Data Analytics Powered by Generative AI: Challenges and Benefits

We live in a digital world where every step we take generates new data – data that could be used to drive innovation and business intelligence. However, it’s quite difficult for businesses to gain actionable insights from raw data.

That’s where analytics comes in. According to a recent survey, 91.1% of organizations achieved measurable value from data and analytics in 2023. [1] These improvements are projected to increase significantly as businesses begin to use generative AI for data analytics.

Unlike ‘legacy’ AI models, which primarily rely on finding underlying patterns in data to make predictions, generative AI models learn to form new content by identifying patterns and creating new variations based on those patterns.

In this guide, we explore the role of generative AI in data analytics, with a focus on how it’s being used to transform data analytics and the unique benefits and challenges it presents.

How is generative AI applied in data analytics?

Before we get to how generative AI is applied in data analytics, it is important to understand what generative AI is in the first place.

Generative AI is a type of artificial intelligence that uses various technologies such as neural networks, complex algorithms, and deep learning models trained on vast, diverse datasets to generate unique outputs by mimicking the characteristics of the original dataset.

Over the past few years, the technology has become quite popular, mostly due to its ease of use and unmatched versatility. Generative AI models enable you to create high-quality text, images, and videos with just a simple prompt.

The deep learning technology incorporated into Generative AI models also enables them to perform advanced analytics on massive datasets.

Some of the key applications of generative AI in analytics include:

Predictive analytics

Predictive analytics is simply the process of using data to forecast future outcomes. [2] Generative AI enables businesses to analyze large datasets, identify patterns and trends, and make accurate predictions.

For instance, a retail business can use gen AI to analyze the relationship between customer churn rate and stock prices. With these insights, the business can better adjust its prices to match customer expectations, identify other bottlenecks, and accurately predict future trends.

Natural language processing (NLP)

Generative AI has disrupted the field of NLP, to say the least. Legacy AI systems mainly use NLP for language translation and simple machine operations. [3] Gen AI has taken these capabilities a step further by accurately understanding and generating human-like text.

This has given rise to numerous applications spanning various fields. For instance, the digital marketing sector has seen a major boost in productivity due to the use of gen AI for content creation. Many businesses are also using gen AI-powered chatbots and translation tools to help them communicate and engage customers effectively.

Fraud detection

Fraud is one of the most commonly experienced crimes worldwide. In 2023 alone, US citizens lost over $10 billion to fraudulent scams. [4] What’s even more concerning is that in most of these cases, the people and businesses affected applied ‘proper’ fraud mitigation mechanisms.

The biggest challenge in fraud mitigation is the prevalence of false positives in data. This is where a legacy AI system identifies an incident of fraud as a fraudulent activity, limiting the system’s effectiveness.

Gen AI can produce data representing normal behavior. When compared to real-world data, it could help identify instances of fraud. Ultimately, this helps businesses mitigate fraud and protect against fraudulent activities in industries like retail, finance, and healthcare.

Image recognition

Image recognition software has been around for quite some time now. By using machine learning and computer vision technology, machines can accurately identify images of people and objects.

Gen AI takes this a step further by enabling users to generate realistic images, produce creative works, and enhance low-resolution images. The data produced by these applications could also be used to train image recognition models to make them more accurate and reliable.

Read more: AI Image Recognition in 2024 – New Examples and Use Cases

Enhancing data quality and preparation

The accuracy and reliability of any AI model all depend on the data used to train it. The same goes for analytics, where businesses use collected data to make predictions. Low-quality data often means inaccurate predictions, which could harm the business.

Gen AI can help find and fill missing values in data and automate data visualization. Organizations could also use gen AI to expand the size and diversity of datasets and simulate situations using synthetic data.

This helps streamline the data quality and preparation process and ensures that prediction models are reliable and resilient.

Read more: Data Preparation for AI Initiatives: The Essential Steps

Automating data insights and reports

Reporting is one of the most time and resource-intensive business operations. Creating a business report and deriving meaningful insights from it often means neglecting other important aspects of business, which could lead to reduced productivity.

Gen AI can help automate data insights and reporting and enhance the business decision-making process by providing data-driven recommendations for informed decisions and improved strategies. It can also provide unbiased insights by examining past data and filling in missing information.

Generative AI models can also create personalized reports, detect hidden patterns and trends, and highlight critical errors in documentation. This not only saves time but also provides consistent, trustworthy insights. This way, organizations can effectively capitalize on opportunities, handle difficulties, and transform raw data into actionable insights.

Providing personalized customer experiences

According to a recent McKinsey report, 71% of customers expect companies to deliver personalized experiences. [5] Gen AI can help improve customer experiences in various ways, including evaluating customer feedback to create immersive experiences with augmented reality and hyper-personalization.

The technology can also help improve customer experience mapping by providing intelligent virtual assistants and chatbots for proactive customer support. There’s also the possibility of leveraging gen AI for omni-channel delivery to provide consistent experiences across all platforms. This, combined with emotional intelligence, integration helps foster more personalized and compassionate customer interactions across the funnel.

A beginner’s guide to generative AI in data analytics

In little more than a decade, data analytics went from traditional manual methods, legacy AI-powered analytics, to what we have now – AI-powered big data analytics. This trend has been driven partly by widespread digitization and massive improvements in storage and processing capabilities.

The past few years have seen yet another transformation with the emergence of models that can analyze data at a previously unprecedented scale and speed. This has drawn a line between advanced analytics (powered by Gen AI) and the legacy AI systems that came before it.

This is not to mean that legacy AI is an outdated concept but rather that gen AI provides advanced capabilities that enhance AI accuracy and reliability when it comes to analytics.

For instance, while many companies have benefited from using data and analytics to improve their business operations and manage costs, implementing data-driven decision-making at scale is still quite challenging.

There’s also the issue of legacy and siloed data. Companies often struggle to find relevant data because they simply ‘forgot where they put it.’ The same notion applies to legacy data, which often hinders the application of analytic data due to inaccurate predictions.

Generative AI effectively solves these problems by making it easier to access, explore, and manipulate data. Gen AI tools are also quite easy to use. Unlike legacy AI, which requires experienced data analysts to perform data analytics, gen AI allows relatively inexperienced users to perform complex tasks using natural language prompts.

Generative AI also substantially improves automation throughout the data analytics lifecycle – right from extract, load, and transform (ETL), to data preparation, analysis, and reporting.

When applied to advanced analytics, generative AI can:

• Streamline the foundational data stages of ETL: Gen AI uses predictive algorithms to optimize data extraction, organize data intelligently during loading, and transform data with automated normalization and schema techniques.
• Accelerate data reparation: Generative AI can predict and fill in missing values, integrate data from external sources, and leverage its advanced pattern recognition capabilities to detect anomalies, thus ensuring data quality and consistency.
• Enhances data analysis: Using generative AI for mapping and spatial analysis allows organizations to accurately interpret geological data. Other gen AI capabilities, such as the automated selection, tuning, and validation of ML models, further enhance the accuracy and efficiency of predictive analysis.
• Streamlines analytics and reporting: Businesses can use custom generative AI tools for interactive data analysis and visualizations tailored to their specific business needs. Large language Models’ NLP capabilities can also help transform complex data into narrative reports, making insights accessible to a broader audience.

Advantages of generative AI in data analytics

Gen AI is transforming data analysis by making the whole process more efficient, accurate, and cost-effective. Here are a few ways in which businesses can benefit from using generative AI for data analytics.

Increased automation

Gen AI’s remarkable capability to find trends and patterns, even in complex, unstructured data reduces the need for manual processing, leading to labor, time, and cost savings. It also automates resource-intensive tasks such as data labeling, cleansing, and automation, freeing up human workers’ time to focus on more strategic, high-value work.

Improved pattern recognition

Nothing beats generative AI when it comes to identifying complex correlations, patterns, and relationships in data. Gen AI models can also simulate different scenarios and identify risks before they happen, giving businesses a chance to deploy proactive mitigation measures.

Exposing generative AI to business data can also benefit businesses by allowing them to identify prospects for growth such as new products, services, or markets.

Take financial institutions, for instance. Various financial institutions use gen AI to replicate patterns from real financial transactions, allowing them to train better-performing fraud detection models. This not only helps mitigate fraud but also allows the organization to develop new financial products that are safer and more aligned with customer needs and expectations.

Efficient data catalogs

A data catalog can be defined as an organized inventory of data assets that enables data users to find what they need in one place. An ideal catalog should be able to offer fast, self-service access to appropriate data with meaningful context.

Businesses can leverage generative AI capabilities in the cataloging process by utilizing it to intelligently categorize and tag data sets. This streamlines the cataloging process and makes the catalog more efficient. Automating the cataloging process also eliminates human error, ensuring data quality and consistency, which is vital for better data management and governance.

Improved efficiency and productivity

Gen AI can significantly improve the effectiveness of data analytics by automating repetitive processes and procedures. Its NLP capabilities also enable users to engage with data using text-based inquiries and commands, streamlining complex processes and speeding up analytical workflows. Ultimately, this helps reduce the time and effort needed for data preparation, analysis, and reporting, leading to cost-saving benefits and increased productivity.

Enhanced data visualization

Gen AI can easily convert complex statistics into understandable visual representations like graphs, charts, and interactive dashboards. This helps make it easy for users to comprehend trends, patterns, and correlations, allowing business leaders to easily digest information and make effective business plans.

Cost reduction

By automating mundane, repetitive tasks, gen AI can help cut down on analytics-related labor costs. Besides helping reduce operational costs, gen-AI-powered automation can also help organizations streamline operations, allocate resources more efficiently, and reduce manual intervention.

Generative AI in data analytics: Use cases

Despite its overwhelming popularity, generative AI is still a relatively new technology. That said, it has already found its way into nearly every industry, showcasing its potential to streamline processes and improve productivity.

---

---

---

---