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Data Labelling Challenges

December 20, 2021
in News & Updates
Data Labelling Challenges

-The majority of organizations struggling with AI and ML projects say that their biggest problems concern data quality, data labeling, and building model confidence. And the 5 primary factors that lie at the foundation of these problems include:

– Workforce management: successful data labeling is a workforce challenge for two reasons – the need to manage enough workers to process the massive volume of unstructured data, and the need to ensure high quality across such a large workforce.

– Dataset quality: there are two main types of dataset quality — subjective and objective — and they can both give rise to data quality issues.

– Financial obstacles: when asked why their AI projects are failing, 26% of enterprises blamed a lack of budget. Without metrics, responsible monitoring, and objective standards for data labeling success, companies are limited in their ability to track results in relation to time spent on work.

– Data privacy: Enterprises are obligated to comply with principles to ensure their data privacy. It is, therefore, challenging for organizations dealing with sensitive data, or that must comply to regulations, to outsource tasks to third party data labeling providers.

– Smart tooling: whether building in-house tools or buying an outsource platform service, there’s always matters to concern. Building an internal tool means risking paying over the odds in terms of time, cost of going to market, and continual maintenance. When it comes to buying, you need to consider whether the tools you select provide all the services that you’re seeking. That’s why it’s critical you find a platform that is robust enough to evolve with your projects, but also mature enough to ensure stability.

Source: Dataloop

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