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The Data Scientist

AI in data science

AI in Data Science: Unlocking Hidden Patterns for Real-Time Business Insights

Artificial Intelligence still lags behind its human counterparts, but it’s already way stronger in specific situations and use cases. For instance, using AI for data analysis guarantees to improve a company’s decision-making abilities since smart machines possess a much greater computing power than any human being (or even a group of people).

This is a truly fascinating trend that relies on relatively unknown patterns – at least if we are talking about the general public. That’s why we are going to dedicate a few pages to AI data analytics, and its outstanding influence on real-time business insights.

What Does AI Have to Do with Data Analysis?

A single data scientist can only do so much work in a given period of time. People have natural limitations – they get tired or nervous, they make mistakes, and they can only complete so much work in a given timeframe.

None of this is an issue with AI.

AI is a truly transformative tool because it allows businesses to dig much deeper in terms of their datasets. New technologies are practically almighty when it comes to analyzing complex sets of information: They can process, sort, filter through, and interpret data in mere seconds (or even milliseconds).

For example, machine learning algorithms are designed to automatically detect unusual patterns or information anomalies that would take human analysts much longer to uncover. It applies to every single type of business:

·                 Online commerce

·                 Supply chain logistics

·                 Video gaming

·                 Stock exchange

·                 You name it

In a way, this is similar to the myhomework app that provides assistance to students who wish to pay someone to do your work. This do my homework for me approach is what entire generations of modern students use to speed up their work whilst completing additional academic tasks at the same time. It’s essentially an automation of homework paper duties that resembles AI-powered automation in analytics. Speaking of analytics, allow us to move on by elaborating on its most frequent forms.

Common Types of AI Data Analysis

Though diverse, most businesses stick to a handful of models of data analysis because it just makes more sense (and is most effective). For instance, they will use AI for descriptive analysis to gain knowledge based on historical trends and former datasets. This has a huge value in the area of customer behavior as well as long-term sales tracking.

More importantly, Artificial Intelligence makes predictive analysis possible because it relies on machine learning models to forecast forthcoming results based on current data. This, for example, can lead to successful predictions of product demand or customer churn.

Another important element of business-related data analytics is the so-called prescriptive analysis. In this case, AI walks the extra mile to suggest tangible actions so as to optimize outcomes – think of goals such as recommending optimal marketing strategies or pricing models. Bear in mind that most organizations use all of these models interchangeably since that is the way to make the most of AI.

Image source: Pixabay

Real-Time Data Processing with AI

Another thing we ought to mention is that advanced software solutions do everything in real time. Instead of delivering asynchronous results (like us humans), they make instantaneous decisions by analyzing and acting on data as it’s generated.

This is in stark contrast with traditional data analysis – outdated models often involved batch processing where data is collected, stored, and analyzed later. On the other hand, AI-based time processing continuously ingests and responds to data streams from who knows how many sources. This actually depends on the type of business and all of the available datasets.

How to use AI in Data Analytics for Businesses?

We believe this is quite obvious since modern businesses can win their battles only if they possess accurate information. AI assists themin the process because it provides perfectly accurate insights that help make better decisions. For instance, we are talking about the ability to quickly predict customer demand or inventory needs – a much needed element in terms of resource optimization.

On the other hand, Natural Language Processing (NLP) makes for an excellent system for analyzing customer reviews through social media comments or support tickets. That way, companies are able to understand customer sentiment and, in return, improve the overall quality of their services.

AI in Customer Behavior Analysis and Personalization

We must explain this part of the topic a bit deeper since AI really plays a big role in customer behavior analysis, and the corresponding personalization of services. Here’s an example: A company will analyze your browsing habits and purchase history to figure out what you are likely to want and purchase in the future.

As a direct result, the same company will start offering personalized product recommendations through targeted marketing campaigns. You probably know the drill because this happens every time you visit an online store to search for a product and suddenly begin seeing that same product all over the Internet. This would by no means be possible without some serious effort from AI and data science combined.

Most companies achieve this level of efficiency in personalization thanks to advanced software solutions. Take TensorFlow as an example: This open-source machine learning framework (developed by Google itself) works in just about any environment – not just business – to help design the best data analysis model.

The same applies to tools such as Microsoft Azure AI or Tableau with AI integration. Each of these platforms combines data reporting and visualization with AI-driven insights. That way, a company gets to see trends and business patterns much more clearly.

The Bottom Line: It’s a New World of Endless Abilities

With that said, we can only add that intelligent machines have tremendous potential to improve nearly all aspects of traditional businesses. Firms that shy away from it will likely slow down and start losing the race against agile competitors. On the other hand, organizations that use this technology the right way are destined to flourish. What side are you on?