What Is AI Analytics?

With AI analytics, everybody is more productive: analysts can get help with their advanced analyses, and non-experts have a data-exploring assistant to guide them. AI analytics provides data analysis combined with algorithms, large language models (LLMs), natural language processing (NLP), and generative AI to help you explore and find insights, uncover trends, and measure performance.

Your company’s data is a gold mine of information waiting to be deciphered, yet many businesses lack the knowledge to use it. By democratizing data, AI analytics delivers insights that can help you make data-driven decisions, regardless of where you sit in the organization. Whether your role is to create new products, market them, or predict outcomes, AI analytics gives you access to a treasure trove of insights based on real-time data at scale.

This guide introduces you to the basic concepts of AI analytics and discusses its applications and techniques along with its limitations. You’ll also learn how your business can build a competitive edge with AI analytics. Let’s get started.

Table of Contents

1. Understanding AI data analytics
2. Applications of AI data analytics
3. Five core pillars of AI analytics
4. Traditional data analytics vs. AI data analytics
5. AI data analytics techniques
6. Challenges and limitations of AI data analytics
7. Building a competitive edge with AI-powered data analytics

What is AI data analytics—and how can it transform your business?

What is AI data analytics?

At its most basic level, AI analytics gives you the ability to use advanced algorithms, LLMs, and generative AI to analyze data, identify patterns, and make data-driven business decisions.

Benefits of AI data analytics

AI provides key advantages to your organization, such as putting insights into the hands of people who rely on data every day and helping your business achieve results faster.

Data democratization

With AI, your company can democratize data so that it’s accessible to everyone—releasing it from the purview of data analytics professionals only. The fact is, the majority of end users of analytics are business users and leaders whose primary job is not data analysis.

Time and cost savings

AI analytics builds and scales data architecture with reusable models and analytics. As a result, you don’t need to configure servers, manage software upgrades, or increase your hardware capacity to meet greater data volume and usage—saving you time and money.

AI analytics is applicable to a variety of industries—it can help businesses achieve a competitive edge, whether in sales, marketing, commerce, and more. Here are just a few industry-specific use cases:

Sales and Service: Personalized emails

Sales reps can quickly autogenerate personalized emails tailored to their customer’s needs, while service teams can autogenerate personalized chat replies and case summaries using sales data.

Marketing: Personalized content

Marketers can autogenerate personalized content, such as charts and data visualizations, to engage customers and prospects across email, mobile, web, and advertising.

Commerce: Automatic insights and recommendations

Commerce can autogenerate insights and recommendations to help tailor and deliver innovative products to consumers. Using AI, teams can autogenerate product descriptions, recommend relevant products, and create seamless buying experiences.

General business insights

Moreover, you can use AI analytics for targeted business decisions, whether you need to create new assets for different stages of the customer journey, predict sales and profitability, or generate insights to better understand trends.

AI analytics relies on five core pillars to enable this transformative technology to rapidly ingest various datasets, analyze data from different sources, predict outcomes, and provide insights.

1. Natural language processing

The first pillar, natural language processing (NLP), processes and interprets unstructured text-based data (such as tweets, social media posts, and so on) so that it can be reviewed and analyzed. NLP can enable meaningful social listening and sentiment analysis and summarize large amounts of text.

2. Machine learning

The second pillar, machine learning (ML), can analyze data and make predictions about future actions using statistical models and algorithms. By training these ML algorithms on large quantities of data, insights are put in the hands of business people.

3. Large language models

The third pillar, large language models (LLMs), is a type of ML algorithm in which the AI is trained using massive amounts of text-based data. LLMs can both recognize and produce text that sounds like it’s created by a human. It is frequently used for classification and pattern recognition.

4. Neural networks

A fourth pillar, neural networks, is a type of ML algorithm based on how the human brain functions. Modeled on brain processes and how the human brain analyzes data, AI data analytics use neural networks to recognize complex patterns.

5. Deep learning

Finally, the fifth pillar, deep learning, is a subset of neural networks that involves training AI models using large amounts of data. Deep learning imitates human learning patterns by using AI and ML to layer algorithms and find patterns in the most complex and abstract data.

Traditional data analytics

With traditional data analytics, enterprises rely on skilled data scientists and data analysts to mine data and create sophisticated algorithms that will reveal trends. This is labor-intensive work, which is expensive and takes time. And, by the time you’ve uncovered findings that pertain to business trends and patterns, time has passed and data can be stale.

AI data analytics

Using AI, companies can quickly analyze massive amounts of data rather than using a manual process—reducing the time required for traditional analysis from months or weeks to a matter of minutes or even seconds. Moreover, the democratization of information means that traditional methods of analysis, which once required specialized expertise in data analytics, are within reach of people across the organization.

By presenting real-time data insights in a consumer-friendly way, every person in the company can make better-informed business decisions quickly, accurately, and at scale.

Will AI replace data analysts?

With all this focus on AI analytics, it may seem as though there’s no longer a place for data analysts. In fact, AI-trained data analysts will play a significant role in the future. Freed from time-consuming projects, their focus can shift to value-add work, including reviewing algorithms and validating data insights generated by AI.

How to determine which AI data analytics technique to use

Deciding which is the right AI data analytics technique for your needs typically comes down to the type of details you want, your industry, the target audience, and how the solution integrates into your existing IT infrastructure. Here are some questions to ask yourself:

• Are you looking for intelligent, personalized, and contextual insights using real-time metrics for everyday use?
• Are you a sales organization that wants predictive modeling to determine the most profitable upsell or cross-sell opportunities?
• Are you a financial services organization that needs to improve customer experiences and manage risk?
• Do you need to generate personalized content for customers that can be shared across email, web, social media, and advertising?

Types of AI analytics techniques

There are several AI techniques that you may want to consider using to support AI data analytics.

Data visualization

Data visualization technology helps you share insights with non-technical people in an accessible way. It enables you to present information and data through a graphical representation and communicate key insights in a manner that’s easily understood by a general audience.

Predictive analytics

Predictive analytics can help you determine the likelihood of future outcomes. Relying on techniques like data mining, statistics, data modeling, artificial intelligence, and machine learning, predictive analytics uses your company’s historical data to make predictions about the future and identify risks and opportunities.

Generative AI

In the past, data analysts have mined, curated, and tested your business data and then built dashboards to share these findings and insights. However, generative AI empowers you to seek answers to questions most relevant to you by ingesting real-time data and providing insights similar to a data analyst.

Tableau Pulse is a type of generative AI technology that delivers trends, patterns, forecasts, and more using your company’s proprietary data and proactively generates prompts or new questions to help you dig deeper.

• AI assistant or copilot technology is a type of generative AI powered by LLMs. A conversational AI assistant, such as a chatbot, enables you to pose questions freely, and it will respond as if you’re communicating with another person. For example, Einstein Copilot for Tableau uses this technology to help you explore data in the way you want to, brainstorm ideas, and create visualizations based on trusted insights.

Many people are aware of AI’s limitations and potential pitfalls. The occurrence of “hallucinations,” where AI makes up “facts,” is one flag that has garnered public attention. However, there are two other areas of concern that include bias and data privacy.

• Bias: Whether by gender, nationality, abilities, or other differences, bias can be intentionally or unintentionally reflected in how the AI is trained.
• Data privacy: In addition, how AI uses personally identifiable information (PII), either from inside the company or from customers, is an important consideration.

While no AI tool is 100% foolproof, you can mitigate the risks of training bias, data privacy, and hallucinations by employing only trusted AI tools. Seek to partner only with AI companies that have committed to responsible AI. (Not to toot our own horn, but Tableau only builds tools based on trusted AI.)

Finally, there is the important role of human oversight, even while using AI analytics. Partner with AI analytics team members to ensure you mitigate bias. It’s also critical to ensure transparency in the process and educate your end users to promote trust in AI tools.