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How Generative AI is Revolutionizing Data Analytics

This article explores the transformative impact of generative AI on data analytics. We’ll examine the technology’s role in enriching market data, optimizing prices, and even predicting customer behavior.

We will also discuss the challenges of using generative AI in data analytics. These include ensuring the quality and quantity of data, technical complexity, and ethical and legal considerations.

Businesses have invested heavily in collecting and leveraging data in the last decade. And while we’ve learned that not all data is equally valuable, the companies that knew how to best extract actionable insights have seen huge gains. But as the use of generative AI in data analytics increases, are we seeing the rise of a new generation of data-driven companies?

In this article we’ll cover:

Let’s jump right in!

Benefits of Using Generative AI in Data Analytics

The main benefits of generative AI for data analytics come from its ability to analyze data far more quickly than a human, but also generate new “synthetic” data based on the training material.

This means that researchers and data analysts can preform complex calculations in days rather than months. And business leaders gain access to reliable data, even in situations where collecting real-world data isn’t feasible.

The three main benefits of generative AI in data analytics are:

  • efficient data analysis
  • better decision-making
  • more accurate market predictions

Efficient Data Analysis

As mentioned, generative AI models can analyze huge volumes of data from different sources. The models can even work on multiple data streams at the same time and make sense of complex, unstructured data sets.

Consequently, we are already seeing reports of AI models doing complex medical research and analyzing hundreds of thousands of compounds. Something that would take years or decades for human researchers.

Better Decision-Making

Generative AI is great at analyzing data. But more importantly, it can use this data to provide context-rich insights – crucial for data-driven decision-making.

Because of their size, the models can see patterns in the data that humans might miss, and suggest actions to take advantage of them.

In a SAAS company, the model can analyze customer behaviour and identify patterns that indicate potential customer churn. The model can then suggest actions to prevent churn before it even happens.

More Accurate Market Predictions

No one can predict the future. But even being close can still bring great benefits to your company. And generative AI solutions are just the way to get there.

GenAI models can predict future trends by creating new datasets that mimic their training data’s properties. This results in much more accurate predictions about the market, allowing you to plan for possible shocks or opportunities.

Use Cases of Generative AI in Data Analytics

Different industries like healthcare and retail are using generative AI to find value in data in innovative ways. In this section, we’ll explore concrete scenarios in which generative AI can revolutionize data analysis.

The three main use cases of generative AI in data analytics are:

  • predicting customer behavior
  • price optimization
  • market data enrichment

Predicting Customer Behavior

Since generative AI models can project data into the future, you can use these solutions to confidently predict customer behavior on a large scale.

This is especially valuable for the retail industry as it allows retailers to find out how customers would react to different future scenarios.

For example, a global retailer might use a genAI model to analyze the buying habits of millions of customers. The company can then generate new data to predict which products each customer is more likely to buy and in which situations.

Price Optimization

The boom of online sales channels resulted in greater competition and greater price sensitivity for most products and services. So making sure you sell at just the right price has a huge impact on your bottom line.

With generative AI, you can generate different price scenarios and see their influence on future sales by analyzing:

  • your past sales data
  • competitor prices
  • patterns of demand
  • market trends

The models can even take into account different goals you might have – like an increase in profit or faster growth.

Market Data Enrichment

The value of generative AI is not just in what you do with the data – sometimes it’s the data itself.

For example, entering a new market always carries a risk. Especially if you’re unable to gather competitive intelligence or do proper research.

In these situations, generative AI’s ability to synthesize new data is invaluable. With it, you get a high-quality substitute for accurate analysis and forecasting.

As a retailer, you might ask the model to predict how customers would react to a series of new products or services. You can then pick the best-performing one – reducing the possibility of failure that might damage your brand.

Challenges of Implementing Generative AI in Data Analytics

Generative AI models have the potential to transform the field of data analytics.

But keep in mind that the quality of the insights and accuracy of predictions are directly connected to the quality and diversity of the training data, and the investment you are willing to make.

In this section, we will cover the three main challenges of implementing generative AI for data analytics:

  • ensuring high levels of data quality and quantity
  • technical complexity
  • ethical and legal challenges

Ensuring High Levels of Data Quality and Quantity

In data analytics, the quality and quantity of data directly impact the quality of the insights you get. And since generative AI models not only provide insights but also generate new data, they are especially sensitive to this rule.

The training data is of low quality if it’s biased, incomplete, or outright inaccurate. As a result, the AI model will provide misleading results or hallucinations – leading to poor decisions or even financial losses.

On the other hand, to make accurate predictions, you need to ensure you have enough training data to reflect the complexity and variability of real-world scenarios.

Technical Complexity

To implement generative AI, you need to train complex machine learning and deep learning models such as GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders).

These models require a deep understanding of machine learning principles and advanced coding skills. A simpler alternative would be to work with an implementation partner that has the right expertise.

This is why we’ve built a separate machine learning team at NETCONOMY to ensure our colleagues are always ahead of the game.

These models also require a lot of computing power to process large data sets. Something which can be challenging for businesses without access to high-performance computing.

Ethical and Legal Challenges

Data analytics often includes handling sensitive customer data, so including generative AI into the mix can raise some ethical and legal questions due to regulations such as GDPR.

The first step should always be to ensure you have the consent to use this data, and that you have processes in place to protect it appropriately. Having something like a CDP or CDC would help you decrease this risk and ensure compliance across the company.

Final Thoughts on Generative AI in Data Analytics

Generative AI holds great promise for data analytics – from predicting customer behavior and optimizing prices to enriching market data. It enables businesses to simulate and predict complex patterns and outcomes, potentially revolutionizing decision-making and strategy.

However, implementing generative AI comes with its own set of challenges, including ensuring high-quality training data, facing technical complexity, and navigating ethical and legal considerations.

Businesses must carefully consider these aspects to fully leverage the capabilities of generative AI while maintaining compliance and safeguarding the trust of their customers.


Authors and Contributors

Boban Djordjevic | Development Lead, NETCONOMY

As part of our Machine Learning team, Boban is responsible for handling initiatives around data and AI, supporting pre-sales activities by providing technical input, designing high-level architectures, and supporting customers in choosing the right solution approach.

Nikola Pavlovic | Content Marketing Manager, NETCONOMY

Nikola is an experienced content and communication professional who believes that powerful storytelling is key for building brands, educating audiences, and designing marketing campaigns that deliver.