Seven Common Misconceptions Businesses Have About Big Data and Artificial Intelligence

By on

Click to learn more about author Irfan Ak.

Artificial intelligence and big data are two of the hottest and most discussed topics in the tech circle. Despite this, there are many misconceptions surrounding both big data and artificial intelligence. There is a lot of hype around both these topics as well, which can sometimes lead to even more misconceptions and myths. That is exactly what has happened in the case of big data and artificial intelligence.

In this article, you will learn about seven common misconceptions businesses have about big data and artificial intelligence.

Expert Take

Jean Michel Franco, Senior Director of Products at Talend, highlights what big data can do:

“When we can capture a lot of information about a business topic that you can improve, you do not want just to scratch the surface. You want to discover the unknown, find out the root cause, predict what will happen, address issues with extreme precision. This is more than what humans can do alone, without the help of the machine.”

Jean Paul Baritugo, Director at Pace Harmon, a business transformation and outsourcing consultancy, thinks that “There is certainly a strong relationship between big data and AI.” According to him, “Big data is the fuel, and AI is the means.”

Seven Common Misconceptions About Big Data and Artificial Intelligence

1. AI Doesn’t Always Depend on Big Data

You might need big data when you have to train artificial intelligence and feed AI applications. You might need a lot of data when your AI has to come up with answers to questions or analyze huge data sets to identify patterns. That does not mean that you need big data all the time. Let’s say you want to train chatbots — you can use a small amount of data for this purpose. It usually depends on the complexity of the problem AI is trying to solve. The more complex the problem, the more data AI needs.

2. You Do Not Need an AI Application for Big Data

Artificial intelligence can enhance your analytical capabilities by automating and streamlining the analysis process, but that does not mean that it is mandatory to extract meaningful insights from large data sets. Businesses can take advantage of data warehousing, business intelligence, and analytics to visualize insights and data even without the need for AI. There is no denying machine learning’s ability to do the heavy lifting when it comes to identifying patterns from huge data sets. This is one of its standout features, but you don’t always need AI applications to make the most of big data.

3. There Are Key Differences Between Advanced Analytics and AI

Most people fail to differentiate advanced analytics from artificial intelligence. They not only use these terms interchangeably but also think that they are the same. Even though artificial intelligence and advanced analytics are strongly associated with one another, there are also key differences that set them apart.

Artificial intelligence can enhance its analysis capabilities with automated learning. On the contrary, advanced analytics lacks this capability and depends on humans to set its perimeters. AI does not. Even if you have some assumptions, you can test your assumptions with AI, but you cannot do the same with advanced analytics.

4. Big Data Can Skew AI Models

Big data is at the heart of artificial intelligence and machine learning. In fact, the more data you feed to your machine learning models, the better they will become. However, just like everything else, big data has its downsides too. It can introduce bias into machine learning models and artificial intelligence, especially when it is not in your control.

Make sure you focus on quality instead of quantity when it comes to data. This means that you should not feed a lot of low-quality data to algorithms and expect it to deliver a better outcome. You should feed it with high-quality data even if it is in small quantities. Accumulating tons of data in your data lake does not guarantee you success with artificial intelligence and machine learning.

5. Integrating AI and Big Data Without Even Knowing It

As mentioned before, there is a lot of hype surrounding artificial intelligence. One of the direct consequences of this hype is that it forced software developers and providers to add AI-centric features into their software. That is why you see software equipped with AI capabilities. In fact, you might be using some of them in your company, but you might not have noticed it. The best thing about these AI-based solutions is that they can fulfill the specific needs of your organization and accelerate AI adoption. This can drastically enhance the user experience. You don’t have to be an AI wizard to take full advantage of these tools.

6. Humans Are Critical for Big Data and AI Merger

One of the biggest obstacles preventing businesses from adopting artificial intelligence is the lack of trust and transparency. Most businesses either ditch the idea altogether or try to throw human resources into the mix. According to Jean Michel Franco:

“You need to bring the human into the loop with Data Governance to take control of the data (data quality, representativity, data privacy) and the algorithms (use explainable AI to be able to understand what’s under the cover of the algorithms.).”

7. Not All Data Is Useful

Just collecting data for the sake of it will not cut it. Businesses need to understand this and ensure the data they are collecting or using to train artificial intelligence is of high quality. Wayne Butterfield, Director of Cognitive Automation and Innovation at ISG, said:

“There is a fine line between having data and having the right data to provide insights when used in conjunction with AI … AI is not the panacea for every problem — at least not yet — and it cannot create something out of nothing. Business leaders need to be aware of this.”

What misunderstandings does your business have about big data and artificial intelligence? Let us know in the comments section.

Leave a Reply