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Companies invest in something if they think it generates value, and AI development is no different in this regard. Myriad sources tell us that AI adoption is becoming more ubiquitous, which would seem to indicate that the people running these companies understand the business value and ROI. This new report, co-created by Emerj and Clickworker, will examine two critical elements of good data – quality and diversity – via two of Clickworker’s typical use cases: facial recognition and voice recognition. We will also expound upon what constitutes good data quality, along with the challenges of acquiring said data. We’ll also look at what steps one leading data enrichment firm, Clickworker, did to overcome these challenges and add value for its client. In this report, you’ll gain insight on: 1. Business Challenges What are the challenges in gathering good source data and training models, how to collect a dataset capable of training NLP models to detect vocal nuance, and how to overcome those challenges 2. Turning Data into Value There is a large need for data that needs to be accurate and diverse in order to make development successful. 3. Results What were the results of applying data quality and diversity in two enterprise use-cases. |
