This is a topic that I had several discussions on with colleagues of mine as well as friends. With co-pilot, and directions of autoML, 1-liner libraries to run algorithms, what should be the value of data-scientist? TLDR: Personally, I feel that the value of a data-scientist should not be the ability to "write" just the latest machine learning API, but more towards critical thinking skills, problem solving abilities (generic), problem formulation as well as communication. I will try to touch on them more and relate them to personal experiences.
Every possible organization that one can think of relies on data to achieve the set objectives. On that note, having access to data that isn't smart enough to get goals accomplished poses a hurdle. It is thus important to have data that is transformed in a manner that can cater to the needs and objectives of the organization. With most organizations relying on Artificial Intelligence (AI) and machine learning, the necessity of dealing with the right data is all the more important for the sole reason that the models employed aim at obtaining meaningful insights. No wonder data is vast and one shouldn't ideally fall short of it while aiming at the objectives.
Let's have an insight into the democratization of AI and its pros and cons Internet is everywhere, anyone from anywhere could access it and learn many things indeed. The same applies to Artificial Intelligence (AI) as well. Anyone with access to the internet could learn and explore the realms of AI without depending on an external factor like a course or maybe a degree. Anyone who has a spark to learn AI in and out could do it just with the readily available sources. This is the exact concept of the Democratization of Artificial Intelligence.
In the United States, an estimated 114,000 people were waiting for organ transplants, and only 30% of those got their organs on time in 2019. According to Kaiser Health News and Reveal from the Center of Investigative Reporting, nearly 170 organs could not be transplanted. Almost 370 endured near misses with delays of two hours or more because of transportation problems. According to the American Transplant Foundation, 20 people die each day because they do not receive their lifesaving organs in time, making the number of annual deaths greater than 40,000. Patti Niles is CEO of Southwest Transplant Alliance, a non-profit organ procurement organization.
Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.
Capturing big data is easy. What's difficult is to corral, tag, govern, and utilize it. NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud. Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.
I've already mentioned data catalogs as one strategic tool. By necessity, they're provisioned by IT and data management teams, who know how to work with the various features in data catalog software and how to set up and deploy them. We can make a useful distinction between tools provisioned in this way by IT and tools adopted by end users. Both have an important role to play in a data strategy, complementing rather than contradicting each other. Data management tools are almost always the domain of IT.