Data enrichment is a general term that refers to processes used to enhance, refine or otherwise improve raw data. The harsh reality of AI is that lot of time is spent preparing data before it can be useful. Data extraction, annotation, cleansing and enrichment is difficult, time consuming and repetitive. Data curation and enrichment is required through the entire journey of AI model maturity. A typical cycle of Data Curation and Enrichment in AI and Machine Learning is as below. Human in the loop and Human Intelligence play a vital role in the journey to verify, validate and fix issues in model outcome so that further efficiency and improvisation can be achieved.
Various aspect or types of Data Enrichment workflows are as below:
Algorithms or Models learn from data. It find relationships, develop understanding, augment intelligence, make decisions, make predictions and measure its confidence and efficiency from the training data its given. And the better the training data is, the better the model performs.
Therefore, Data curation and enrichment services are very vital catalyst to develop useful and efficient AI and Machine Learning Models through supervised learning. Applicability is across wide range of verticals Retail, Automotive, Healthcare, BFSI, Manufacturing, Enterprise, Governance are to name a few. Irrespective of the use case the AI Model is trying to (re)solve or improve, availability of quality ground truth dataset is very crucial and important and hence the importance of Data Curation and Data Enrichment work in Artificial Intelligence.