AI in Retail

Solving The $3.1 Trillion Data Organization Problem With AI4 min read

July 5, 2023   |   3 min read

Solving The $3.1 Trillion Data Organization Problem With AI4 min read

Reading Time: 3 minutes

Every business is grappling with the bad data problem. Today, enterprise data is incomplete, inaccurate, and inadequate. Records are inconsistent and unstructured, and oftentimes non-conforming. As a result, data science teams spend over 80% of their time preparing this data rather than using it. This makes data organization a top priority for data-driven businesses.

With AI, organizations can not only manage enterprise data but transform it into a strategic asset as well. Here’s how: 

1. Build Rich & Enhanced Datasets

Solving The $3.1 Trillion Data Organization Problem With AI

AI can help businesses extract, enrich, enhance, and organize their data. With any input of unstructured data (text, images, video, or any other format),’s platform can extract detailed attribute tags and build a structured dataset from it. The data generated by AI takes in inputs from all available sources, is nuanced, and mapped to the hierarchy and data organization structures that the users define. Businesses can automate the creation of rich datasets that match their business goals and use cases with minimal effort.

With AI-powered product tagging, retail teams have seen 97% accuracy in their catalog data.

2. Define A Data Organization Structure Or Taxonomy Customized To Your Business

Today, businesses look to differentiate themselves while catering to the end user’s needs. They might also have different ways of using their data. It’s safe to say that every business wants a customized structure that they can leverage. The platform puts the control in the hands of business teams and enables them to build custom taxonomy and deploy the models built for inference. This can be applied to any new content or data inventory coming in and map data at scale.

Business teams have witnessed up to an 80% decrease in the time taken to generate tags by scaling custom AI models.

3. Moderate Content Across Workflows

Most businesses today have fixed guidelines around how content, text, image, audio, or video, should appear on their platforms. As a result, they invest significant amounts of time and resources to ensure that the content onboarded onto their platforms adheres to internal guidelines. With AI, businesses can moderate content at scale. This means, theycan ensure that the submitted content meets preset criteria, does not contain any inappropriate content, violate existing copyright rules, etc. Thus, improving the accuracy of the content onboarded while considerably reducing the cost associated with manual moderation.

With AI-powered image moderation, businesses have seen a 70% reduction in the number of tickets raised by vendors about wrongly rejected images.

4. Enable Faster User Generated Content Onboarding

With AI, businesses can automate onboarding workflows that involve the qualification of text and visual content. This allows them to deliver faster and more effective user experiences. harnesses a variety of AI-enabled APIs to detect attributes that can be used to qualify UGC and other forms of incoming media  — based on their quality and content, in real-time. This includes checking for the presence of NSFW content, blurred images, and other internal guidelines that need to be met for quality control. As well as providing immediate feedback for individual criteria. This guarantees a friction-free experience for the end-user. 

With AI-powered content moderation, businesses have been able to double the speed at which they go to market.

5. Make Content Discovery More Effective

With AI-enriched metadata and standardized taxonomy across the board, businesses can deliver highly relevant navigation experiences across their website or app. They can also improve content discovery for users. Similarly, even for internal teams, structured data allows for easy retrieval and usage of data. uses computer vision and NLP to extract detailed attribute tags and build robust data catalogs. This enables platforms to surface the most suitable result through search and filtering.

Businesses have seen a 51% uplift in accuracy for search and discovery with AI-generated data.

With AI, enterprises can enhance their data, improve process efficiencies and turn it into a strategic asset. Thus, driving competitive value in the long term.

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ABOUT THE AUTHOR engineers bespoke AI transformation roadmaps for enterprises across industries. Retailers to resellers, auto-extracting data from files to extrapolating fashion styles, 150+ conglomerates in five continents recruit How can we help yours?