AI in Retail

Here’s Why You Need Vue.ai’s Data Organization Tool3 min read

March 22, 2022 3 min read

Here’s Why You Need Vue.ai’s Data Organization Tool3 min read

Reading Time: 3 minutes

In 2022, companies are expected to have an average of 35 AI projects in place. While most eCommerce businesses turn to AI to secure a competitive edge over the others, the challenges that stand in the way of them realizing the full potential of their investments are embedded deep within multiple layers. At the root of which lies the ugly data problem.

Most retailers today aggregate data from multiple sources and channels. According to a recent survey, companies on average source data from over 400 different sources. What’s more, over 20% of organizations draw data from a whopping 1000+ sources. Data coming in from each one of these sources is often siloed and structured in a manner that is oftentimes inconsistent and incompatible with the retailer’s data organization structure. In fact, over 90% of the world’s data is unstructured and inaccessible for business operations.

In order to combat this, teams spend a substantial amount of time manually verifying, sorting, cleaning, and organizing the data.

Considering the sheer scale of volumes, this process is resource extensive and typically results in three critical issues:

  • Inefficient processes
  • Errors and inaccuracies in results
  • Substantial time and effort required for quality checks

Off-the-shelf AI and automation tools that are usually introduced to improve the efficiency of the process bring in a new host of problems — ML models are only as good as the data they are trained on. What this means is that teams require in-house ML engineers to work alongside domain experts to ensure the quality of the data going into the systems.

Furthermore, in order to ensure optimal knowledge sharing, a common language then needs to be engineered, for every domain introduced so that both parties can understand the process. 

Introducing Vue.ai’s Data Organization Tool

In order to help businesses effectively organize and manage their data, we built a fully customizable, no-code, AI-powered data organization tool.

With Vue.ai’s catalog data organization tool, business teams can orchestrate the entire journey from consolidating and transforming raw data to deploying custom AI models all on a single interface. 

Teams can upload their data catalogs and label only ~10% of their total data points on an interactive, intuitive UI. During which the system observes, learns from user interactions, predicts labels for the remaining data points. Thus, organizing the data into a structure that is compatible with your internal systems.

As a result, raw data in the form of images, numerical, structured text attributes, and unstructured text are enriched with rich attributes that are relevant to your industry and use case. Then, the data is organized into a hierarchy that brings structure to your data management system so that your teams can use it to build AI models that solve mission-critical problems. 

Vue.ai’s data organization tool abstracts the underlying complexity of your data so that anyone can build ML models. Whether you’re a data labeler tagging training data, or an ML engineer tuning model hyperparameters. The easy-to-use interface allows teams without any knowledge of code to take part in shaping the models most relevant to their business and use case.

With this tool, organizations can drastically reduce the amount of time and the resources needed to manage their data and focus on solving challenges that are critical to their KPIs. 

Teams can deploy their AI models in hours rather than months and realize the value of their AI implementation in weeks!

Here’s the impact organizations have seen after integrating this ML tool into their processes:

data organization

We’re eager to put this self-learning chisel in your hands to help you manage, shape, and derive unlimited value from your data — to answer insightful questions specific to your use-case, or act as a launchpad to build personalization and sense-making applications on. 


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