Data Quality: The Key to Strategic AI Conversations in the Food & Beverage and Perfumery Sectors
Generative artificial intelligence has revolutionized how companies access and process information. However, the usefulness of an AI-driven conversation largely depends on the quality, timeliness, and structure of the data used. In this article, we explore why optimized data is essential and how Entrii-Copilot makes a difference in the food & beverage and perfumery sectors.
The Challenge of Proprietary Data in Companies
Companies in the food & beverage and perfumery sectors manage vast volumes of internal data, but this data presents several challenges:
- Partial Samples and Obsolete Data: Often, the available data represents only a fraction of the market, and in many cases, it is outdated and does not reflect current realities.
- Complex Excel Structures: Many companies store information in extensive Excel files with heterogeneous data structures, making automated analysis difficult.
- Limitations in Using Generic AI: A standard GenAI model, such as ChatGPT, can only process the data it receives in a conversation or file, without the ability to cross-check or supplement it with updated external sources.
Generic GenAI vs. Entrii-Copilot
To illustrate the difference in data quality, let’s compare two approaches:
- Case 1: A user uploads a spreadsheet with their company’s sales data into a standard GenAI model.
- The AI provides general analyses based solely on the provided data.
- The quality of the insights depends entirely on the structure and timeliness of the uploaded information.
- Case 2: A user queries Entrii-Copilot for price trends in European supermarkets.
- In addition to using generative AI, Entrii-Copilot accesses data that has been periodically collected and updated from physical supermarkets and online stores in over 30 countries.
- The data is structured, filtered, and refined by Entrii Analytics to ensure precision and applicability.
- Integration of Proprietary Company Data: Entrii-Copilot allows businesses to incorporate their own data in a private and secure environment, ensuring information privacy and structuring it so that it can be effectively leveraged by AI.
The Importance of Data Curation and Refinement
Raw data can contain errors, outdated information, or inconsistent structures. Therefore, Entrii Analytics focuses on:
- Processing and Cleaning Data: Removing duplicates, inconsistencies, and errors.
- Standardizing Information: Ensuring usability by AI without requiring manual transformations.
- Continuously Updating Data Sources: To reflect real-time market conditions.
- Collecting Data from Physical Supermarkets and Online Stores: The data is gathered from multiple sources, processed, and structured before being integrated into Entrii-Copilot, preventing the use of unrefined data that could lead to inaccurate insights.
- Integrating Proprietary Business Data: Creating a private environment where each company can combine its internal data with Entrii Analytics’ structured database, maximizing the accuracy and utility of generated analyses.
Applications of Entrii-Copilot in the Food & Beverage and Perfumery Sectors
Companies using Entrii-Copilot gain strategic insights for:
- Predicting Price Trends in supermarkets across different countries.
- Analyzing Competitors with data from multiple markets.
- Optimizing Export Strategies based on real and updated information.
- Combining Proprietary Data with Market Information for more customized and accurate analyses.
Formats for Accessing High-Quality Data
Entrii Analytics provides structured and refined data in multiple formats, adapting to each company’s needs:
- AI-Generated Reports, customized based on client demands.
- Entrii-Copilot, enabling real-time interactions with high-quality data, providing strategic insights tailored to business needs.
- Private Data Environments for Companies, allowing businesses to integrate and structure their own information to optimize AI-driven decision-making.
The difference between using a GenAI model with proprietary data and a specialized tool like Entrii-Copilot lies in the quality, structure, and timeliness of the data. Companies that rely solely on their internal records often face incomplete or difficult-to-process information. In contrast, Entrii-Copilot enables informed strategic decision-making by providing refined, industry-specific data while also offering the ability to work with private company data in a secure environment. The combination of generative AI with specialized and personalized databases is the true differentiator in transforming business intelligence.