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.
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.
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