Master Data Management

Enhancing data analysis with generative AI insights

Data analysis is at the heart of modern businesses. Analytics insights drive vital business processes such as market research, customer relations, supply chain management, and strategy making.

With increasing digitalisation and the corresponding rise in data volumes, traditional methods of data processing and analysing are no longer enough. Generative AI is a boon for analysts struggling to extract meaningful insights from vast volumes of incomplete and unstructured datasets.

The use of generative AI in data analytics has four key benefits, as follows:

  • AI models can process large data volumes faster and more accurately
  • AI can navigate, contextualise, and interpret complex and unstructured data
  • AI facilitates real-time data analysis and updates
  • Multimodal Large Language Models (LLMs) can interpret data insights into various formats, including natural language and visualisations, making the insights more accessible to non-technical users

The convergence of AI and data analytics is a potent force, unlocking new possibilities for businesses. This blog explores how the use of generative AI is transforming the data analytics landscape.

Enhancing data analytics with AI

Generative AI is a game-changer for data analytics in many ways. Here are the key ways AI in analytics can help generate deeper insights and foster innovation:

  • Data augmentation  
  • Data augmentation is among the primary use cases of generative AI in analytics. Many organisations have limited or incomplete datasets, which impairs the efficacy of analytics and model training.

    Generative AI augments datasets by creating synthetic copies of the original data. This improves data generalisation, reduces overfitting, and increases the accuracy of the machine learning model.

  • Feature engineering  
  • Generative AI tools can help data analysts extract relevant features from the existing data and engineer new features that are similar to the existing ones. By leveraging the most significant information from raw data to create new variables, data scientists can build more efficient ML models.

  • Data imputation  
  • Imputation is the process of substituting missing data in a dataset with alternative values. By identifying patterns and relationships within existing datasets, generative AI in analytics can predict the missing values more accurately than traditional techniques. This application is crucial in completing datasets for analysis.

  • Anomaly detection  
  • Using AI for data analytics simplifies anomaly detection. Generative AI tools can recognise patterns within datasets and quickly identify deviations from the norm. This feature is invaluable in developing applications for error detection, fraud detection, predictive maintenance, and data security.

  • Report generation  
  • Generative AI can process large volumes of data in milliseconds, extract valuable insights, and automatically generate summaries to facilitate business intelligence reports. AI-generated reports are significantly more accurate compared to those based on manual data analysis.

    Most report generation tools combine Natural Language Generation (NLG) with imaging technologies, enabling analysts to enhance reports with visual data, such as tables, charts, graphs, and infographics.

  • Clustering and segmentation  
  • Using AI in data analytics allows for clustering and segmentation, simplifying the identification of subgroups within a dataset. Cluster analysis facilitates essential business processes, such as customer segmentation, pattern recognition, and anomaly detection, that may be difficult to grasp through traditional analysis methods.

  • Scenario simulation  
  • Generative AI can simulate various real-world scenarios and observe their outcomes, eliminating the need to harvest data from physical sites. This capability allows analysts to test various hypotheses, improve predictions, and study potentially risky situations that defy direct investigation.

    For instance, business analysts may simulate certain market conditions ahead of a niche product launch to estimate demand. Financial analysts may use simulation to test investment strategies.

  • Code generation and interpretation
  • While generative AI cannot write full-fledged, well-structured code yet, it can generate template code in SQL or Python based on previous repositories and the analyst’s input for specific use cases. Additionally, text-based LLMs can interpret code into natural language for non-technical users.

Tips and best practices for adopting AI in data analytics

AI-enhanced data analytics has use cases in multiple scenarios, industries, and operations, including business intelligence, enterprise software intelligence, customer service, marketing, finance, and geospatial technology. However, the outcome is only as good as the process that drives it.

Below are some tips and best practices to ensure success in implementing AI for data analytics at your organisation:

  • Use high-quality data
  • Incomplete, biased, or corrupt data will produce erroneous or biased outcomes. While leveraging AI for analytics, it is crucial to obtain first-party data for model training or purchase it from an ethical and reliable source.

  • Define your end goals and use cases
  • Generative AI use cases differ according to the end goals. Your analytics needs may be generic or specific. Determine whether you need AI for market insights, consumer analytics, or software intelligence. If you are looking for vertical solutions, domain-specific LLMs will produce the best outcomes.

  • Establish stringent data security and privacy measures
  • In March 2023, a bug in an open-source library caused a data leak that allowed some users to view other users’ active chat history and payment-related details. While this incident was an outlier, it serves as a reminder to establish strict data policies, privacy protocols, and governance measures before rolling out AI for data analytics.

  • Use analytics platforms with embedded AI/ML tools
  • The easiest way to deploy AI in analytics is to invest in analytics platforms with integrated generative AI capabilities. Most data analytics vendors now offer solutions with natural language generation, data visualisation, and insight generation capabilities.

How can Infosys BPM help connect AI and analytics?

Infosys Topaz by Infosys BPM is an AI-first platform that helps companies reimagine their business processes with generative AI. The platform’s 12,000+ AI use cases and 150+ pre-trained AI models can help you fast-track the adoption of generative AI for business.

Know how BPM’s generative ai for business can help your digital evolution.

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