Unlocking insights at the speed of thought with generative AI
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Business leaders need to make data-driven decisions quickly. From real-time fraud detection in finance to personalized recommendations in eCommerce, the need for real-time data analytics and faster insights is paramount. However, the lack of readily available and easily understandable insights is a problem that hinders agility and decision-making.
Traditional data analysis methods require knowledge of programming languages or statistical applications, limiting it to individuals with technical skills. This results in delays as business users submit requests and wait their turn while data piles up by the second.
A Mckinsey report states that empowering users in the commercial organization with data, analytics & technology can increase EBITDA from 15-25%.¹
Here’s a scenario
Consider a multinational retailer seeking to optimize their marketing budget across various regions and channels globally. The Marketing Manager needs to identify the most effective channels for a specific campaign across geographies. By the time an analyst delivers a report, the campaign window may have closed.
AI/ML capabilities are already able to locate and extract data from unstructured documents with nearly 95% accuracy². Generative AI can provide instant self-service analytics access to business users like the Marketing Manager. They can leverage Generative AI for use cases like creating targeted marketing content and personalizing customer journeys, all without relying on specialized data querying expertise.
Generative AI-powered real-time data analytics
Business decision-makers with limited technical know-how can get autonomous access to data insights by democratizing data analytics with generative AI. This empowers faster, more agile decisions free from the limitations of manual analysis.
To make this vision of democratized analytics a reality, our team developed a generative AI-based data analysis solution designed to empower non-technical business users with on-demand insights. Simply describe what you want to know and the application will generate customized visualizations from your underlying datasets. For example:
Automating data analysis for faster insights
Dataset input
The application accepts datasets in CSV or Excel formats and allows users to upload large-scale data files with ease. It seamlessly handles diverse data types at scale, including sales figures, customer demographics, inventory levels, production metrics, and more, empowering comprehensive analysis across various domains.
Prompting
Provide a prompt or ask a specific question related to the uploaded dataset to initiate data analysis. These prompts can range from macro-level business queries to granular data exploration requests, enabling users to extract meaningful insights tailored to their needs.
Generative AI-powered code generation
Generative AI creates a code that replicates analytical processes typically performed by data analysts manually. This code is tailored to the specific dataset and the insights desired by the user. By automating the code generation process, data analysis workflows become significantly faster and the need for users to possess coding expertise is eliminated.
Data analysis
The data analysis process is prompt-driven and automated by running the code generated by generative AI. It leverages advanced algorithms to systematically examine the data and identify correlations, trends, and anomalies. This automated, real-time data analytics phase ensures that users receive comprehensive insights without the need for manual data and analytics processing or manipulation.
Insights delivery
Users gain near real-time insights by simply submitting their prompts or questions. The system swiftly executes the AI-generated code to analyze the datasets and produce visually rich outputs, including graphs, charts, and visualizations that illustrate key findings and patterns. This rapid delivery of insights empowers users to make informed decisions.
The entire process is streamlined to ensure user-friendliness and efficiency. Users are required to provide only one prompt or question related to the insights they seek from the data. Whether it’s identifying sales trends, forecasting demand, or detecting anomalies, users can articulate their queries accordingly.
Conclusion
Slow, complex, and often inaccessible to non-technical users, traditional data analysis methods create bottlenecks, impacting agility and hindering access to valuable insights. Generative AI offers a transformative approach. Through the automation of code generation and streamlining of the entire analysis process, a growing number of users are becoming data-driven decision-makers. This is a fundamental shift in how we approach business intelligence. Democratizing data fosters a culture of data-driven decision-making, empowering business teams to access, analyze, and leverage insights for innovation and efficiency.
Sources:
1. https://www.mckinsey.com/growth-marketing-and-sales/creating-data-driven-commercial-growth
2. https://www.forbes.com/sites/forbestechcouncil/2023/01/11/five-data-analytics-trends-on-tap-for-2023
About the Authors
1. Ritesh Kumar is the Senior Lead Data Scientist at Sigmoid with over 8 years of experience across retail, banking, pharma, and BFSI domains. An expert in Python, R, PySpark, Hadoop, and machine learning techniques like predictive analytics and deep neural networks, he has consistently demonstrated strong analytical abilities to develop data-driven solutions and strategies. Ritesh leverages his cross-industry knowledge to guide enterprises in extracting meaningful data insights for informed decision-making.
2. Rahul Kushwaha is an Associate Data Scientist at Sigmoid with over 2 years of experience across retail, banking, and beverage industries. An engineering graduate from NIT Jamshedpur, he leverages predictive modeling, machine learning, and data visualization to uncover insights and drive improvements through data analytics. At Sigmoid, Rahul applies his skills to enable data-driven services and solutions across Retail, CPG, Manufacturing, and BFSI verticals.
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