4 Ways to Use Generative AI for Business Analytics

In the world of business, making smart decisions is key to success. That's where generative AI comes in. It's a powerful tool that helps companies make better choices by understanding data in new ways. Let's explore how innovators use generative AI to change the game in business and data analytics.

First, let's look at the difference between generative AI versus traditional AI. Think about the chatbots on websites. Older chatbots can answer simple questions, but they can't really chat like a human. Generative AI, like ChatGPT, is different: It can talk to you almost like a real person, which is just one example of how generative AI is changing things. Here are four ways generative AI helps data analytics:

1. Data collection and generation

Training generative AI requires a vast amount of data. Often, companies do not have the volume of data needed to train their AI system to complete desired tasks. To simplify this problem, generative AI models created for data manipulation, can be used to manipulate existing data sets and create additional data.

Synthetic data created by generative AI also comes in handy when businesses need to anonymize their data before analysis. For example, a financial institution can create a synthetic data set using only their consumer behavior data. This data is no longer tied to any single person and therefore can be used in any way needed.

2. Data preparation

Data can be messy. Generative AI helps clean it up so it's easier to use. It can fill in missing pieces, find mistakes, and make sure everything is in a format that's easy to analyze.

Large data sets can be difficult to analyze when they’re not clean. The better the data set, the better the results. Generative AI can be used to clean data sets in the following ways: 

• Data imputation: Variational Autoencoders (VAEs) can be used for data imputation, which is the process of filling in missing or incomplete data within a data set. VAEs are well-suited for this task due to their ability to learn complex distributions of data and generate new data points that are similar to the original data set. 
• Outlier detection: Generative Adversarial Networks (GANs) are a class of artificial intelligence algorithms used in unsupervised machine learning. GANs offer a flexible and powerful framework for modeling complex data distributions, which can be very useful in identifying data points that do not conform to the expected pattern. Hence they can be tasked with the job of identifying outliers and flagging them for review.
• Data normalization: Generative AI can be particularly useful for data normalization, which is the process of adjusting values measured on different scales to a common scale. Normalization is essential in machine learning to ensure that each feature contributes proportionately to the final prediction.  By using these generative AI techniques, data scientists can ensure that their data  sets are transformed and standardized.
• Data augmentation and expansion: Generative AI models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can create synthetic data to augment data sets. These models, along with transformer-based models for text, can be combined to enhance data diversity and quality. The synthetic data is evaluated for authenticity and, once approved, is used to expand the original data set, improving machine learning model training, especially in areas with limited or sensitive data.
• Data denoising: A denoising autoencoder (DAE) is capable of generating new data points to replace meaningless or noisy data as part of the data denoising process. The primary function of a DAE is to clean the input data by learning to ignore the "noise" in the data.

3. Data modeling

Generative AI can also help companies figure out complex problems. For example, if a company wants to build a solar power set-up, generative AI can help find the best place to put it by looking at lots of different data.

Generative AI can be used to build data models by synthesizing realistic and diverse data sets that can be used for training machine learning models. This is particularly useful when actual data is scarce, sensitive, or expensive to collect. For instance, in the insurance industry, a company could use generative AI to create synthetic data that mimics real customer profiles and claim patterns. This synthetic data would be generated based on the characteristics of the existing data sets but would not correspond to any real individuals, thus maintaining privacy and confidentiality. This approach can lead to more accurate models, better risk management, and more personalized insurance products, ultimately leading to a competitive advantage in the market.

4. Predictive analytics

Generative AI can predict the future by analyzing vast data sets to identify patterns and trends that may not be evident to humans.

For instance, a city's public transportation department could use generative AI to predict future transit demands. The AI would analyze historical ridership data, population growth trends, urban development plans, and even weather patterns to project changes in transit usage. This predictive insight would enable city planners to adjust routes, schedules, and resource allocation efficiently, improving service while managing costs. By anticipating and responding to the evolving needs of the community, public sector agencies can improve service delivery, optimize infrastructure investments, and better serve the public interest.

Generative AI for real-world business decisions

Generative AI makes big data analysis and decision making across an organization a feasible task. Implementing generative AI for data-driven decision making generally begins with a plan. First, decide which areas of your business would benefit most from optimized decision making. Next, choose the generative AI models that best fit the decision making processes that need to be changed. Then, collect and optimize your training data with the assistance of other generative AI models. Finally, train, analyze the final output, and optimize your models.

Want to learn more? Discover how Salesforce approaches the development of trusted generative AI with guidelines for responsible development best practices.