Integrating data visualization and AI

The world has changed drastically ever since Artificial Intelligence (AI) stepped into the digital world. Of course, data visualization wasn’t an exception. At first, it might have sounded ambitious to integrate human creativity with AI efficiency to enrich data analysis. Nevertheless, its practical application throughout the economy is growing apace.

With the help of data visualization, you can understand complex data and detect anomalies and trends within the boundaries of the datasets. Now imagine creating AI-powered visualizations in the blink of an eye.

Are you intrigued?

Read on to learn more about integrating data visualization and artificial intelligence and how analytics/ software are stepping up their game by leveraging AI.

Why artificial intelligence

Stating the obvious, data visualization facilitates you to understand data through intuitive representations and interactive experiences. Previously, creating a compelling and highly informative visualization was a difficult task. But today, with the help of data visualization tools such as PlotSet, creating a data visual representation has become accessible and straightforward.

To make matters even more advanced, AI techniques are here to step the process up a notch, and it has the potential to do so much more in the future. Incorporating artificial intelligence into software can help you with bulky tasks and speed up the creation process.

This would be helpful when you are working with endless rows and columns of data. The issue with a large dataset is that it often makes it challenging for you to recognize the trends, identify anomalies, and detect changes over time. It is time-consuming. Besides, the larger the data gets, the higher the chance of human error occurrences.

With AI, you can increase the efficiency of the tasks and their accuracy simultaneously. Consequently, by leveraging AI and taking advantage of automation, you can lower labor costs and avoid human error.

What is visual analytics

The term Visual Analytics refers to the use and integration of advanced data-processing software like AI with data visualization. The goal is to draw dynamic visualizations. During this paradigm, you enter into a discourse with the AI-powered software about data. As you deliver your queries, you continue receiving results back in visual form.

Considering how modern AI systems are improving at interpreting human speech, you may expect such conversational discourse to become more natural and powerful in the future. Nowadays, synthesizing and generating photo-realistic images conditioned on text description have become possible. With a few pointing and clicking and writing out instructions, you can create a visualization. It is only a matter of time before you talk to your computer out loud, Star Trek style, to accomplish this task!

Important notes to remember about visual analytics:

• In visual analytics, there is a strong emphasis on the role of human analysts in the data analysis process. It provides them with interactive visual tools to explore data and raw insights.
• Users have significant control over the analysis. They are the ones to manipulate visualizations, apply filters, and customize other configurations. In addition, it is up to the users to discover the patterns and relationships.
• This approach relies heavily on data visualization techniques to provide the elements to represent data in a visual format. Simply put, AI is the handle to work with the already existing settings more efficiently.
  

Reading into visual analytics, you can easily detect the advancement it brings with itself in facilitating you with agile solutions. However, does it stop there?

Absolutely not!

Time to meet augmented analytics!

What is augmented analytics

Now, augmented analytics is a serious upgrade to visual analytics as it aims to use existing and emerging software technologies such as advanced statistics, machine learning (Ml), and natural language processing (NLP) to develop sophisticated insights from data.

This approach expands human's ability to interact with data at a contextual level. Via tools and software with analytical capabilities, augmented analytics can provide recommendations, insights, or guidance on a query. Consequently, it will be an excellent source of information for interpreting any type of data while presenting a compelling visualization.

As you might know, machine learning’s job is to extract algorithms and learning models. It ultimately helps people in their analysis by providing insights and making faster decisions with data.

Important notes to remember about augmented analytics:

• Augmented analytics aims to incorporate machine learning and automation to assist analysts in tasks like data preparation, insight generation, and model building. In other words, it helps the analysts to uncover insights they might not see or discover otherwise.
• With the help of artificial intelligence and natural language processing (NLP), augmented analytics automatically identify patterns and anomalies in data. As a result, it can provide actionable insights to the users.
• It also goes further to explain what has happened in the data (descriptive analytics) and provides predictive and prescriptive insights, suggesting what might happen and what actions to take.
• Augmented analytics aims to make data analysis accessible to a broader audience, including business users who may not have a deep background in data analysis.
  

Tableau is the best example of software that incorporates this approach. Being one of the most powerful data visualization tools that excels in providing an intuitive experience for its users, Tableau’s integration of AI-powered features allows them to simply ask questions in natural language and receive immediate visual responses.

By leveraging machine learning algorithms, Tableau amplifies its advanced analytics skills. The integration facilitates complex data analysis and unlocks the potential for deeper data-driven insights.

What to expect in the future

One of the challenges of data analysis is that the numbers are always changing. In certain scenarios, even changes in small scales mean massive. That is why detecting anomalies can be a great choice as a prospective goal where AI systems continuously monitor a stream of data like a data center.

When something new or unusual happens, it would alert a human and automatically recommend a custom visual representation of that specific change, providing a verbal description of it. In that scenario, the role of the human operator would be to converse with the AI system to refine their understanding of the situation and take appropriate action.

Such an approach is vastly different from what analysis is done at the moment. However, it can be something to expect, and frankly enough, it is not that far-fetched, considering how fast technology is advancing these days.