Data analytics is no longer a nice-to-have for businesses. With more organizations digitizing their operations, data-driven decision-making is becoming the norm, as it helps business leaders and managers make evidence-based decisions instead of relying on guesswork.
However, a Gartner survey revealed that less than half of data and analytics teams effectively provide value to their organizations. It isn’t surprising, given that traditional data management and analytics come with several challenges.
For starters, businesses generate and collect data from various sources, making it difficult to manage and process it. They often struggle with consistency and quality issues. Visualizing the data and extracting actionable insights from it is equally painstaking, as every question must be turned into a coded database query, causing process bottlenecks.
The advent of c (GenAI) holds the promise to help organizations overcome these roadblocks and harness their data to the fullest.
Powered by machine learning (ML) and large language models (LLM), GenAI solutions are adept at identifying patterns, correlations, and relationships from extensive data sets. Better still, GenAI can understand what you ask of it in natural language and turn those questions into reports and answers based on the data it was trained on.
The capabilities of generative AI aren’t restricted to creating images and text at scale. It can also transform the way businesses approach data analytics. In this article, we’ll take a closer look at how GenAI is changing data analytics for the better. Let’s get right to it.
Filling in Data Gaps
Data analytics tools and ML can’t deliver insights unless they’re connected to the right data. Moreover, available data doesn’t always capture the nuances of different real-world scenarios.
Generative AI techniques like generative adversarial networks (GANs) and variational autoencoders (VAE) come in handy for data augmentation. In other words, you can use these GenAI models to create new data from existing data sets. They can even draw conclusions about the contexts and relationships within a data set and fill in missing data.
This synthetic yet realistic data comes in handy for training and testing ML models. It’s particularly useful for replicating rare scenarios and emergencies for which data isn’t readily available. You can even generate data that represents a normal scenario and compare it with real-life data to identify anomalies. This can come in especially handy for detecting fraudulent user behavior in financial institutions.
Additionally, the use of data augmentation helps allay concerns related to data privacy. For instance, Mostly AI uses optimized GenAI models to create fully anonymous synthetic data that’s on par with real-life data. It lets businesses use data to feed into analytics models while ensuring compliance with privacy regulations.
Automated Insights and Reporting
When combined with data analytics tools, GenAI models can help users of all skill levels extract meaningful insights from data. All they have to do is use the right text-based prompts and let the AI identify correlations and hidden patterns within the available information.
Better still, GenAI summarizes these insights using plain language, making it easier to understand for non-technical users. It’s just as straightforward to visualize the data in the form of charts and graphs.
Moreover, you can configure notifications that automatically alert you when a given metric crosses a predefined threshold or follows a specific pattern. For instance, you can use GenAI to receive alerts when there are sudden spikes in company sales.
You can further ask questions like “Which products are contributing most to sales?” or “How do sales compare to this time last year?” for more in-depth insights. Then, you can use this information to guide future sales strategies.
Efficient Handling of Complex Tasks
Today’s leading GenAI-powered data analytics tools offer a chatbot-style interface with self-service capabilities. In other words, you can use plain language to instruct the platform to perform complex analyses and extract the required insights. That makes data analytics accessible to everyone, even when an expert isn’t available to create a custom coded query.
The GenBI tools in the Pyramid Analytics BI platform let you use everyday language in speech and text form for complex data analysis and visualization. The plug-and-play solution connects directly to your own data sources like Redshift, SAP, and Snowflake, letting any authorized user extract actionable insights from your data. It also works with multiple LLMs, so you can choose which AI is the best fit for the business question at hand.
Whether you want to prep and normalize data, recognize emerging trends, or turn insights into graphs, you can do it all with the right prompts. With Pyramid, you can perform tasks as complex as creating a multi-page marketing performance report, or slicing information into segments correlated with geo-data, for example.
Improved Data Cataloging
Creating data catalogs is a crucial aspect of data governance and management. Think of a data catalog as a well-organized inventory of data assets that lets users with the necessary permissions find the right data and track data lineage.
However, manual cataloging is error-prone and time-consuming. GenAI helps overcome this shortcoming with AI-driven data curation and cataloging. It recognizes correlations and relationships between data sets and automatically categorizes and tags them.
Additionally, GenAI-powered data catalogs can offer self-service capabilities with chatbot-style interfaces, facilitating seamless data discovery. Automated cataloging also helps maintain data consistency and integrity, which are crucial for data management.
Enhanced Predictive Analytics
Predictive analytics comes in handy for forecasting demand and preempting changes in customer behavior. It also helps businesses mitigate potential risks and fraudulent activities.
With generative AI’s data augmentation capabilities, it’s possible to create data sets simulating a diverse range of real-life scenarios. That, in turn, helps predictive analytics solutions factor in more variables and create more accurate forecasts.
With a predictive GenAI platform like Pecan, you can even simplify the process of defining and applying predictive analytics models. For instance, Pecan’s Predictive Chat helps you use everyday language to specify what you want to predict. Then, you can connect the platform to your data source and let it automatically build predictive models with your data.
A New Era of Data Analytics
More than 80% of businesses are projected to have used generative AI APIs or applications by 2026. Among the varied business use cases of GenAI, its role in transforming data analytics is significant.
GenAI models can help overcome several challenges of traditional data analytics, including inferior data quality and manual cataloging. With a chatbot-style interface, generative AI platforms can help perform complex data analysis tasks and even improve predictive analytics outcomes.
The best part is that users no longer have to write lengthy code to perform these tasks. Instead, they can use everyday language to extract relevant insights and reports, resulting in a truly democratized data landscape across organizations.
