etc.


Cognitive data is a key element of the future of recommendation systems and other user engagement marketing processes. Rapidly reasoning through the text on an email or the tone on a phone call, will help organizations to recommend better products to their customers while also enhancing the understanding of their marketing data.


This is an obvious one. Cognitive data will be essential to improve defense operations by better reasoning through the millions of data signals collected by soldiers and equipment on the field. Additional, cognitive science will help to build more intelligent defense equipment such as drones or robots that are becoming an integral part of modern warfare.

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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