Advanced Analytics

7 AI and Big Data Myths You Should Not Fall for

Story Highlights
  • AI Does Not Always Need a Massive Database
  • You Do Not Need AI Always
  • Advanced Analytics and AI Aren’t Synonymous
  • Too Much Data Can Bias AI Models
  • Tools Already Have Built-in AI
  • Employees Are the AI Foundation
  • AI Is Not Always the Solution

It is easy to believe big data myths because the field is still new to us. According to Pace Harmon director JP Baritugo, “Big data is the fuel, and AI is the means”. To leverage these technologies, organizations often fall prey to preconceived notions. In this article at the Enterprisers Project, Stephanie Overby warns of seven such AI and big data myths that you should avoid.

Checking Off Big Data Myths

Talend senior director Jean-Michel Fargo observes that you cannot just ‘scratch the surface’ when big data comes into play. Also, artificial intelligence allows us to think beyond our limitations. So, when you talk about one, it is natural to think about the other. This is where you must pause and think. Here are the seven AI and big data myths that you must not fall for:

AI Does Not Always Need a Massive Database

Everest Group VP Sarah Burnett reveals that not all artificial intelligence assignments need you to accumulate heaps of data. Machine learning (ML) can train with data less than that of deep learning.

You Do Not Need AI Always

ISG director Wayne Butterfield conveys that you do not always need AI to extract relevant information. Business intelligence, analytics, and data warehousing are some of the useful alternatives.

Advanced Analytics and AI Aren’t Synonymous

Since AI and advanced analytics do almost the same activities, people tend to use them interchangeably. According to Burnett, “AI can try out assumptions, self-learn, and enhance its analysis”. Whereas analytics “can analyze data, cannot self-learn and relies on people to set its parameters”.

Too Much Data Can Bias AI Models

One of the big data myths is that the more data you feed, the better results you get. However, if you do not follow good data hygiene, your AI and ML systems would produce biased results.

Tools Already Have Built-in AI

Some applications come with artificial intelligence fed into them, and you are already using it unknowingly. So, you do not need to invest heavily in training your workforce to leverage emerging technologies.

Employees Are the AI Foundation

Among all the big data myths, the scariest is that these technologies could take over human jobs. Franco insists that you cannot leave the human equation out of the AI game. Train your staff about data governance and algorithms to make the most of it.

AI Is Not Always the Solution

The emerging technologies would give the results you want when you give them the right direction and data. Otherwise, the tools are just a fancy investment.

Click on the following link to read the original article: https://enterprisersproject.com/article/2020/3/big-data-and-ai-7-common-misunderstandings

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