5 days ago
Clean Centralised Data Seen as Key to E-Commerce AI Success
E-commerce companies are using AI to help sell products and manage their businesses.
But AI needs accurate and well-organised information to work properly.
Product, inventory and customer information is often stored in separate systems.
This can stop AI from seeing the full picture.
If the information is wrong, AI may repeat and expand those mistakes.
For example, it could advertise an item that is already out of stock.
Nisum recommends bringing data together and assigning responsibility for keeping it accurate.
The report says companies that prepare their data can have a better chance of turning AI experiments into useful business tools.
Nisum says e-commerce companies need clean, structured and centralised data before expanding artificial intelligence projects.
Scattered product, inventory and customer information can prevent AI systems from producing reliable business insights.
Poor-quality data may scale problems such as inaccurate inventory counts and duplicate customer records.
Data weaknesses pose risks for personalisation, dynamic pricing, demand forecasting and inventory management.
Nisum CEO Anurag Chauhan said only 5.5 per cent of organisations using AI currently generate tangible financial returns.
- Who
- E-commerce companies and retailers, with recommendations from technology firm Nisum and its CEO Anurag Chauhan.
- What
- A Nisum report warns that AI e-commerce projects may fail to deliver measurable returns without clean, structured and centralised data.
- Where
- The report was datelined New Delhi.
- When
- The report's publication date is not specified.
- Why
- Retail data is often fragmented or inaccurate, which can make AI forecasts, recommendations and automated decisions unreliable.
Key facts
- Report author
- Global technology firm Nisum
- Critical requirement
- Clean, structured and centralised data
- Data sources cited
- Product information, inventory records and customer data
- AI applications affected
- Personalisation, dynamic pricing, demand forecasting and inventory management
- Recommended measures
- Identify fragmented data sources, assign data-quality ownership and establish governance frameworks
- Reported financial-return figure
- Only 5.5 per cent of organisations using AI currently generate tangible financial returns, according to Anurag Chauhan
Sources
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