As businesses continue seeking new ways to monetize data, cognitive computing will play an increasingly important role. Cognitive computing has the potential to transform the way we view data analytics. Cognitive computing is a blend of artificial intelligence (AI), neuro-linguistic programming (NLP), machine-learning algorithms, ontology, data ingestion and data lakes.

To a data management practitioner, cognitive computing is about gaining insights from data-centric platforms and applications, and blending them with artificial intelligence, which effectively mirrors a human’s ability to identify trends and draw powerful insights.

With today’s powerful processors, storage capacity and the increasing sophistication of artificial intelligence technologies, cognitive computing is poised to revolutionize data analytics.

To better understand cognitive computing and its potential impact on data analytics, it is first important to acknowledge the drivers of cognitive computing, applications necessary to be successful and, ultimately, the biggest challenges to developing these applications.

What drives cognitive computing applications?

There are four drivers leading the way for cognitive computing. The first is the volume and growth of web pages and applications. In 2016 alone, hundreds of thousands of web pages and applications were developed and that number is growing daily.

The second driver is data that has been derived from intelligence devices, which is then used in the development of cognitive computing applications.

The third driver is cognitive computing’s appeal for analytics because data is growing more complex by the second.

Lastly, the final driver of cognitive computing is evolving industries like healthcare and life sciences. Experts in these fields are pushing for cognitive computing solutions that can help them better understand complex medical diagnoses. Rather than trying to keep up with all the published articles and research studies, researchers need to be able to make decisions quickly based on data analytics solutions.

What are the challenges facing the development of cognitive computing?

In the development of cognitive computing applications, it is inevitable there will be a learning curve. The biggest challenge facing cognitive computing will be training the applications or systems to look at unclear, raw data elements and identify relationships among them. Recognizing these data trends is an essential step to create useful, actionable insights.

What do cognitive computing applications need to be successful?

For the development of cognitive computing applications to be effective, there are four primary elements that will lead to success.

1) The first and most important element is to have a full understanding of the problem we are trying to solve.

2) Next, it is essential to fight the tendency to jump into big data and data lakes. We must define a domain-specific problem, formulate a hypothesis and analyze a small amount of data that is specific to the problem we initially identified.

3) Third, we must utilize consistently reliable data to analyze patterns. As with any data analytics project, the reliability and quality of the data used is key to success.

4) Last but not least, it is important to understand the connections between data elements of data-centric applications, as well as the volume and variety of data being used.

While we are still in the early stages of developing cognitive computing applications to help monetize data, it is important remember the core elements needed to succeed in any technical endeavor: Finding a specific domain, centering on a specific focus, and establishing specific goals. Unless we identify the problem we are trying to solve, the best technology in the world will not lead us to a solution.

(About the author: Prakash Mishra is senior director and offshore (India) practice head, enterprise data architecture, at NTT Data)

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