In the business, it is frequently noticed that many firms building an AI/ML pipeline approach the task of data labeling haphazardly and underestimate its complexity. After facing the necessity to provide usable data, enterprise after enterprise fails.
They don’t lack raw data; in fact, businesses are overflowing with it as more and more of it comes online every day. At any given time, organizations are gathering enormous volumes of images from cameras, sensors, and other equipment. The difficulty is determining how to analyze and make this data usable.
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The challenge of workforce management
Because of the enormous amount of unstructured data processed and the need to maintain high-quality standards across such a huge workforce, successful data labeling is a workforce challenge.
Although data labeling involves a lot of volumes, both quantity and quality are equally important. Businesses must strike a delicate balance between rapidly growing their staff and training and managing such a sizable and diverse group. Data labeling and other data processing requirements are initially managed internally by successful startup teams and even businesses. This is effective, but only while datasets are still manageable.
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Managing consistent dataset quality
High dataset quality is obviously the foundation for good data, but this presents unique difficulties. Companies must devise strategies to guarantee that labelers have the tools necessary to provide datasets of consistent quality. No matter how effective your dataset quality checking mechanism is, human error cannot be totally eliminated. This forces data operations teams to establish a closed-loop feedback mechanism that checks for faults in order to find strategies to address both subjective and objective quality issues.
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Keeping track of financial cost
Companies have struggled on numerous occasions to allocate the proper amount of money for data labeling because there are no set prices or indicators. 26% of businesses who asked why their AI projects were failing gave the excuse of a lack of funding. Companies have limited ability to track results in relation to work time if metrics, responsible monitoring, and objective criteria are not in place. It has observed that there is a lack of transparency regarding the precise services that businesses are paying for when contracting out or performing in-house data labeling operations.
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Complying with data privacy requirements
The GDPR, DPA, and CCPA are just the beginning. At the same time that businesses are gathering more data than ever, data confidentiality compliance rules are expanding globally. Unstructured data that needs to labeled contains personal information like faces, license plates, and any other identifying information that might included in photos. Businesses required to adhere to standards including handling data lawfully, fairly, and transparently in regard to the topic. This makes it difficult for businesses that handle sensitive data or must adhere to regulations to outsource work to third-party data labeling service providers.
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Maintaining smart tooling at scale
A mix of skilled laborers and clever equipment, such as AI-assisted data annotation, automation, data management, and data pipelines, required to produce high-quality data. We observe that tooling requirements keep increasing as AI penetrates more domains and required to comprehend more human jobs.
According to our experience, firms who start out using tools created in-house frequently find that their annotating requirements keep expanding and that they must put in more effort than anticipated to keep up.
Conclusion
Data labeling may be quite difficult and complex, especially when done at a big scale, which is necessary nowadays for many use cases. Without resolving these issues, the data may be of low quality or may add extra complexity layers and cost money. It is frequently ideal to outsource this process to companies you can rely on, as well as those who deliver quality and speed while properly addressing all of these problems.
