Keeping up with the ever-evolving, and increasingly complicated vernacular produced by the digital age is extremely confusing. Why is LOL ‘lots of love’ and ‘laugh out loud’? How are we to know? However, absconding from the idea of using these slang words is also not an option in today’s world. If only there were something that could make the origins and meanings clearer! Something that could help us understand these words so that it would become easier for us to use them too…
In the world of data, this complex territory of slang words is replaced with big data, which can seem like a perplexing mass of unstructured, structured, and semi-organized data if not understood better. Examining such data and adding context to it is therefore essential. However, doing this manually is next to impossible, especially considering that all this data is also available in a myriad of formats, from audios and videos, to pdfs and images. Data labelling as a tool, therefore, can be transformational, especially in how data is processed today.
Annotation of data to provide the necessary context and meaning to such data can be achieved through trained ML models. These models can extract valuable data points to carry out this mammoth task, while also ensuring that all data, regardless of its format, is processed efficiently.
Once the data is properly labeled, it opens up use cases such as:
dataX.ai helps its clients achieve this even at large-scale data sets, and evolving project requirements, while also saving time, and ensuring accuracy. The latter is accomplished through improved model performance, due to high-quality annotations. Moreover, outsourcing processes such as data labelling is much more cost-effective.