Non-technical Users Research Articles

Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard

In today’s rapidly evolving business landscape, data analytics has become a critical tool for organizations striving to remain competitive and make informed decisions. Although technology facilitates data analysis, many decision-makers need more technical knowledge to use these tools. This project focuses on developing a business intelligence system in Power BI to analyze agricultural exports from Mexico as part of the Agro-logistics Observatory, aiming to bridge this gap and make data more accessible for non-technical users. The objective is to create an interactive panel that would allow users to visualize and compare export and import data, thereby supporting decision-making for both business and academic purposes. The methodology involved collecting and cleaning data from various sources, such as public institutions, and a modeling process that integrates geospatial data and economic classifications. Key performance indicators (KPIs), such as trade balance and the export share by-product, were designed and calculated to identify critical products and trade patterns. Although similar approaches have been presented in the literature, the novelty of this research lies in the detailed presentation of its methodology, which provides a step-by-step guide for its replication that is often lacking in other studies. In addition, it offers a dashboard that integrates data from various sources, presenting them through easy-to-interpret visualizations so that stakeholders can quickly and efficiently access valuable information beyond traditional data visualization. Furthermore, the project highlights opportunities for improvements in data visualization, including integrating more specific product data and enhancing the dashboard’s capabilities through predictive analysis.

Developing a Graphical Domain-Specific Modeling Language for Efficient Lightweight Block Cipher Schemas Configuration: LWBCLang

The lightweight block cipher is an encryption technique with negligible computational overhead. Despite its advantages, it faces a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this research, we suggest a new graphical domain-specific modeling language to make it easier for both non-technical users and domain specialists to implement lightweight block cipher schemes. The proposed language, called LWBCLang, is a modular and extensible language that offers graphical components for constructing three essential types of inner block cipher structures. Seven different methods of keystream generation and all the tests of the NIST suite with performance analysis are provided. In the context of its meta-model, LWBCLang's abstract and concrete syntaxes are specified. LWBCLang has been implemented as an internal DSML with Python as the host language. The evaluation of LWBCLang is based on qualitative analysis to demonstrate the language's effectiveness and efficiency. Further benefits of this proposed language are evaluated and discussed in depth in this research.

NoviCode: Generating Programs from Natural Language Utterances by Novices

Abstract Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets. However, current studies focus on technical instructions and programmer-oriented language, and it is an open question whether these models can effectively translate natural language descriptions given by non-technical users and express complex goals, to an executable program that contains an intricate flow—composed of API access and control structures as loops, conditions, and sequences. To unlock the challenge of generating a complete program from a plain non-technical description we present NoviCode, a novel NL Programming task, which takes as input an API and a natural language description by a novice non-programmer, and provides an executable program as output. To assess the efficacy of models on this task, we provide a novel benchmark accompanied by test suites wherein the generated program code is assessed not according to their form, but according to their functional execution. Our experiments show that, first, NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm. Second, we show that a novel approach wherein we align the NL utterances with the compositional hierarchical structure of the code, greatly enhances the performance of LLMs on this task, compared with the end-to-end Text-to-Code counterparts.

Exploring Innovative Use Cases for Enhancing Digital Wallet Adoption and User Engagement: A Strategic Study for Future Implementation

The advancement of digital payment methods especially the use of the digital wallets has greatly impacted the financial industry as it provides safe means of payment. Nonetheless, while these platforms have become popular, user participation is still low, and current platforms do not offer new functionalities that could improve consumer interaction and address the variety of consumer segments. This research, titled "Exploring Innovative Use Cases for Enhancing Digital Wallet Adoption and User Engagement: The research titled “Digital Wallets: A Strategic Study for Future Implementation,” looks at possible new use cases where digital wallets can be applied to enhance their current roles of facilitating payments. The research aims at establishing how new technologies like; Artificial Intelligence (AI), Blockchain, Augmented Reality (AR) and gamification can improve engagement, the user experience and thus create more adoption. Some of the key areas include Peer-to-Peer (P2P) lending, Cryptocurrency, Voice identity, and transactions that can serve the unbanked population as well as regions that are still to embrace digital wallets. Technological factors, consumer behaviors, and socio- economic conditions that affect the uptake of digital wallets are discussed in this research to guide Fintech firms, banks, and regulators. The study used a systematic literature review which involved using articles from the last four years from academic journals, industry reports, and case studies. This particular secondary research helped to determine the tendencies, problems, and potential of digital wallet usage, including such aspects as simple and intuitive interface for non-technical users, the application of gamified approaches in financial literacy, and the opportunities for the implementation of blockchain technology to provide financial services to unbanked customers. Therefore, the research shows that gamification, P2P lending, and cryptocurrency-enabled wallets will greatly increase user engagement and financial inclusion. In addition, AI-enabled personalization in the context of digital wallets, and AR-based shopping represent a vision of how the future of cross-border payments, sustainability incentives, and UI advancements will shape the user experience. Finally, the research provides a set of tactical recommendations for digital wallet providers on how to bring such innovative use cases into practice to enhance user engagement and to expand market coverage in the developed and developing countries.

Learning to Listen Properly: Participatory Digital Projects at the German Maritime Museum Based on Iterative Co-Creation

At the German Maritime Museum, the user-centered approach of the digitization department has emerged as a driving force for implementing an open and participatory strategy. The department establishes long-term cooperations with various focus groups and partner institutions. These are particularly valuable for the continuous improvement of our digital infrastructures. At the heart of our participatory work lies a Goal-Directed Design approach, which focuses on understanding user needs and motivations. This approach helps us to maintain clarity in complex and heterogeneous projects and to communicate with non-technical users, enabling them to participate in the discussion of future use scenarios. In such co-creative processes, we have found web-based applications—especially Progressive Web Apps (PWAs)—to be particularly valuable due to their accessibility and flexibility. This paper illustrates our interdisciplinary design-led working process through a case study in school education. Additionally, it explores how the German Maritime Museum as a whole has developed further through increased participation: close transdisciplinary collaboration allows us to embed our practical methodological knowledge across the organization. This consistent commitment to participation is already reshaping the museum, giving it new citizen-centered roles.

Enhancing Urban Sustainability: Developing an Open-Source AI Framework for Smart Cities

To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide.

Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set.

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design process. Although conventional machine learning techniques have shown some success in this field, the scarcity of annotated toxicity data poses a significant challenge in enhancing models' performance. In this study, we explore the potential of leveraging large unlabeled small molecule data sets using semisupervised learning to improve drug cardiotoxicity predictive performance across three cardiac ion channel targets: the voltage-gated potassium channel (hERG), the voltage-gated sodium channel (Nav1.5), and the voltage-gated calcium channel (Cav1.2). We extensively mined the ChEMBL database, comprising approximately 2 million small molecules, and then employed semisupervised learning to construct robust classification models for this purpose. We achieved a performance boost on highly diverse (i.e., structurally dissimilar) test data sets across all three targets. Using our built models, we screened the whole ChEMBL database and a large set of FDA-approved drugs, identifying several compounds with potential cardiac ion channel activity. To ensure broad accessibility and usability for both technical and nontechnical users, we developed a cross-platform graphical user interface that allows users to make predictions and gain insights into the cardiotoxicity of drugs and other small molecules. The software is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred2.

Challenges and Opportunities of Cybersecurity Education for Non-technical Majors

Cybersecurity is fast becoming a crucial area of study due to the advancement of digital technology. As cybersecurity impacts all digital technology users, there is a pressing need for educating users on this issue. Moreover, the risk of falling victim to cyber threats is far more eminent in non-technical background users. Thus, this study focuses on the need for cybersecurity education towards non-technical majors in university, as an effort to address this problem. Through analysis of previous literatures, this paper highlights the possible challenges and opportunities in teaching cybersecurity for non-technical majors. This could provide valuable insights into the digital literacy gap for non-technical majors. The relevance of this study lies in its potential to enhance cybersecurity awareness and promote a safer digital environment.

Improving OpenDevin: Boosting code generation LLM through advanced memory management

OpenDevin, a code generation AI tool, has emerged as a powerful assistant for both technical and non-technical users, offering a practical approach to coding challenges. Unlike traditional code generators that merely output code, OpenDevin excels by executing code directly in a console, allowing for immediate testing and verification. This functionality not only streamlines the coding process but also enhances learning and troubleshooting, making it accessible to a broader audience. In this project, we address several key challenges to improve OpenDevins effectiveness, especially in handling multi-round conversations and contextually relevant code generation. Our team identified and tackled two main challenges faced by OpenDevin: variety of input, and multi-step conversations. Through incorporating a series of functions to parse, summarize, and organize LLM agents memory logs, we significantly improved OpenDevin agents capabilities among a variety of tasks. The integration of efficient memory management led to a notable increase in accuracyfrom 44.4% to 88.9% in multi-round conversations, highlighting the importance of effective memory management in AI-powered coding tools. This report details our methodology, the challenges we faced, and the solutions we implemented, showcasing OpenDevins potential to revolutionize the way users from various backgrounds engage with coding tasks.

NLP-based smart decision making for business and academics

Natural Language Processing (NLP) systems enable machines to understand, interpret, and generate human-like language, bridging the gap between human communication and computer understanding. Natural Language Interface to Databases (NLIDB) and Natural Language Interface to Visualization (NLIV) systems are designed to enable non-technical users to retrieve and visualize data through natural language queries. However, these systems often face challenges in handling complex correlation and analytical questions, limiting their effectiveness for comprehensive data analysis. Additionally, current Business Intelligence (BI) tools also struggle with understanding the context and semantics of complex questions, further hindering their usability for strategic decision-making. Also, when building these models for generating the queries from natural language, the system handles only the semantic parsing issues as each column header is being changed manually to their normal names by all existing models which is time-consuming, tedious, and subjective.Recent studies reflect the need for attention to context, semantics, and especially ambiguities in dealing with natural language questions. To address this problem, the proposed architecture focuses on understanding the context, correlation-based semantic analysis, and removal of ambiguities using a novel approach. An Enhanced Longest Common Subsequence (ELCS) is suggested where existing LCS is modified with a memorization component for mapping the natural language question tokens with ambiguous table column headers. This can speed up the overall process as human intervention is not required to manually change the column headers. The same is evidenced by carrying out thorough experimentation and comparative study in terms of precision, recall, and F1 score. By synthesizing the latest advancements and addressing challenges, this paper has proved how NLP can significantly enhance the accuracy and efficiency of information retrieval and visualization, broadening the inclusivity and usability of NLIDB, NLIV, and BI systems.

Soil footprint: A simple indicator to communicate and quantify soil security

Soil erosion is a major threat to soil security, and therefore to agriculture. To address this challenge, effective strategies are needed to protect soil resources without compromising crop yields. Currently, there are three main players involved: scientists, policymakers, and land managers. However, the technical knowledge required to understand how soil erosion affects food production hinders the communication of the potential benefits of soil sustainability strategies to nontechnical stakeholders, such as farmers and food producers. Moreover, a major player in this fight is still missing, consumers. If there is no connection between soil and society then the soil itself may not be valued and it is less likely to be managed sustainably. In this study, we propose the concept of soil footprint, defined as the ratio of soil loss to crop yield, to communicate, quantify, and compare soil security for different crops. It is a versatile concept that can be applied for a multitude of potential practical uses. To showcase some of these potential uses, we used national data to calculate the soil footprint of the ten main crops in Spain. We demonstrate the utility of this metric to compare and rank crops, identify problematic regions, and assess the effect of land management on the soil footprint. The soil footprint indicator offers advantages in terms of simplicity, facilitating easy calculation, comparison, and interpretation by non-technical users. This can engage society in soil conservation, empower land managers to define sustainable soil management strategies, and promote policies to enhance soil security.

AI-Augmented LangChain: Facilitating Natural Language SQL Queries for Non-Technical Users

This study explores the potential of LangChain, a framework for constructing applications with advanced language models, to translate natural language queries into executable SQL code. Study propose an innovative LangChain-based architecture that receives a natural language query, analyzes it with a language model, and generates the corresponding SQL statement for database querying. This approach aims to empower non-technical users, facilitate inter-team collaboration, and enable data-informed decision-making. However, challenges persist, including managing complex queries and grasping domain-specific terminology. This research investigates the methodology and system design of our proposed natural language interface for databases, leveraging LangChain and extensive language models. Study also explore the possibilities and potential applications of this system, as well as future research avenues for enhancing its functionalities and addressing current constraints. By integrating advanced natural language processing with database technologies, this research aims to enable inclusive and powerful data querying experiences.

MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs. To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text. This research assesses existing work on Text-to-SQL conversion and proposes the MedT5SQL model specifically designed for EMR retrieval. The proposed model utilizes the Text-to-Text Transfer Transformer (T5) model, a Large Language Model (LLM) commonly used in various text-based NLP tasks. The model is fine-tuned on the MIMICSQL dataset, the first Text-to-SQL dataset for the healthcare domain. Performance evaluation involves benchmarking the MedT5SQL model on two optimizers, varying numbers of training epochs, and using two datasets, MIMICSQL and WikiSQL. For MIMICSQL dataset, the model demonstrates considerable effectiveness in generating question-SQL pairs achieving accuracy of 80.63%, 98.937%, and 90% for exact match accuracy matrix, approximate string-matching, and manual evaluation, respectively. When testing the performance of the model on WikiSQL dataset, the model demonstrates efficiency in generating SQL queries, with an accuracy of 44.2% on WikiSQL and 94.26% for approximate string-matching. Results indicate improved performance with increased training epochs. This work highlights the potential of fine-tuned T5 model to convert medical-related questions written in natural language to Structured Query Language (SQL) in healthcare domain, providing a foundation for future research in this area.

Semantic precision: crafting RDF-based digital editions for unveiling the layers of historical correspondence

Abstract This article proposes that Resource Description Framework (RDF) technology is well suited for representing and storing inherently connected epistolary data. We justify this proposition by creating an RDF-based digital edition of the correspondence of members of the Bernoulli dynasty and Leonhard Euler and depicting the benefits this type of edition provides, which are hardly achievable otherwise. We describe the ontologies defined to fully represent letters and illustrate how we formulated letters’ historical, scientific content, rich with mathematical formulae, markups, figures, and references, as RDF statements to facilitate complex queries common in the humanities field. Next, we outline the tools we developed for presenting these editions in an efficient and user-friendly form, hiding the complexity of the data structure from non-technical users while offering sophisticated analysis features to explore atoms of knowledge and their relations. Additionally, since the citability and durability of the studied digital sources play a crucial role for scholars who publish research on the editions, we explain the steps we undertook to ensure the sustainability of the RDF-based editions and to offer persistent citation possibility.

Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology

A vast multitude of tasks in histopathology could potentially benefit from the support of artificial intelligence (AI). Many examples have been shown in the literature and first commercial products with FDA or CE-IVDR clearance are available. However, two key challenges remain: (1) a scarcity of thoroughly annotated images, respectively the laboriousness of this task, and (2) the creation of robust models that can cope with the data heterogeneity in the field (domain generalization). In this work, we investigate how the combination of prototypical few-shot classification models and data augmentation can address both of these challenges. Based on annotated data sets that include multiple centers, multiple scanners, and two tumor entities, we examine the robustness and the adaptability of few-shot classifiers in multiple scenarios. We demonstrate that data from one scanner and one site are sufficient to train robust few-shot classification models by applying domain-specific data augmentation. The models achieved classification performance of around 90% on a multiscanner and multicenter database, which is on par with the accuracy achieved on the primary single-center single-scanner data. Various convolutional neural network (CNN) architectures can be used for feature extraction in the few-shot model. A comparison of nine state-of-the-art architectures yielded that EfficientNet B0 provides the best trade-off between accuracy and inference time. The classification of prototypical few-shot models directly relies on class prototypes derived from example images of each class. Therefore, we investigated the influence of prototypes originating from images from different scanners and evaluated their performance also on the multiscanner database. Again, our few-shot model showed a stable performance with an average absolute deviation in accuracy compared to the primary prototypes of 1.8% points. Finally, we examined the adaptability to a new tumor entity: classification of tissue sections containing urothelial carcinoma into normal, tumor, and necrotic regions. Only three annotations per subclass (e.g., muscle and adipose tissue are subclasses of normal tissue) were provided to adapt the few-shot model, which obtained an overall accuracy of 93.6%. These results demonstrate that prototypical few-shot classification is an ideal technology for realizing an interactive AI authoring system as it only requires few annotations and can be adapted to new tasks without involving retraining of the underlying feature extraction CNN, which would in turn require a selection of hyper-parameters based on data science expert knowledge. Similarly, it can be regarded as a guided annotation system. To this end, we realized a workflow and user interface that targets non-technical users.

Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations

Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most of whom are neither database experts nor professional programmers. Despite the success of spreadsheets, authoring complex formulas remains challenging, as non-technical users need to look up and understand non-trivial formula syntax. To address this pain point, we leverage the observation that there is often an abundance of similar-looking spreadsheets in the same organization, which not only have similar data, but also share similar computation logic encoded as formulas. We develop an Auto-Formula system that can accurately predict formulas that users want to author in a target spreadsheet cell, by learning and adapting formulas that already exist in similar spreadsheets, using contrastive-learning techniques inspired by "similar-face recognition" from compute vision. Extensive evaluations on over 2K test formulas extracted from real enterprise spreadsheets show the effectiveness of Auto-Formula over alternatives. Our benchmark data is available at https://github.com/microsoft/Auto-Formula to facilitate future research.

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

Auto-Tables: Relationalize Tables without Using Examples

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheettables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Tableau forums.

IMMERSIVE ANALYTICS

Immersive Analytics focuses on the benefits and challenges of using immersive environments for data analysis, and developing designs to improve efficiency. Although immersive technologies are widely available, practical solutions have not gained widespread acceptance in real- world applications. Research in this field focuses on abstract 3D visualization, immersive environments, paper sampling and use case evaluation. 03 Related Works • Brooks early review of VR applications found it effective in specific domains like flight simulators, automotive engineering, and astronaut training. • Van Dam et al. highlighted VR applications for SciVis, benefiting archaeology and medical fields. • Laha and Bowman reviewed VR techniques for visualizing volume data, highlighting the need for controlled experiments to explore individual components of immersion. • Reda et al. summarized research for hybrid reality environments like the CAVE2, emphasizing the possibility of collaborative data analysis. • Brath collected evidence that 3D visualizations offer advantages beyond 2D, focusing on immersive displays. 05 Result • Overall, immersive analytics can lead to better data comprehension, better decision-making, more engagement and teamwork, effective big data exploration, creative data visualization methods, non-technical users' empowerment, and applications in a variety of fields. • These results add to the increasing importance of immersive analytics as an efficient tool for decision support and data analysis. Introduction • To facilitate data-driven analytical reasoning, immersive analytics leverages interactive technology like as virtual reality glasses, big flat screen displays, and even the internet of things. • Immersion refers to an experience that creates a genuine sense of presence in a virtual environment. • A person experiences a shift in awareness from their immediate real environment to another reality. • Immersion analytics is still a relatively new field that has mostly been studied in use cases and workshops. • The notion remains extremely difficult, even within the small community of immersive analytics practitioners. Methodology 4.1 Immersive environments • The study focuses on immersive environments leading to a mixed reality experience. • Abstract 3D visualizations must be presented in a mixed or VR environment where hardware and user interact closely. 4.2 Abstract 3D visualizations • Data that lacks a physical representation or intrinsic spatial organization is referred to as abstract data. • In visualization, abstraction is obtained by using colors and shapes that are not directly associated with the object. 4.3 Paper sampling • Paper sampling in immersive analytics refers to the process of selecting and gathering relevant research papers and publications related to immersive analytics. Conclusion and future scope • In conclusion, immersive analytics has the potential to completely transform how we work with data by facilitating deeper understanding, better decision-making, and improved teamwork. • Immersion analytics will develop further and contribute significantly to data analysis and decision support in the future by tackling issues and seizing opportunities in technological innovation, domain-specific applications, ethical issues, and user experience design.

Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data

Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data files and receive automated insights, default visualizations, and the ability to generate custom visualizations through intuitive natural language prompts. The solution streamlines the workflow, eliminating the need for coding expertise while ensuring data privacy and integrity within a secure execution environment. User studies and benchmarking demonstrate increased productivity, time savings, and high user satisfaction. This solution has the potential to democratize data analysis and visualization, empowering decision-makers across various industries with data-driven insights and informed decision-making processes.