Artificial intelligence, or AI, is the science of making computers act like humans. It can mean many different things, and many fields within AI focus on various aspects of this science.
AI is used in data streaming and data pipeline to manage the flow of data (learn more about what is data streaming over here.) Users can use it to move data from one location to another, and they can also use it to filter out unnecessary parts of the data stream. AI can be trained to recognize specific patterns and make decisions based on what it learns. It allows it to optimize the process of filtering out unnecessary data stream portions.
The AI uses patterns in the data, along with a set of rules, to decide how to handle the information provided by the streaming data. The decision-making process is done using algorithms trained on historical data sets and then tested against new sets (which may be different in some way). The algorithms can then make predictions based on what they’ve learned from previous experience.
Nowadays, there’s a lot of data floating around online. This vast quantity of information is a direct result of online activity by both consumers and businesses alike. Everything people do ranging from checking out MLB predictions, for example, to making purchases and so on leaves some kind of data trace online.
We call this big data and experts suggest that by 2025, big data will have around 150–200 zettabytes of information consisting of things we upload and share online every day. But why is this big data so important these days?
Simply put, this data contains a lot of information that can help businesses in so many ways. However, for a human to analyze this amount of data and extract valuable insights from it, it would take a lot of time even with today’s computing power. That’s where AI (Artificial Intelligence) comes into play.
As a matter of fact, AI’s machine learning capabilities allow it to process information much faster than any human being ever could. It can also extract valuable insights from the data for data scientists to further analyze and turn into viable strategies for businesses. With that in mind, here’s how AI has helped businesses analyze big data.
Data-driven decisions
Today, the online market is larger and more competitive than ever before. Every business that operates online is looking for a way to gain a competitive advantage. By combining big data analytics and AI, this is very much a strong possibility.
The main reason is that AI can help analyze big data and provide businesses with insights, such as predicting consumer behavior, predicting future market trends and market shifts, help discover new opportunities, uncover new strategies and so on. These types of insights are simply invaluable to modern businesses.
So far, most companies had to rely heavily on market research to develop product, services and marketing campaigns. Today, they can rely on data-driven decisions generated by AI technology. In other words, this process takes market research and makes it more seamless, reliable and much faster than when conducted manually.
How is AI being used to analyze big data?
Big data is being constantly fed with information from various sources, such as shared and liked content, loyalty programs, CRM software and so on. This information contains data about preferences, needs, expectations and demands of consumers, among other things, of course.
You cannot simply tap into this pool and take what you want from it. That requires modern technology that, even though is easily available today, can be complicated to handle and valuable insights become elusive the more data you extract.
However, AI handles that activity much more efficiently and can actually process all of the information to help businesses gain something from it all. Here’s how AI is used to analyze big data.
Data collection
One of the main advantages of using AI in big data analysis is its learning abilities. Machine learning, deep learning and natural language recognition capabilities of AI allow it to seamlessly collect consumer data and extract valuable information from it.
AI is actually able to recognize useful insights and adjust accordingly when collecting consumer information. The more data AI processes the more efficient it becomes by learning from what it’s doing.
Business analytics
Certain business processes like supply chain optimization, for example, rely heavily on data. That said, AI can provide real-time insights to help businesses optimize their supply chain fulfillment the best way possible.
Furthermore, businesses can plan their finances, marketing efforts and other business goals based on the information AI manages to analyze and extract from data mining. Still, companies must first determine the best way to mine data and structure it before giving it to AI for processing.
A new level of automation
Big data analytics and AI help create a whole new level of automation that requires fewer human inputs than ever before. Of course, data scientists and AI algorithm programmers are still needed to make everything possible.
However, once AI is fully optimized to collect and analyze big data, it can efficiently automate the majority of business tasks. As a matter of fact, it’s estimated that the combination of big data and AI can automate 80% of physical work, 70% of data processing and 64% of data collection tasks. As you might imagine this can lead to substantial reduction of expenses and quite an increase in profits for organizations of all sizes.
This also ultimately leads to unlocking the full potential of AI. In other words, the less humans are needed to run AI algorithms, the more efficient it becomes and the more data AI analyzes the smarter it becomes. This can go on until AI becomes fully automated to perform tasks without any intervention whatsoever.
Using AI to analyze big data in your organization
Although AI is technically still in its infancy when you consider how much potential it has, it’s still quite capable of helping businesses extract valuable insights from big data. However, if you decide to leverage this technology in your organization, AI will require a bit of help, beforehand.
As mentioned before, it’s crucial to determine how the data is being mined and structured. The main reason is that bad data can lead to bad results.
AI will process whatever information you provide it with and it won’t distinguish whether the data is good or bad on its own. Once you start feeding AI with data, you have to make sure that it knows what it’s doing. Here are a few ways you can accomplish this.
- Vision – AI must be trained to identify, process, respond and understand images and videos using computer vision algorithms.
- Speech – AI must also be trained to comprehend consumer behavior by understanding speech patterns. Speech to text and text to speech, as well as annotated transcripts can help you achieve this.
- Knowledge – AI should be trained to support semantic search, map information, and provide recommendations to have user experience in mind when extracting insights.
- Language – AI needs to learn how to utilize its natural language process (NLP) capabilities. The main reason is that consumers now frequently use voice search to browse the Internet and your AI must be able to comprehend their intent.
Closing Words
Big data analysis has become quite popular and very important for businesses today. However, with the help from the AI, analyzing big data and extracting useful information from it can prove to be quite a challenge.
