Data Science using AI on ERP Data: Unlocking Business Potential

Introduction

In today's data-rich environment, businesses are constantly seeking ways to leverage their information for better decision-making.

This blog post explores how Data Science and AI, particularly with a solution like Terno AI, can transform Enterprise Resource Planning (ERP) data into actionable insights, addressing common challenges and showcasing real-world use cases.

The ERP Data Deluge and Decision-Making Bottlenecks

ERP systems are the backbone of modern enterprises, integrating core business functions like finance, HR, sales, and supply chain. They centralize data and workflows, aiming to improve efficiency and collaboration. However, despite the vast amounts of data collected, many organizations still face slow decision-making due to several factors:

• Difficulty in extracting insights: A wealth of data doesn't automatically translate to clear insights.
• Reliance on IT/technical teams: Generating reports often requires specialized technical skills, creating bottlenecks.
• Fragmented reporting: Data and reports are often siloed across different departments, hindering a holistic view.
• Time-consuming custom reports: Ad-hoc analysis and custom report generation can take significant time.
• Balancing security and flexibility: Ensuring data security while providing flexible access is a constant challenge.
• Understanding data meaning: Deciphering the underlying meaning and implications of data can be complex.

Introducing Terno AI: Your AI Data Scientist

Terno AI is an, 'AI Data Scientist', designed to overcome these challenges. It empowers both technical and non-technical teams to instantly analyze enterprise data and gain actionable insights by simply asking questions in plain English.

Key features of Terno AI include:

• Works on Your Data Locally: Simply connect your Data and get instant 100% accurate complex analytics reports.
• Secure connectivity: Connects to various data sources like ERPs, databases (PostgreSQL, MySQL, BigQuery, Snowflake), data warehouses, and even allows for the insertion of files in all formats (images, CSV, JSON, PDF, html, videos, etc).
• Enterprise-grade security: Features like SQLShield, read-only access, schema-only analysis, encrypted connections, virtual environment execution, and SOC 2-compliant infrastructure ensure data privacy and security.
• Full analytics workflow: Performs exploratory data analysis, advanced machine learning modeling, real-time processing, and automated report generation.
• Appservices: Like the reports your team generates? Instead of generating the reports multiple times, you can create Apps and share it with your team.
• Customizable: You can customize Terno according to your organizational needs.

How Terno AI Works

Terno AI is an advanced intelligent analytics platform that performs deep data analysis using metadata rather than raw data, ensuring both speed and security.

When connected to your organization’s database, Terno scans the metadata layer, the structure that defines your tables, relationships, and data types, to understand how your information is organized. It then uses this understanding to generate precise SQL queries and perform complex analytics tasks, from descriptive summaries to predictive insights, all without directly exposing your sensitive data.

By interpreting metadata, Terno builds a semantic understanding of your business data, identifying entities, hierarchies, and metrics, and then uses advanced language models and reasoning engines to answer questions, detect trends, and visualize patterns in real time. This metadata-driven approach allows Terno to deliver accurate, context-aware analytics while maintaining data privacy and governance standards, empowering teams to make data-driven decisions effortlessly.

Use Cases of Terno AI

Terno AI offers a wide range of applications across various business functions:

1. Consumer Insights & Marketing

• Scenario: A wellness company launches a new natural sweetener.
• Terno AI's Role: Segments global consumers into key groups (e.g., health-conscious Millennials, households with diabetic members, trend followers) and suggests personalized marketing strategies by region (e.g., Instagram/TikTok campaigns in North America/Europe, WeChat/WhatsApp in Asia, retail loyalty programs in Latin America).

2. Sales & Distribution Optimization

1. Scenario: A global healthy food brand experiences seasonal surges in protein drink mix sales in Europe.
2. Terno AI's Role: Detects this pattern early, forecasts demand, and helps adjust the distribution network (e.g., routing more shipping containers to Europe, scaling down warehouses in Southeast Asia).
3. Outcome: 8% reduction in storage costs and better product availability in high-demand regions.

3. Supply Chain & Operations

• Scenario: A multinational chocolate producer uses IoT sensors to track ingredient usage.
• Terno AI's Role: Highlights that one plant consumes 15% more cocoa per batch than the global average, leading to the discovery of a calibration issue.
• Outcome: Annual cost savings of $2 million and more efficient production.

4. Product Development & Innovation

• Scenario: A wellness product company wants to launch innovative products.
• Terno AI's Role: Conducts global trend analysis, identifying rising interest in gut health and probiotics. It then analyzes customer testing results for new prototypes, showing acceptance rates in different regions.

5. Consumer Health & Wellness Analytics

• Scenario: An FMCG company integrates with fitness apps
• Terno AI's Role: Analyzes user data (e.g., sugar intake, steps) and sends personalized product suggestions (e.g., "Try our zero-sugar chocolate bar" for high sugar intake, "Protein Recovery Shakes" for runners). This shifts the company's image to a proactive wellness partner.

6. Financial & Strategic Analytics

• Scenario: A global skincare brand wants to optimize pricing.
• Terno AI's Role: Builds price elasticity models, revealing that a price cut drives significant sales increases in Latin America but not in Western Europe.
• Outcome: Adoption of a region-specific pricing strategy (competitive in emerging markets, premium in developed regions).

7. Quality & Compliance

• Scenario: An international company deploys AI-powered vision systems on packaging lines.
• Terno AI's Role: Automatically detects defects like cracked bottles or mislabeling in real time.
• Outcome: 25% fewer customer complaints, improved production speed, and consistent product quality.

Case Studies: Terno AI in Action

Case Study 1: Marketing Analysis

• Challenge: Identifying which marketing campaigns truly drive conversions and revenue amidst heavy investment across multiple channels.
• Solution: Terno AI unifies lead and sales data from ERP (e.g., Odoo's crm_lead, sale_order, utm_campaign tables), computes ROI metrics (leads, conversions, revenue), visualizes insights, identifies top/bottom performers, and recommends optimizing marketing spend.

Case Study 2: Customer Segmentation

• Challenge: Businesses treating all customers the same, leading to inefficient marketing.
• Solution: Terno AI extracts and prepares ERP sales and customer data (order frequency, spend, recency), engineers features for segmentation, applies machine learning (K-Means clustering), visualizes segments, and generates targeted actions for different customer groups (e.g., Champions, Loyal Regulars, At-Risk, New Low-Value).

Case Study 3: Customer Churn

• Challenge: Losing revenue when loyal customers become inactive without realizing it until it's too late.
• Solution: Terno AI extracts customer and order data from ERP, computes RFM (Recency, Frequency, Monetary) metrics, identifies at-risk customers based on lowest Recency scores, visualizes insights, and recommends retention actions (personalized emails, win-back discounts).

Case Study 4: Sales Forecasting

• Challenge: Struggling to anticipate sales trends for upcoming quarters, impacting inventory and marketing planning.
• Solution: Terno AI extracts monthly sales data from ERP, preprocesses it for forecasting, fits a Holt-Winters additive trend model on historical data (e.g., Q1) to predict future sales (e.g., Q2), compares forecasts with actuals, and visualizes the results to support data-driven decision-making.

Conclusion

Data Science and AI, as exemplified by Terno AI, offer powerful solutions for businesses to unlock the full potential of their ERP data. By transforming raw data into actionable insights, organizations can make faster, more informed decisions, optimize operations, enhance customer engagement, and drive measurable growth.

Useful Links

• Terno AI: https://terno.ai
• PPT link: PPT