How Does Generative AI Transform Data Analytics

It’s the age of informed decision-making. Everything is weighed, calculated, and calibrated with precision—even seemingly intangible data—using technology, redefining how we can harness data in decision-making. A game-changer in this segment is Generative AI (Gen AI), which empowers individuals and companies to reshape and scale their businesses by coupling it with data analytics. 

Gen AI has been a digital adversary for a while now. While its roots trace back to the 1960s, it wasn’t until the introduction of generative adversarial networks (GAN) in 2014 that it gained significant growth and widespread attention in deep learning and generative modeling. In 2022, the emergence of the smart chatbot ChatGPT was another milestone. Since then, large language models (LLMs) used in Gen AI training have given rise to a technology that possesses the capacity to think and reason.

According to a 2024 Forbes report, 75% of professionals expect that Gen AI will cause ‘significant or disruptive change like their industry’s competition’ over the next three years. Gen AI is also predicted to contribute $15.7 trillion to the global economy by 2030.

In this blog, we’ll explore Gen AI’s transformative potential in data analytics and its possibilities and applications.

What is Generative AI for Data Analytics?

Gen AI primarily involves algorithms designed to generate new content using existing datasets. Gen AI facilitates synthetic data creation when applied in data analytics, enhancing the ability to test and validate analytical models. Synthetic data is artificially generated data that mimics real-world data but is created rather than collected from actual observations or measurements.

This was impossible with conventional approaches that read into historical data because of their rigidity. Gen AI enhances the richness of existing data and allows businesses to predict potential outcomes by crafting tailor-made scenarios from the given data. 2024 is said to be the breakout year for Gen AI. It will soon be a go-to solution for data analysis, as it is faster, more efficient, and facilitates easier generation of insights from massive datasets.

Using machine learning for data insights is a key element of generative AI. Drawing from machine learning principles, natural language processing, and generative models like GANs or variational autoencoders, Gen AI simulates a series of data ‘instances,’ enabling analysis of potential trends and patterns within given datasets.

Applications of Generative AI in Data Analytics

Gen AI can support your data and business throughout digital transformation and routine data analytics tasks by maintaining the quality of your data, refining it through multiple checks, navigating compliance regulations, and resolving glitches and gaps.

Automate application development coding

By using Gen AI-specific application development large language models (LLMs), you can generate the template code for your applications. It acts as a migration accelerator to convert legacy codes to codes supported by the source destination.  

Automation for code changes enhances your applications’ development lifecycle by improving productivity and speeding up delivery. Gen AI can automate the transformation of large data sets by interpreting and executing changes for vast data in a shorter time. It helps developers save time and reduce effort. 

Chatbots for engaging interactions and reports 

Are you thinking about adding chatbots to enhance your content? Thanks to LLM models, it’s now easier to implement and launch chatbots for your website. Gen AI, by analyzing language and generating text, assists in creating high-quality and engaging blog posts, news articles, and more.  

When incorporated into front-end analytics, Gen AI chatbots can add valuable context to your existing reports, enhancing their effectiveness. Depending on your preference, you can integrate Gen AI chatbots with your workflows through API integration or native app deployment.

Data integration, visualization, and optimization

Pool diverse data sources and let Gen AI seamlessly integrate them via synthetic datasets—mirrored real-world scenarios that help identify potential challenges with harmonious convergence of different data sets. 

Also, where visual representation in data analytics aids decision-making the most, Gen AI can be an artist that draws up synthetic datasets for testing and refining. Boosting efficiency in data processes, Gen AI can become a guide in data analytics and help refine data using algorithms, increasing collaborative efforts to set up optimized data processes.

Best practices for data in cloud migration journeys

Staying compliant with governance frameworks is a top priority in migration journeys, for which Gen AI tests and mitigates data misuse risks, helping you shift smoothly away from legacy systems in your digital transformation towards a new outlook on innovation with modern platforms.

Moreover, in shifting business intelligence (BI) solutions to the cloud, Gen AI enables you to overcome compatibility challenges arising from legacy software. It takes over code conversions, transforms SAS code into Python or PySpark, and addresses various other BI data requirements. It can also convert SQL queries to Apache Spark SQL for optimized big data processing and refactor R scripts into Scala for integration with Apache Kafka streaming platforms. 

No compromise on data quality in any process

Let Gen AI guard your datasets and alert you proactively regarding potential issues by constantly assessing the robustness of data through multiple quality checks. Gen AI ensures data accuracy and consistency by cleansing and standardizing data and identifying and rectifying inconsistencies, duplicates, and errors. 

It leverages advanced analytics for thorough data profiling, gaining deep insights into quality metrics, and continuously monitoring data health while seamlessly integrating disparate data sources and performing intelligent data transformations to drive accurate analysis and reporting.

An Industry-specific Outlook: AI-Powered Business Solutions

When it comes to data analytics, tailor-made solutions are the norm today. Let’s see how you can prepare your data to yield the best insights from your data using generative AI. 

Customized industry-specific solutions help with the following:

• Customer Data Analytics: Increase satisfaction and delight customers by preempting their needs using Gen AI for insights in customer data analytics. Different customer behaviors can be simulated as scenarios and tested, enabling you to develop accurate predictive analytics to enhance marketing strategies. Walmart, for instance, uses advanced AI for inventory management and pricing strategies.
• Marketing Analytics: Gen AI empowers strategies and fine-tunes campaigns to simulate market scenarios and optimize performance. Cloud hyperscalers like Microsoft, AWS, Snowflake, and more have multiple AI features for businesses looking for Gen AI solutions. For example, Hexaware’s AWS-backed Gen AI solution helps build better retail marketing strategies by offering in-depth analysis and insights into customer reviews and ratings. The solution empowers product experts by enabling them to ask Gen AI questions to gain deep insights into customer behavior, emerging trends, and new product specifications. This accelerates e-retail feedback loops and puts you ahead of your competition.
• Supply Chain Analytics: Gen AI can simplify your supply chain and perfect operational excellence. It helps with deeper insights and minimizes risks by aligning your data to mirror real-world scenarios. Gen AI assists supply chain management and lets manufacturers optimize operations by identifying demand fluctuations. It can also provide personalized delivery operations and transport route optimization. Ultimately, it helps identify bottlenecks and vulnerabilities, aiding in strategy development.
• Finance and Operations Analytics: When coupled with data analytics, Gen AI can give precise forecasts so that you can plan for contingencies and allocate resources for an optimal operational budget accordingly. It is a transformative solution for robust risk management strategies, new business cases, vendor management, and regulatory measures. For instance, in automating the loan auditing process, Gen AI helps financial institutions enhance efficiency and accuracy while reducing operational costs. In the case of ERISA bonds, Hexaware’s Gen AI solution leverages Microsoft’s OpenAI, providing a user-friendly interface that simplifies bond classification and risk assessment. It secures bond investments and ensures that they align seamlessly with the specifications under the ERISA Act and other regulations.

Benefits of Integrating Generative AI in Data Analytics

For many businesses, the most time-consuming, labor-intensive manual process is sifting through vast amounts of data. It’s just like searching for a needle in a haystack. Nevertheless, this process is crucial and unavoidable. It demands meticulous attention to detail and a considerable amount of guesswork when done manually, making it prone to human errors that can prove costly. 

Gen AI in data analytics improves data quality by continuously refining it using custom algorithms for specific scenarios. This helps businesses pinpoint and work through potential issues before they affect growth and revenue. Here’s how:

• Quick Time-to-market: Time to market is quicker as and when analytical models and demand-based innovation become possible. If you are about to launch a product and wish to conduct a test campaign, combining data analytics and Gen AI helps leverage the data that you already have. It helps with compelling storytelling, anticipating market reactions, and exploring alternative strategies.
• Contextual Understanding of Data: The AI system can comprehend and interpret data within the appropriate context. It helps with context-specific, deeper insights. Leveraging Gen AI to integrate natural language into data analytics enhances data agility, enabling it to reflect intricate datasets and generate diverse scenarios accurately.
• Natural Language Queries: When natural language processing and Gen AI are used together in data analytics, data insights become more accessible through natural language queries. This streamlined interaction enhances the user’s experience with the data.
• Support for Automation and Real-Time Analysis: Real-time analysis gives you the agility to respond to market conditions quickly. Especially for marketing firms, the ability to instantly work around trends, consumer reactions, and behaviors as they unfold can allow for real-time adjustments to campaigns, content, and strategies.
• Recognition of Patterns, Correlations, and Relationships: Gen AI makes it possible to read into data like never before and reveals hidden narratives. It helps integrate and visualize data from various sources to identify outliers, clusters, correlations, and changes to improve accuracy in problem-solving.
• Scalability in Data Processing: Gen AI enhances scalability in data processing by employing advanced algorithms that can efficiently handle large and growing volumes of data. For instance, in healthcare, it can analyze massive datasets from patient records to identify trends and patterns for personalized treatment plans.
• Finding Perfect Business Solutions: Gen AI adeptly navigates extensive historical and unstructured data, unveiling hidden patterns that empower your business to pinpoint optimal strategies for resolving complex organizational challenges.

Generative AI: The Perfect Solution for Enhancing Data Analytics

If you want to enhance the effectiveness and efficiency of your data-powered initiatives, generative AI models could be the differentiator. They add value in challenges arising from scenarios where markets change rapidly, businesses are subject to global turmoil, and other factors need keen monitoring, even pre-empting. 

By clubbing Gen AI with data analytics, you can deal with the complexities of data precisely. Moreover, Gen AI has become relatively easy to adapt to, as IT services companies like Hexaware are increasingly enabling businesses to transform such ideas into business goals. Get insights on our advanced data and AI solutions, which are already elevating businesses to simplify tomorrow’s challenges.

Conclusion

Data-driven decision-making doesn’t need to be a challenge anymore. Generative AI has brought a transformative wave in data analytics. From amplifying industry-specific business intelligence to redefining advanced analytics for various business verticals, Gen AI solutions now play a crucial role in modernizing businesses. 

Your business can use Gen AI to scale data processes, improve contextual understanding of data, and even automate application development—the list is endless. Contact Hexaware today to take data analytics to the next level.