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Data Science

Generative AI for Data Analytics and Management

Dr. Jagreet Kaur Gill | 24 November 2023

Generative AI for Data Analysis and Management

Introduction of Gen AI for Data Analytics and Management

In today’s world, we have tones and tones of data. Interpreting this huge amount of data and extracting insights is a challenging task. As a result of this changing technology, we’re now entering the age of Generative AI. Generative AI is a new generation of technology that creates new types of content, including text, images, or video.

Artificial intelligence (AI) and large-scale language models (LLM) have become the talk of the town, grabbing everyone’s attention in everything from boardrooms to dinner parties. LLMs represent a groundbreaking development due to their ability to grasp and generate text in a human-like manner, displaying a profound understanding of context. With their extraordinary ability, LLMs have the potential to outperform in a wide range of tasks, including language translation, sentiment analysis, code generation, and even creative writing. The overarching potential of these models to generalize knowledge and understanding across various domains holds the promise of revolutionizing multiple industries and fundamentally reshaping our interactions with AI technologies.

Generative AI for Data Analysis 

Generative AI is transforming the data analysis landscape, streamlining the process of extracting valuable insights from vast datasets. This technology empowers computers to find patterns within data and utilize that knowledge for creating new content or predicting. 

Traditionally, data analysis demanded the expertise of a dedicated team who can carefully investigate datasets in search of interesting trends. However, the advent of generative AI algorithms has automated these processes. This automation allows businesses to swiftly pinpoint crucial indicators and make well-informed decisions based on real-time information. 

Automating repetitive tasks, such as data cleaning and organization, is another significant boon provided by Generative AI in data analysis. By eliminating mundane manual efforts, analysts can redirect their focus towards building advanced models and scrutinizing results, enhancing the efficiency of the analytical process. 

Moreover, generative AI facilitates a deeper understanding of customer behaviour by analyzing copious amounts of unstructured data, such as social media posts or online reviews. Companies can harness these insights to craft targeted marketing strategies and enhance overall customer experiences. 

The impact of Generative AI on Data Analytics has ushered in a new era where decision-makers can access levels of insight that were previously unattainable without automation. The ongoing integration of these technologies is evidently reshaping industries across various sectors, fostering increased productivity and giving us an unprecedented understanding of our world. 

Generative AI for Data Lifecycle Management 

Data lifecycle management involves the process of managing data throughout its entire lifespan, from creation or acquisition to disposal. The data lifecycle typically consists of several phases, and the specific steps may vary depending on your organization and data type. There are various steps in which Generative AI can be applied: 

1. Data Extraction 

  • Web Scraping

    LLMs excel in web scraping and extracting text, links, and images from web pages. They understand text meaning, identify patterns, and summarize information. Extracted data is then pre-processed for further analysis. 

  • Schema Inference & Data Parsing

    Generative AI is used in inferring data schemas and parsing unstructured or semi-structured data. Trained on sample data, models learn patterns and extract structured elements, facilitating the transformation of raw data into a structured format. 

  • Transactional Data Extraction

    LLMs extract data from articles, documents, and data marketplaces, saving it in an appropriate format within the Enterprise Data Platform. For instance, extracting financial data from reports, summarizing it, and generating starter code for export to JSON format. They also extract transactional data from documents like invoices and receipts in various text formats, including PDFs. 

2. Data Integration 

  • Schema Mapping and Transformation

    Generative models, trained on source and target data schemas, create mapping rules and transformations. This simplifies data integration, ensures schematic alignment, and provides audit reference documents. 

  • Entity Resolution and Matching

    Generative AI is used in entity resolution and matching tasks, identifying and linking entities across diverse datasets. 

  • Data Unification and Deduplication

    Trained on existing data, generative models learn patterns to identify duplicate records, generating rules and algorithms for merging similar records. This streamlines data integration by eliminating duplicates. 

3. Data Transformation 

  • Data Cleansing

    LLM identifies and corrects anomalies within datasets, assisting in standardizing formats and performing deduplication tasks. 

  • Data Mapping and Transformation

    Generative AI, trained on source and target data schemas, creates mappings and transformation rules. LLMs generate code for tasks like merging, formatting or filtering data.

    For example, LLMs can transform data across the medallion data flow pattern (Bronze, Silver, Gold), refining and aggregating to generate reports on Sales, Marketing, and Supply Chain/Logistics. LLMs also aid data analysts by quickly validating hypotheses and generating framework code for data transformation rules when generating reports.

4. Data Discovery and Exploration 

  • Data Profiling

    Generative AI analyzes dataset content, structure, and metadata, generating descriptive summaries, statistics, and visual representations like distribution charts. 

  • Data Clustering and Classification

    Generative models scrutinize features and relationships to identify groups or categories and help segment datasets. 

  • Exploratory Data Visualization

    Generative AI supports exploratory data visualization by generating diverse visual formats, helping users interactively explore patterns, trends, and relationships. It creates representations like network graphs or relationship maps for uncovering data dependencies. 

  • Anomaly/Outlier Detection

    Generative AI models assist in detecting anomalies or outliers in datasets, flagging potential issues for further investigation during the data discovery process.

    Conversational, natural language interfaces leverage Generative AI to create user-friendly interfaces for data discovery. They interpret user queries, retrieve relevant data, and provide insights in a conversational manner. 

5. Data Quality 

  • Data Quality Assessment

    Generative AI analyzes data patterns and distributions and identifies anomalies, outliers, and potential quality issues. It flags erroneous, incomplete, and missing data for data cleaning. 

  • Data Preprocessing

    Generative AI automates preprocessing tasks like missing value imputation and feature scaling. It predicts missing values and applies standardization techniques for data consistency and quality. 

  • Data Synthesis and Augmentation

    Generative AI aids in generating synthetic data points mirroring the patterns of the original dataset. This enhances data for further exploration and hypothesis validation.

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Technology Options for Generative AI in Data Analytics and Management 

Various tools in the market offer diverse generative AI capabilities, ranging from analytics and reporting to natural language processing and chatbot development, catering to various application needs. 

1. Microsoft

  • Azure OpenAI Service: Large-scale generative AI models with token and image-based pricing models. 

  • Copilot: Generates visualizations, insights, DAX expressions, and narrative summaries in Power BI. 

2. Qlik

  • OpenAI Analytics Connector: Allows generative content in Qlik Sense apps. 

  • OpenAI Connector for Application Automation: Enhances workflows with generative content. 

3. Google

  • Vertex AI: customizable models embeddable in applications; tuning with Generative AI Studio.

  • Generative AI App Builder: An entry-level builder for chatbots and search applications.  

4. AWS

  • AWS Bedrock: Fully managed service for developing and deploying Generative AI applications. 

5. Tableau 

  • Tableau Pulse: Powered by Tableau GPT for automated analytics and surfacing insights through natural language. 

6. Sigma 

  • Sigma AI: AI-powered features include Input Tables AI, Natural Language Workbooks, and Helpbot. 

7. LangChain

  • LangChain: Open-source framework connecting large language models to external components for LLM-based applications. 

Conclusion of How Gen AI helps in Data Analytics and Management 

Generative AI is becoming popular in data analytics, helping users democratize, automate, and improve their analytics with AI support. Major models are now usable in enterprise data analytics, and many new generative AI startups are creating specialized solutions for different industries. 

This sector is expected to grow rapidly due to its relevance to businesses. However, quick adoption without ethical guidelines and careful decision-making could have serious consequences. To get the most from generative AI in data analytics while maintaining security, customer privacy, and ethical standards, it's essential to follow best practices that suit your organization and industry.