So far in this blog series, I have identified the driving factors for cognitive computing and the differences between artificial intelligence and cognitive. Having a better grasp of cognitive computing context is valuable, but let’s focus on how you can apply it to your end-customers for solving their data-driven issues. Who or what would benefit from cognitive technologies?
When business analyst or research scientist is too limiting
The terms “data science” and “data scientist” have been used for quite some time, but they've really taken off in the last two years. Companies are now hiring for data scientists, and entire conferences are run under the name of data science. But despite the widespread adoption, data scientists can really be described with traditional terms like “statistician,” “quant,” or “data analyst.” Data science is just a newly coined term because business analyst and research scientist are too limiting of descriptors for this type of work. To put it to scale, data science is where computer science meets statistics forming something new.
Data science is a combination of computer hacking, data analysis, and problem solving. It runs the gamut from data collection and managing, through application of statistics and machine learning and related techniques, to the interpretation, communication, and visualization of the results.
Having the right mix, matters
Performing data science might involve utilizing cognitive technologies or artificial intelligence, but the terms are not interchangeable. A data scientist will use neural networks, machine learning, and algorithms to allow them to do more problem solving and analysis on types of data that could not be done without cognitive computing technology. For example, a data scientist might use cognitive to analyze unstructured social media data then plug the results of that analysis into a statistical model to produce predictive or prescriptive analytics to help a business make better decisions.
With this de-mystifying blog series I have walked you through the driving factors of cognitive computing, the difference of artificial intelligence and cognitive and now the true meaning of data science. Your mind now should be clear regarding these hot topics in the industry and the noise hopefully has quieted down.
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